def findsubimage(imglist):
# print "LOGX:: Entering `findsubimage` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import readhdr, readkey3
from numpy import abs, argmin
import string
import os
import sys
import re
imgsub = []
for img in imglist:
imglist2 = imglist[:]
imglist2.remove(img)
hdr = readhdr(img)
JD0 = readkey3(hdr, 'JD')
xcum0 = readkey3(hdr, 'xcum')
distance = []
imglist3 = []
for img2 in imglist2:
if abs(readkey3(readhdr(img2), 'xcum') - xcum0) > 10:
distance.append(abs(JD0 - readkey3(readhdr(img2), 'JD')))
imglist3.append(img2)
if len(distance) > 0:
imgsub0 = imglist3[argmin(distance)]
else:
imgsub0 = ''
imgsub.append(imgsub0)
return imgsub
def clean_image(img, cleanimg):
# print "LOGX:: Entering `clean_image` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import readkey3, readhdr, delete
array, header = ntt.cosmics.fromfits(img, verbose=False)
import warnings
def fxn():
# print "LOGX:: Entering `fxn` method/function in %(__file__)s" %
# globals()
warnings.warn(" ", DeprecationWarning)
original_filters = warnings.filters[:]
# Ignore warnings.
warnings.simplefilter("ignore")
try:
c = ntt.cosmics.cosmicsimage(array, gain=readkey3(header, 'gain'), readnoise=readkey3(
header, 'ron'), sigclip=5.0, sigfrac=0.3, objlim=5.0, verbose=False)
c.run(maxiter=4, verbose=False)
fxn()
finally:
warnings.filters = original_filters
if not cleanimg:
delete(img)
cleanimg = img
ntt.cosmics.tofits(cleanimg, c.cleanarray, header, verbose=False)
return cleanimg
def searchbias(img, listbias):
# print "LOGX:: Entering `searchbias` method/function in %(__file__)s" %
# globals()
from ntt.util import readhdr, readkey3
import ntt
import glob
import re
from numpy import argmin
from numpy import abs
hdr = readhdr(img)
JD = readkey3(hdr, 'JD')
_instrume = readkey3(hdr, 'instrume')
if not listbias:
directory = ntt.__path__[0] + '/archive/' + str(_instrume) + '/bias'
listbias = glob.glob(directory + '/*fits')
else:
directory = ''
if listbias:
biasfile = ''
distance = []
for bias in listbias:
JDbias = readkey3(readhdr(bias), 'JD')
distance.append(abs(JD - JDbias))
if len(distance) >= 1:
biasfile = listbias[argmin(distance)]
else:
biasfile = ''
else:
biasfile = ''
return biasfile, directory
def skysofifrom2d(fitsfile, skyfile):
# print "LOGX:: Entering `skysofifrom2d` method/function in %(__file__)s"
# % globals()
import ntt
from ntt.util import readhdr, readkey3, delete
from numpy import mean, arange, compress
try:
from astropy.io import fits as pyfits
except:
import pyfits
from numpy import interp as ninterp
hdr = readhdr(fitsfile)
_grism = readkey3(hdr, 'grism')
if _grism == 'GR':
_order1 = 10
else:
_order1 = 6
yy1 = pyfits.open(fitsfile)[0].data[:, :].mean(1)
crval2 = readkey3(hdr, 'CRVAL2')
cd2 = readkey3(hdr, 'CD2_2')
xx1 = arange(len(yy1))
aa1 = crval2 + (xx1) * cd2
yy1cut = compress((aa1 < 18400) | (aa1 > 18650), yy1)
aa1cut = compress((aa1 < 18400) | (aa1 > 18650), aa1)
yy1cut1 = compress((aa1cut < 11600) | (aa1cut > 11800), yy1cut)
aa1cut1 = compress((aa1cut < 11600) | (aa1cut > 11800), aa1cut)
yy1interp = ninterp(aa1, aa1cut1, yy1cut1)
delete('_new3.fits')
hdu = pyfits.PrimaryHDU(yy1interp)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('_new3.fits')
hdulist.close()
ntt.util.updateheader('_new3.fits',0,{'CRVAL1':[crval2,''],'CD1_1':[cd2,'']})
# hdulist[0].header.update('CRVAL1', crval2)
# hdulist[0].header.update('CD1_1', cd2)
fitsfile = ntt.efoscspec2Ddef.continumsub('_new3.fits', _order1, 1)
yy1 = pyfits.open(fitsfile)[0].data
crval2 = pyfits.open(fitsfile)[0].header.get('CRVAL1')
cd2 = pyfits.open(fitsfile)[0].header.get('CD1_1')
xx1 = arange(len(yy1))
aa1 = crval2 + (xx1) * cd2
skyff = pyfits.open(skyfile)[0].data
crval1 = pyfits.open(skyfile)[0].header.get('CRVAL1')
cd1 = pyfits.open(skyfile)[0].header.get('CD1_1')
skyxx = arange(len(skyff))
skyaa = crval1 + (skyxx) * cd1
shift = ntt.efoscspec2Ddef.checkwavelength_arc(
aa1, yy1, skyaa, skyff, '', '')
delete('_new3.fits')
return shift
def searchfringe(img, listfringe):
# print "LOGX:: Entering `searchfringe` method/function in %(__file__)s" %
# globals()
from ntt.util import readhdr, readkey3
import ntt
import glob
import re
from numpy import argmin
from numpy import abs
hdr = readhdr(img)
JD = readkey3(hdr, 'JD')
_filter = readkey3(hdr, 'filter')
_instrume = readkey3(hdr, 'instrume')
if not listfringe:
directory = ntt.__path__[0] + '/archive/' + \
str(_instrume) + '/fringing/' + _filter
listfringe = glob.glob(directory + '/*fits')
else:
directory = ''
if listfringe:
fringefile = ''
distance = []
for fringe in listfringe:
hdrf = readhdr(fringe)
JDfringe = readkey3(hdrf, 'JD')
filterfringe = readkey3(hdrf, 'filter')
if filterfringe == _filter:
distance.append(abs(JD - JDfringe))
if len(distance) >= 1:
fringefile = listfringe[argmin(distance)]
else:
fringefile = ''
else:
fringefile = ''
return fringefile, directory
def sofispecreduction(files, _interactive, _doflat, listflat, _docross, _verbose=False):
# print "LOGX:: Entering `sofispecreduction` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import delete, readhdr, readkey3, correctcard, rangedata
import string, re, sys, os, glob
try:
from astropy.io import fits as pyfits
except:
import pyfits
from pyraf import iraf
from numpy import argmin, array, min, isnan, arange, mean, sum
from numpy import sqrt, pi
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['ccdred.flatcombine', 'ccdproc', 'specred.apall', 'longslit.identify', 'longslit.reidentify',
'longslit.fitcoords', 'specred.transform', 'specred.response', 'imutil.hedit']
for t in toforget:
iraf.unlearn(t)
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.dispaxi = 2
iraf.specred.mode = 'h'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.overscan = 'no'
iraf.ccdproc.ccdtype = ''
iraf.ccdred.instrument = "/dev/null"
iraf.set(direc=ntt.__path__[0] + '/')
if _interactive:
_interact = 'yes'
else:
_interact = 'no'
if _verbose:
iraf.ccdred.verbose = 'yes'
iraf.specred.verbose = 'yes'
else:
iraf.specred.verbose = 'no'
iraf.ccdred.verbose = 'no'
import datetime
import 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
# if they are not sorted the fieldlist dict could crash
files = ntt.sofiphotredudef.sortbyJD(files)
outputlist = []
setup = []
fieldlist = {}
OBID = {}
RA = {}
DEC = {}
objects = {}
flats = {}
lamps1 = {}
_rdnoise = readkey3(readhdr(re.sub('\n', '', files[0])), 'ron')
_gain = readkey3(readhdr(re.sub('\n', '', files[0])), 'gain')
for img in files:
img = re.sub('\n', '', img)
hdr = readhdr(img)
_object = readkey3(hdr, 'object')
_filter = readkey3(hdr, 'filter')
_date = readkey3(hdr, 'date-night')
_exptime = readkey3(hdr, 'exptime')
_grism = readkey3(hdr, 'grism')
_obsmode = readkey3(hdr, 'obsmode')
_type = ''
if _grism.lower() not in ['gr', 'gb']:
_type = 'image'
if not _type:
if _object.lower() == 'flat':
_type = 'flat'
if _date not in flats:
flats[_date] = {}
if _grism not in flats[_date]:
flats[_date][_grism] = [img]
else:
flats[_date][_grism].append(img)
elif _object.lower() == 'lamp':
_lampid = (readkey3(hdr, 'esoid'), readkey3(hdr, 'grism'))
if _lampid not in lamps1:
lamps1[_lampid] = [None, None]
if readkey3(hdr, 'lamp1') == 'Xenon':
lamps1[_lampid][0] = img
else:
lamps1[_lampid][1] = img
_type = 'lamp'
# if readkey3(hdr,'lamp1')=='Xenon':
# _type='lamp'
# if _grism not in lamps:
# lamps[_grism]=[img]
# else:
# lamps[_grism].append(img)
# else:
# _type='notgood'
if not _type:
_ra = readkey3(hdr, 'RA')
_dec = readkey3(hdr, 'DEC')
_object_name = readkey3(hdr, 'object')
_OBID = (readkey3(hdr, 'esoid'), _grism)
if string.count(_object_name, '/') or string.count(_object_name, '.') or string.count(_object_name, ' '):
nameobj = string.split(_object_name, '/')[0]
nameobj = string.split(nameobj, ' ')[0]
nameobj = string.split(nameobj, '.')[0]
else:
nameobj = _object_name
if _grism not in fieldlist:
fieldlist[_grism] = {}
if _OBID not in OBID:
count = 1
nameobj0 = nameobj + '_' + str(count)
answ = 'yes'
while answ == 'yes':
if nameobj0 in fieldlist[_grism]:
count = count + 1
nameobj0 = nameobj + '_' + str(count)
else:
answ = 'no'
fieldlist[_grism][nameobj0] = []
OBID[readkey3(hdr, 'esoid'), _grism] = nameobj0
fieldlist[_grism][nameobj0].append(img)
if _verbose:
print img
print _type, _object, _filter
print 'lamps', lamps1
lamps = {}
for _lampid in lamps1:
lamp = ''
output = 'arc_' + str(_lampid[0]) + '_' + str(_lampid[1]) + '.fits'
if lamps1[_lampid][0] and lamps1[_lampid][1]:
print lamps1[_lampid][0], lamps1[_lampid][1]
# try:
ntt.util.delete(output)
iraf.imarith(lamps1[_lampid][0], '-', lamps1[_lampid]
[1], result=output, verbose='yes')
# except:
# print 'warning, lamp file not ON/OFF'
# os.system('cp '+lamps1[_lampid][0]+' '+output)
lamp = output
elif lamps1[_lampid][0] and not lamps1[_lampid][1]:
os.system('cp ' + lamps1[_lampid][0] + ' ' + output)
lamp = output
if lamp:
if _lampid[1] not in lamps:
lamps[_lampid[1]] = [lamp]
else:
lamps[_lampid[1]].append(lamp)
if _verbose:
print '\n### FIELDS\n', fieldlist
print '\n### OBID\n', OBID
print '\n### FLATS\n', flats
print '\n### LAMPS\n', lamps
# if not flats:
# sys.exit('\n### error: spectroscopic flat not available, add flats in the directory and try again')
# if not lamps:
# sys.exit('\n### error: spectroscopic lamp not available, add lamps in
# the directory and try again')
if not listflat:
print '\n### list of available spectroscopic flats (ON,OFF):'
for _date in flats:
for _grism in flats[_date]:
for img in flats[_date][_grism]:
if pyfits.open(img)[0].data.mean() >= 2000:
print img, _grism, _date, 'ON ? '
else:
print img, _grism, _date, 'OFF ? '
for _date in flats:
for _grism in flats[_date]:
flat = {'ON': [], 'OFF': []}
for img in flats[_date][_grism]:
_type = ''
if readkey3(hdr, 'lamp3'):
print '\n### header lamp3 found: flat ON ', str(img)
_type = 'ON'
else:
if pyfits.open(img)[0].data.mean() >= 2000:
_type = 'ON'
else:
_type = 'OFF'
aa, bb, cc = ntt.util.display_image(img, 1, '', '', False)
print '\n### number of flat already selected (ON,OFF): \n ### please select same number ' \
'of ON and OFF flats \n' + \
str(len(flat['ON'])) + ' ' + str(len(flat['OFF']))
print '\n### image ' + str(img)
answ = raw_input(
'ON/OFF/REJECT/STOP [' + str(_type) + '] ok (ON[n]/OFF[f]/r/s) [' + _type + '] ? ')
if not answ:
answ = _type
if answ in ['ON', 'on', 'n']:
_type = 'ON'
if answ in ['OFF', 'off', 'f']:
_type = 'OFF'
if answ in ['s', 'S', 'STOP', 'stop', 'Stop']:
_type = 'stop'
if answ in ['r', 'R', 'reject']:
_type = 'r'
if _type in ['ON', 'OFF']:
flat[_type].append(img)
elif _type == 'stop':
if len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) >= 2:
break
elif len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) == 0:
break
else:
print '\n### Warning: you can stop only if the numbers of ON and OFF are the same'
print len(flat['ON']), len(flat['OFF'])
if len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) >= 2:
ff = open('_flatlist', 'w')
for ii in range(0, len(flat['OFF'])):
delete('flat_' + str(_date) + '_' + str(_grism) +
'_' + str(MJDtoday) + '_' + str(ii) + '.fits')
iraf.imarith(flat['ON'][ii], '-', flat['OFF'][ii],
result='flat_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
ii) + '.fits', verbose='no')
ff.write(
'flat_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(ii) + '.fits\n')
ff.close()
masterflat = 'flat_' + \
str(_date) + '_' + str(_grism) + \
'_' + str(MJDtoday) + '.fits'
delete(masterflat)
_order = '80'
iraf.ccdred.flatcombine(input='@_flatlist', output=masterflat, combine='median', rdnoise=_rdnoise,
gain=_gain, ccdtype='')
hdr = readhdr(masterflat)
matching = [s for s in hdr.keys() if "IMCMB" in s]
for imcmb in matching:
aaa = iraf.hedit(masterflat, imcmb, delete='yes', update='yes',
verify='no', Stdout=1)
delete('_flatlist')
print masterflat
correctcard(masterflat)
if masterflat not in outputlist:
outputlist.append(masterflat)
ntt.util.updateheader(masterflat, 0, {'FILETYPE': [41102, 'flat field'],
'SINGLEXP': [False, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
print '\n### master flat ........... done '
delete('n' + masterflat)
iraf.specred.response(masterflat, normaliz=masterflat + '[100:900,*]',
response='n' + masterflat, interac=_interact, thresho='INDEF', sample='*',
naverage=2,
function='spline3', low_rej=3, high_rej=3, order=_order, niterat=20, grow=0,
graphic='stdgraph', mode='q')
listflat.append('n' + masterflat)
if 'n' + masterflat not in outputlist:
outputlist.append('n' + masterflat)
ntt.util.updateheader('n' + masterflat, 0, {'FILETYPE': [41203, 'normalized flat field'],
'TRACE1': [masterflat, 'Originating file']})
# ntt.util.updateheader('n'+masterflat,0,{'TRACE1':[masterflat,'']})
flattot = flat['ON'] + flat['OFF']
num = 0
for img in flattot:
num = num + 1
ntt.util.updateheader(masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file'],
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader('n' + masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
if listflat:
print '\n### flat available:\n### ' + str(listflat), '\n'
elif len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) == 0:
print '\n### no good flats in this set ......'
else:
sys.exit('\n### Error: number of ON and OFF not the same')
for _grism in fieldlist:
obj0 = fieldlist[_grism][fieldlist[_grism].keys()[0]][0]
# ############# arc #########################
if _grism not in lamps:
print '\n### take arc from archive '
arcfile = ntt.util.searcharc(obj0, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
lamps[_grism] = [arcfile]
if _grism in lamps:
arclist = lamps[_grism]
if arclist:
arcfile = ntt.util.searcharc(obj0, arclist)[0]
else:
arcfile = ntt.util.searcharc(obj0, '')[0]
print arcfile
if arcfile:
print arcfile
datea = readkey3(readhdr(arcfile), 'date-night')
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
if _doflat:
if listflat:
flat0 = ntt.util.searchflat(arcfile, listflat)[0]
else:
flat0 = ''
else:
flat0 = ''
if flat0:
_flatcor = 'yes'
else:
_flatcor = 'no'
_doflat = False
ntt.util.delete('arc_' + datea + '_' + _grism +
'_' + str(MJDtoday) + '.fits')
print arcfile, flat0, _flatcor, _doflat
if _doflat:
iraf.noao.imred.ccdred.ccdproc(arcfile,
output='arc_' + datea + '_' + _grism +
'_' +
str(MJDtoday) + '.fits',
overscan='no', trim='no', zerocor='no', flatcor=_flatcor, flat=flat0)
else:
os.system('cp ' + arcfile + ' ' + 'arc_' + datea +
'_' + _grism + '_' + str(MJDtoday) + '.fits')
iraf.noao.imred.ccdred.ccdproc('arc_' + datea + '_' + _grism + '_' + str(MJDtoday) + '.fits', output='',
overscan='no', trim='yes', zerocor='no', flatcor='no', flat='',
trimsec='[30:1000,1:1024]')
arcfile = 'arc_' + datea + '_' + \
_grism + '_' + str(MJDtoday) + '.fits'
ntt.util.correctcard(arcfile)
print arcfile
if arcfile not in outputlist:
outputlist.append(arcfile)
ntt.util.updateheader(arcfile, 0, {'FILETYPE': [41104, 'pre-reduced 2D arc'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'PROV1': [readkey3(readhdr(arcfile), 'ARCFILE'), 'Originating file'],
'TRACE1': [readkey3(readhdr(arcfile), 'ARCFILE'),
'Originating file']})
arcref = ntt.util.searcharc(obj0, '')[0]
if not arcref:
identific = iraf.longslit.identify(images=arcfile, section='column 10',
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat', nsum=10,
fwidth=7, order=3, mode='h', Stdout=1, verbose='yes')
else:
print arcref
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(ntt.util.searcharc(obj0, '')[1] + '/database/id' + re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(obj0, '')[1] + '/database/id' + re.sub('.fits', '',
arcref) + ' database/')
print arcref, arcfile
# time.sleep(5)
# os.system('rm -rf database/idarc_20130417_GR_56975')
# raw_input('ddd')
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac='NO', # _interact,
section='column 10', shift=0.0,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
# print identific
# raw_input('ddd')
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac=_interact,
section='column 10', shift=1.0,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
# fitsfile = ntt.efoscspec2Ddef.continumsub('new3.fits', 6, 1)
# I need to run twice I don't know why
# print identific
# raw_input('ddd')
if _interactive:
answ = raw_input(
'\n### do you like the identification [[y]/n]')
if not answ:
answ = 'y'
else:
answ = 'y'
if answ in ['n', 'N', 'no', 'NO', 'No']:
yy1 = pyfits.open(arcref)[0].data[:, 10:20].mean(1)
xx1 = arange(len(yy1))
yy2 = pyfits.open(arcfile)[0].data[:, 10:20].mean(1)
xx2 = arange(len(yy2))
ntt.util.delete('_new3.fits')
hdu = pyfits.PrimaryHDU(yy1)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('_new3.fits')
fitsfile = ntt.efoscspec2Ddef.continumsub('_new3.fits', 4, 1)
yy1 = pyfits.open(fitsfile)[0].data
ntt.util.delete('_new3.fits')
hdu = pyfits.PrimaryHDU(yy2)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('_new3.fits')
fitsfile = ntt.efoscspec2Ddef.continumsub('_new3.fits', 4, 1)
yy2 = pyfits.open(fitsfile)[0].data
_shift = ntt.efoscspec2Ddef.checkwavelength_arc(
xx1, yy1, xx2, yy2, '', '') * (-1)
print arcref, arcfile, _shift
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac='YES',
section='column 10', shift=_shift,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
answ = raw_input('\n### is it ok now ? [[y]/n] ')
if not answ:
answ = 'y'
if answ in ['n', 'N', 'no', 'NO', 'No']:
sys.exit(
'\n### Warning: line identification with some problems')
iraf.longslit.reidentify(referenc=arcfile, images=arcfile, interac='NO', section='column 10',
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat', overrid='yes', step=10,
newaps='yes', nsum=5, nlost=2, mode='h', verbose='no')
iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile), fitname=re.sub('.fits', '', arcfile),
interac='no', combine='yes', databas='database',
function='legendre', yorder=4, logfile='', plotfil='', mode='h')
if identific:
_rms = float(identific[-1].split()[-1])
_num = float(identific[-1].split()[2].split('/')[0])
hdr = ntt.util.readhdr(arcfile)
hedvec = {'LAMRMS': [_rms * .1, 'residual RMS [nm]'],
'LAMNLIN': [_num, 'Nb of arc lines used in the fit of the wavel. solution'],
'SPEC_ERR': [(_rms * .1) / sqrt(float(_num)), 'statistical uncertainty'],
'SPEC_SYE': [0.1, 'systematic error']}
ntt.util.updateheader(arcfile, 0, hedvec)
else:
sys.exit('Warning: arcfile not found')
else:
print 'here'
# ########################################################################################################
for field in fieldlist[_grism]:
listaobj = fieldlist[_grism][field]
listaobj = ntt.sofiphotredudef.sortbyJD(listaobj)
listatemp = listaobj[:]
# ############## flat ######################
if listflat and _doflat:
flat0 = ntt.util.searchflat(listaobj[0], listflat)[0]
else:
flat0 = ''
if flat0:
_flatcor = 'yes'
else:
_flatcor = 'no'
########## crosstalk ###########################
listatemp2 = []
_date = readkey3(readhdr(listatemp[0]), 'date-night')
for img in listatemp:
# num2=listatemp.index(listasub[j])
imgout = field + '_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
listatemp.index(img)) + '.fits'
print '\n### input image: ' + str(img)
delete(imgout)
listatemp2.append(imgout)
if _docross:
print '### correct for cross talk ..... done'
ntt.sofiphotredudef.crosstalk(img, imgout)
correctcard(imgout)
ntt.util.updateheader(
imgout, 0, {'CROSSTAL': ['True', '']})
else:
os.system('cp ' + img + ' ' + imgout)
correctcard(imgout)
if _flatcor == 'yes':
print '### correct for flat field ..... done'
try:
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='no', zerocor='no',
flatcor=_flatcor, flat=flat0)
except:
iraf.imutil.imreplace(
images=flat0, value=0.01, lower='INDEF', upper=0.01, radius=0)
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='no', zerocor='no',
flatcor=_flatcor, flat=flat0)
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='yes', zerocor='no',
flatcor='no', flat='', trimsec='[30:1000,1:1024]')
ntt.util.updateheader(
imgout, 0, {'FLATCOR': [flat0, 'flat correction']})
if imgout not in outputlist:
outputlist.append(imgout)
ntt.util.updateheader(imgout, 0, {'FILETYPE': [42104, 'pre-reduced frame'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'PROV1': [readkey3(readhdr(imgout), 'ARCFILE'), 'Originating file'],
'TRACE1': [readkey3(readhdr(imgout), 'ARCFILE'), 'Originating file']})
print '### output image: ' + str(imgout)
listatemp = listatemp2[:]
######### differences object images #####################
listasub = ntt.sofispec2Ddef.findsubimage(listatemp)
reduced = []
print '\n### Select Frames to be subtracted (eg A-B, B-A, C-D, D-C, ....) '
print '### frame1 \t frame2 \t offset1 \t offset2 \t JD1 \t JD2\n'
if len(listatemp) >= 2 and len(listasub) >= 2:
for j in range(0, len(listatemp)):
print '### ', listatemp[j], listasub[j], str(readkey3(readhdr(listatemp[j]), 'xcum')), str(
readkey3(readhdr(listasub[j]), 'xcum')), \
str(readkey3(readhdr(listatemp[j]), 'JD')), str(
readkey3(readhdr(listatemp[j]), 'JD'))
if _interactive:
answ = raw_input('\n### ok [[y]/n] ? ')
if not answ:
answ = 'y'
else:
answ = 'y'
num1 = j
image1 = listatemp[j]
_date = readkey3(readhdr(image1), 'date-night')
if answ == 'y':
num2 = listatemp.index(listasub[j])
image2 = listasub[j]
else:
image2 = raw_input(
'which image do you want to subtract')
num2 = listatemp.index(image2)
imgoutsub = field + '_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
num1) + '_' + str(num2) + '.fits'
delete(imgoutsub)
iraf.images.imutil.imarith(
operand1=image1, op='-', operand2=image2, result=imgoutsub, verbose='no')
ntt.util.updateheader(imgoutsub, 0, {'skysub': [image2, 'sky image subtracted'],
'FILETYPE': [42115, 'pre-reduced frame sky subtracted'],
'TRACE1': [image1, 'Originating file'],
'PROV2': [readkey3(readhdr(image2), 'ARCFILE'),
'Originating file'],
'TRACE2': [image2, 'Originating file']})
reduced.append(imgoutsub)
if imgoutsub not in outputlist:
outputlist.append(imgoutsub)
######################## 2D wavelengh calibration ########
for img in reduced:
if arcfile:
hdra = ntt.util.readhdr(arcfile)
delete('t' + img)
iraf.specred.transform(input=img, output='t' + img, minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
ntt.util.updateheader('t' + img, 0,
{'ARC': [arcfile, ''], 'FILETYPE': [42106, 'wavelength calibrate 2D frames'],
'TRACE1': [img, 'Originating file']})
ntt.util.updateheader(
't' + img, 0, {'TRACE1': [img, 'Originating file']})
ntt.util.updateheader('t' + img, 0,
{'LAMRMS': [ntt.util.readkey3(hdra, 'LAMRMS'), 'residual RMS [nm]'],
'LAMNLIN': [ntt.util.readkey3(hdra, 'LAMNLIN'), 'number of arc lines'],
'SPEC_ERR': [ntt.util.readkey3(hdra, 'SPEC_ERR'), 'statistical uncertainty'],
'SPEC_SYE': [ntt.util.readkey3(hdra, 'SPEC_SYE'), 'systematic error']})
###########################
delete('t' + arcfile)
iraf.specred.transform(input=arcfile, output='t' + arcfile, minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
specred = ntt.util.spectraresolution2(arcfile, 50)
if specred:
ntt.util.updateheader(
't' + img, 0, {'SPEC_RES': [specred, 'Spectral resolving power']})
delete('t' + arcfile)
###########################
iraf.hedit('t' + img, 'TRACE2', delete='yes',
update='yes', verify='no', Stdout=1)
if 't' + img not in outputlist:
outputlist.append('t' + img)
print '\n### 2D frame t' + str(img) + ' wavelengh calibrated ............ done'
_skyfile = ntt.__path__[
0] + '/standard/ident/sky_' + _grism + '.fits' # check in wavelengh #########
hdr = ntt.util.readhdr(img)
if glob.glob(_skyfile) and readkey3(hdr, 'exptime') > 20.:
_original = readkey3(hdr, 'ORIGFILE')
_archive = readkey3(hdr, 'ARCFILE')
if os.path.isfile(_archive):
imgstart = _archive
elif os.path.isfile(_original):
imgstart = _original
else:
imgstart = ''
if imgstart:
delete('_tmp.fits')
print imgstart, arcfile
iraf.specred.transform(input=imgstart, output='_tmp.fits', minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
shift = ntt.sofispec2Ddef.skysofifrom2d('_tmp.fits', _skyfile)
zro = pyfits.open('_tmp.fits')[0].header.get('CRVAL2')
delete('_tmp.fits')
if _interactive:
answ = raw_input(
'do you want to correct the wavelengh calibration with this shift: ' + str(
shift) + ' [[y]/n] ? ')
if not answ:
answ = 'y'
else:
answ = 'y'
if answ.lower() in ['y', 'yes']:
ntt.util.updateheader('t' + img, 0,
{'CRVAL2': [zro + int(shift), ''], 'shift': [float(shift), '']})
# ntt.util.updateheader('t'+img,0,{'shift':[float(shift),'']})
print '\n### check wavelengh calibration with sky lines ..... done'
try:
hdrt = ntt.util.readhdr('t' + img)
wavelmin = float(readkey3(hdrt, 'CRVAL2')) + (0.5 - float(readkey3(hdrt, 'CRPIX2'))) * float(
readkey3(hdrt, 'CDELT2'))
wavelmax = float(readkey3(hdrt, 'CRVAL2')) + (
(float(readkey3(hdrt, 'NAXIS2')) + 0.5 - float(readkey3(hdrt, 'CRPIX2'))) * float(
readkey3(hdrt, 'CDELT2')))
hedvec = {}
hedvec['WAVELMIN'] = [
wavelmin * .1, '[nm] minimum wavelength']
hedvec['WAVELMAX'] = [
wavelmax * .1, ' [nm] maximum wavelength']
hedvec['XMIN'] = [wavelmin, '[A] minimum wavelength']
hedvec['XMAX'] = [wavelmax, '[A] maximum wavelength']
hedvec['SPEC_BW'] = [
(wavelmax * .1) - (wavelmin * .1), '[nm] Bandpass Width Wmax - Wmin']
hedvec['SPEC_VAL'] = [
((wavelmax * .1) + (wavelmin * .1)) / 2., '[nm] Mean Wavelength']
hedvec['SPEC_BIN'] = [
((wavelmax * .1) - (wavelmin * .1)) /
(float(readkey3(hdr, 'NAXIS2')) - 1),
'Wavelength bin size [nm/pix]']
hedvec['VOCLASS'] = ['SPECTRUM V1.0', 'VO Data Model']
hedvec['VOPUB'] = ['ESO/SAF',
'VO Publishing Authority']
# hedvec['APERTURE']=[float(re.sub('slit','',readkey3(hdrt,'slit'))),'aperture width']
ntt.util.updateheader('t' + img, 0, hedvec)
except:
pass
else:
print '\n### Warning: arc not found for the image ' + str(img) + ' with setup ' + str(_grism)
reduceddata = rangedata(outputlist)
print '\n### adding keywords for phase 3 ....... '
f = open('logfile_spec2d_' + str(reduceddata) +
'_' + str(datenow) + '.raw.list', 'w')
for img in outputlist:
if img[-4:] == 'fits':
hdr = readhdr(img)
# ###############################################
# cancel pc matrix
if 'PC1_1' in hdr.keys():
aaa = iraf.hedit(img, 'PC1_1', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC2_2' in hdr.keys():
aaa = iraf.hedit(img, 'PC2_2', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC1_2' in hdr.keys():
aaa = iraf.hedit(img, 'PC1_2', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC2_1' in hdr.keys():
aaa = iraf.hedit(img, 'PC2_1', delete='yes',
update='yes', verify='no', Stdout=1)
#################
# added for DR2
print img
if 'NCOMBINE' in hdr:
_ncomb = readkey3(hdr, 'NCOMBINE')
else:
_ncomb = 1.0
ntt.util.updateheader(
img, 0, {'DETRON ': [12, 'Readout noise per output (e-)']})
ntt.util.updateheader(img, 0, {'EFFRON': [12. * (1 / sqrt(readkey3(hdr, 'ndit') * _ncomb)) * sqrt(pi / 2),
'Effective readout noise per output (e-)']})
ntt.util.phase3header(img) # phase 3 definitions
############################
# change for DR2
############################
texp = float(readkey3(hdr, 'dit')) * float(readkey3(hdr, 'ndit'))
mjdend = float(readkey3(hdr, 'MJD-OBS')) + (float(readkey3(hdr, 'ndit')) * (
float(readkey3(hdr, 'dit')) + 1.8)) / (60. * 60. * 24.)
strtexp = time.strftime('%H:%M:%S', time.gmtime(texp))
_telapse = (mjdend - float(readkey3(hdr, 'MJD-OBS'))) * \
60. * 60 * 24.
# tmid=_telapse/2.
tmid = (mjdend + float(readkey3(hdr, 'MJD-OBS'))) / 2
ntt.util.updateheader(img, 0, {'quality': ['Final', 'fast or rapid reduction'],
'BUNIT': ['ADU', 'Physical unit of array values'],
'DIT': [readkey3(hdr, 'dit'), 'Detector Integration Time'],
'NDIT': [readkey3(hdr, 'ndit'), 'Number of sub-integrations'],
'TEXPTIME': [texp, 'Total integration time of all exposures (s)'],
'EXPTIME': [texp, 'Total integration time. ' + strtexp],
'MJD-END': [mjdend, 'End of observations (days)'],
'TELAPSE': [_telapse, 'Total elapsed time [days]'],
'TMID': [tmid, '[d] MJD mid exposure'],
'TITLE': [readkey3(hdr, 'object'), 'Dataset title'],
#'TITLE':[str(tmid)[0:9]+' '+str(readkey3(hdr,'object'))+' '+str(readkey3(hdr,'grism'))+' '+\
# str(readkey3(hdr,'filter'))+'
# '+str(readkey3(hdr,'slit')),'Dataset
# title'],\
'EXT_OBJ': [False, 'TRUE if extended'],
'CONTNORM': [False, 'spectrum normalized to the continuum'],
'TOT_FLUX': [False, 'TRUE if phot cond and all src flux is captured'],
'SPECSYS': ['TOPOCENT', 'Reference frame for spectral coordinate'],
'FLUXCAL': ['ABSOLUTE', 'type of flux calibration'],
'FLUXERR': [34.7, 'Fractional uncertainty of the flux [%]'],
'DISPELEM': ['Gr#' + re.sub('Gr', '', readkey3(hdr, 'grism')),
'Dispersive element name']})
if readkey3(hdr, 'tech'):
ntt.util.updateheader(
img, 0, {'PRODCATG': ['SCIENCE.IMAGE', 'Data product category']})
aaa = str(readkey3(hdr, 'arcfiles')) + '\n'
f.write(aaa)
try:
ntt.util.airmass(img) # phase 3 definitions
except:
print '\n### airmass not computed for image: ', img
else:
print img + ' is not a fits image'
f.close()
return outputlist, 'logfile_spec2d_' + str(reduceddata) + '_' + str(datenow) + '.raw.list'
parent_dir = os.getcwd() + '/'
now = datetime.datetime.now()
JD = julday(now.year, now.month, now.day, now.hour, now.minute)
user = os.environ['USER']
Y, M, D, H, m, s, x, x, x = time.gmtime()
lista = ntt.util.readlist(args[0])
if option.output:
_output = option.output
else:
_output = user + '_' + str(JD) + '_exel.txt'
lista = ntt.sofiphotredudef.sortbyJD(lista)
for img in lista:
hdr = readhdr(img)
print readkey3(hdr, 'ORIGFILE')
print readkey3(hdr, 'ARCFILE')
print readkey3(hdr, 'OBJECT')
print readkey3(hdr, 'date-obs')
OBID = readkey3(hdr, 'HIERARCH ESO OBS ID')
_object = readkey3(hdr, 'object')
_start = readkey3(hdr, 'HIERARCH ESO OBS START')
_end = str(Y) + '-' + str(M) + '-' + str(D) + 'T' + \
str(H) + ':' + str(m) + ':' + str(s)
print 'A \t OB done fully within constraints \nB \t OB mostly with in constrain\nC \t not usefull for science\n'
_QC = raw_input('QC Grade (A,B,C) [A] ?')
if not _QC:
_QC = 'A'
print 'seeing,clouds,wind (1.0,clear,low)'
_observcond = raw_input('observing conditions [1.5,clear,10km/s]?')
if not _observcond:
description="> check header "
usage= "%prog \t listframes [option] "
if __name__ == "__main__":
parser = OptionParser(usage=usage,description=description,version="%prog 1.0")
parser.add_option("-v", "--verbose",dest="verbose",action="store_true")
option,args = parser.parse_args()
if len(args)<1:
sys.argv.append('--help')
option,args = parser.parse_args()
lista=ntt.util.readlist(args[0])
lista=ntt.sofiphotredudef.sortbyJD(lista)
for img in lista:
correctcard(img)
hdr=readhdr(img)
_instrume=readkey3(hdr,'instrume')
_type=readkey3(hdr,'tech').lower()
print img,_type,_instrume
for key in keyword[_instrume]['all'].keys():
if readkey3(hdr,key)==None:
print key,readkey3(hdr,key)
else:
if type(readkey3(hdr,key)) is not typeobj[keyword[_instrume]['all'][key]]:
print key,readkey3(hdr,key)
else:
if option.verbose:
print key,readkey3(hdr,key)
for key in keyword[_instrume][_type].keys():
if readkey3(hdr,key)==None:
print key,readkey3(hdr,key)
else:
def makeillumination(lista, flatfield): #,outputfile,illum_frame):
import os, glob, string, re
from astropy.io import fits as pyfits
import ntt
from ntt.util import readhdr, readkey3, delete, display_image, defsex, name_duplicate, correctcard
from numpy import compress, array, argmax, argmin, min, argsort, float32
import datetime
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
illum_frame = name_duplicate(
lista[0], 'illum_' + _date + '_' + _filter + '_' + str(MJDtoday), '')
from pyraf import iraf
iraf.images(_doprint=0, Stdout=0)
iraf.imutil(_doprint=0, Stdout=0)
iraf.utilities(_doprint=0, Stdout=0)
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.ccdred(_doprint=0, Stdout=0)
iraf.digiphot(_doprint=0, Stdout=0)
iraf.daophot(_doprint=0, Stdout=0)
iraf.generic(_doprint=0, Stdout=0)
toforget = [
'digiphot.daophot', 'imutil.imarith', 'image', 'utilities.surfit'
]
for t in toforget:
iraf.unlearn(t)
n = len(lista)
# start loop to read image names from the input file
lista1 = []
iraf.ccdred.verbose = 'no'
ff = open('templist.lst', 'w')
for i in range(0, len(lista)):
ff.write('C' + lista[i] + '\n')
delete('C' + lista[i])
delete('C' + re.sub('.fits', '_sub.fits', lista[i]))
ntt.sofiphotredudef.crosstalk(lista[i], 'C' + lista[i])
iraf.noao.imred.ccdred.ccdproc('C' + lista[i],
output='',
overscan="no",
trim="yes",
ccdtype='',
darkcor='no',
fixpix='no',
zerocor="no",
flatcor='yes',
illumco='no',
trimsec='[1:1024,1:1007]',
biassec='',
flat=flatfield,
illum='')
correctcard('C' + lista[i])
lista1.append('C' + lista[i])
ff.close()
print '\n### prereducing STD frames to compute illumination correction ........'
lista2, skyfile = ntt.sofiphotredudef.skysub(
lista1, readkey3(readhdr(lista1[0]), 'ron'),
readkey3(readhdr(lista1[0]), 'gain'), True)
lista2 = ntt.sofiphotredudef.sortbyJD(lista2)
print '\n### use x on the star and q to continue....'
display_image(lista2[0], 2, '', '', False)
delete('tmpone.coo')
iraf.image.tv.imexamine(lista2[0],
2,
logfile='tmpone.coo',
keeplog='yes',
xformat='',
yformat='',
wcs='logical')
iraf.tvmark(2,
'tmpone.coo',
mark="circle",
number='yes',
label='no',
radii=8,
nxoffse=5,
nyoffse=5,
color=204,
txsize=2)
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x0, y0 = string.split(xycoo[0])
x0 = float(x0)
y0 = float(y0)
xcum0 = readkey3(readhdr(lista2[0]), 'xcum')
ycum0 = readkey3(readhdr(lista2[0]), 'ycum')
iraf.digiphot(_doprint=0, Stdout=0)
iraf.daophot(_doprint=0, Stdout=0)
iraf.noao.digiphot.daophot.datapars.datamin = -1000
iraf.noao.digiphot.daophot.datapars.datamax = 60000
iraf.noao.digiphot.daophot.daopars.function = 'gauss'
iraf.noao.digiphot.daophot.photpars.zmag = 0
namesex = defsex('default.sex')
for i in range(0, len(lista2)):
j = i + 1
xcum = readkey3(readhdr(lista2[i]), 'xcum')
ycum = readkey3(readhdr(lista2[i]), 'ycum')
xx = x0 - xcum0 + xcum
yy = y0 - ycum0 + ycum
# sex objects
os.system('sex ' + lista2[i] + ' -c ' + namesex + '> _logsex')
delete('_logsex')
xpix = iraf.proto.fields('detections.cat', fields='2', Stdout=1)
ypix = iraf.proto.fields('detections.cat', fields='3', Stdout=1)
cm = iraf.proto.fields('detections.cat', fields='4', Stdout=1)
cm = compress((array(xpix) != ''), array(cm, float))
ypix = compress((array(xpix) != ''), array(ypix, float))
xpix = compress((array(xpix) != ''), array(xpix, float))
if len(xpix) > 300:
num = 300
else:
num = len(xpix) - 1
xpix = xpix[argsort(cm)][0:num]
ypix = ypix[argsort(cm)][0:num]
distance = (ypix - yy)**2 + (xpix - xx)**2
xx1, yy1 = xpix[argmin(distance)], ypix[argmin(distance)]
f = open('tmpone.coo', 'w')
f.write(str(xx1) + ' ' + str(yy1) + '\n')
f.close()
display_image(lista2[i], 1, '', '', False)
iraf.tvmark(1,
'tmpone.coo',
mark="circle",
number='yes',
label='no',
radii=8,
nxoffse=5,
nyoffse=5,
color=204,
txsize=2)
answ = 'n'
while answ != 'y':
answ = raw_input('selected the right one [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['y', 'YES', 'yes', 'Y']:
print lista2[i]
delete('pippo.' + str(j) + '.mag')
gggg = iraf.digiphot.daophot.phot(lista2[i],
"tmpone.coo",
output="pippo." + str(j) +
".mag",
verify='no',
interac='no',
Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
else:
print '\n### select the std star'
display_image(lista2[i], 1, '', '', False)
iraf.image.tv.imexamine(lista2[i],
1,
logfile='tmpone.coo',
keeplog='yes',
xformat='',
yformat='',
wcs='logical')
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x2, y2 = string.split(xycoo[0])
f = open('tmpone.coo', 'w')
f.write(str(x2) + ' ' + str(y2) + '\n')
f.close()
delete('pippo.' + str(j) + '.mag')
print '###### new selection ' + str(x2), str(y2)
gggg = iraf.digiphot.daophot.phot(lista2[i],
"tmpone.coo",
output='pippo.' + str(j) +
'.mag',
verify='no',
interac='no',
Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
os.system('ls pippo.*.mag > tempmag.lst')
tmptbl0 = iraf.txdump(textfile="@tempmag.lst",
fields="XCENTER,YCENTER,FLUX",
expr='yes',
Stdout=1)
ff = open('magnitudini', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
# delete the temporary images and files
delete("temp*.fits")
delete('temp*.lst')
delete(illum_frame)
print '\n### fitting the illumination surface...'
aaa = iraf.utilities.surfit('magnitudini',
image=illum_frame,
function="polynomial",
xorder=2,
yorder=2,
xterms="full",
ncols=1024,
nlines=1024,
Stdout=1)
iraf.noao.imred.generic.normalize(illum_frame)
correctcard(lista[0])
data, hdr = pyfits.getdata(illum_frame, 0, header=True)
data0, hdr0 = pyfits.getdata(lista[0], 0, header=True)
delete(illum_frame)
pyfits.writeto(illum_frame, float32(data), hdr0)
flatfield0 = string.split(flatfield, '/')[-1]
ntt.util.updateheader(illum_frame, 0,
{'MKILLUM': [flatfield0, 'flat field']})
display_image(illum_frame, 1, '', '', False)
for i in range(0, len(lista)): # in lista:
img = lista[i]
delete('pippo.' + str(i) + '.mag')
delete('C' + img)
delete('C' + re.sub('.fits', '_sky.fits', img))
# delete('C*.fits.mag.1')
# iraf.hedit(illum_frame,'MKILLUM','Illum. corr. created '+flatfield,add='yes',update='yes',verify='no')
return illum_frame
def makeflat(lista):
# print "LOGX:: Entering `makeflat` method/function in %(__file__)s" %
# globals()
flat = ''
import datetime
import glob
import os
import ntt
from ntt.util import readhdr, readkey3, delete, name_duplicate, updateheader, correctcard
from pyraf import iraf
iraf.images(_doprint=0, Stdout=0)
iraf.imutil(_doprint=0, Stdout=0)
iraf.imgeom(_doprint=0, Stdout=0)
# iraf.blkavg(_doprint=0, Stdout=0)
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.generic(_doprint=0, Stdout=0)
toforget = [
'imgeom.blkavg', 'imutil.imarith', 'immatch.imcombine', 'noao.imred'
]
for t in toforget:
iraf.unlearn(t)
import datetime
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
output = name_duplicate(
lista[3],
'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday), '')
if os.path.isfile(output):
answ = raw_input('file already prooduced, do again [y/[n]] ? ')
if not answ:
answ = 'n'
else:
answ = 'y'
if answ in ['yes', 'y', 'YES', 'Y', 'Yes']:
delete(
"temp_off.fits,temp_off_mask.fits,temp_on_mask.fits,temp_on.fits")
iraf.image.immatch.imcombine(lista[0] + ',' + lista[7],
output="temp_off.fits")
iraf.image.immatch.imcombine(lista[1] + ',' + lista[6],
output="temp_off_mask.fits")
iraf.image.immatch.imcombine(lista[2] + ',' + lista[5],
output="temp_on_mask.fits")
iraf.image.immatch.imcombine(lista[3] + ',' + lista[4],
output="temp_on.fits")
# create the bias correction for the flat-on according to the
# Lidman technique0
delete(
"temp_onA.fits,temp_onC.fits,temp_onB.fits,temp_onAC.fits,temp_onACB.fits,temp_onACB_2D.fits"
)
delete("temp_on_bias.fits")
iraf.imgeom.blkavg(input="temp_on.fits[500:600,*]",
output="temp_onA.fits",
option="average",
b1=101,
b2=1)
iraf.imgeom.blkavg(input="temp_on_mask.fits[500:600,*]",
output="temp_onC.fits",
option="average",
b1=101,
b2=1)
iraf.imgeom.blkavg(input="temp_on_mask.fits[50:150,*]",
output="temp_onB.fits",
option="average",
b1=101,
b2=1)
iraf.imutil.imarith("temp_onA.fits", "-", "temp_onC.fits",
"temp_onAC.fits")
iraf.imutil.imarith("temp_onAC.fits", "+", "temp_onB.fits",
"temp_onACB.fits")
iraf.imgeom.blkrep(input="temp_onACB.fits",
output="temp_onACB_2D.fits",
b1=1024,
b2=1)
iraf.imutil.imarith("temp_on.fits", "-", "temp_onACB_2D.fits",
"temp_on_bias.fits")
# same as above for the flat-off
delete(
"temp_offA.fits,temp_offC.fits,temp_offB.fits,temp_offAC.fits,temp_offACB.fits,temp_offACB_2D.fits"
)
delete("temp_off_bias.fits")
iraf.imgeom.blkavg(input="temp_off.fits[500:600,*]",
output="temp_offA.fits",
option="average",
b1=101,
b2=1)
iraf.imgeom.blkavg(input="temp_off_mask.fits[500:600,*]",
output="temp_offC.fits",
option="average",
b1=101,
b2=1)
iraf.imgeom.blkavg(input="temp_off_mask.fits[50:150,*]",
output="temp_offB.fits",
option="average",
b1=101,
b2=1)
iraf.imutil.imarith("temp_offA.fits", "-", "temp_offC.fits",
"temp_offAC.fits")
iraf.imutil.imarith("temp_offAC.fits", "+", "temp_offB.fits",
"temp_offACB.fits")
iraf.imgeom.blkrep(input="temp_offACB.fits",
output="temp_offACB_2D.fits",
b1=1024,
b2=1)
iraf.imutil.imarith("temp_off.fits", "-", "temp_offACB_2D.fits",
"temp_off_bias.fits")
# create the corrected flat-field
# output=name_duplicate("temp_on_bias.fits",'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday),'')
output = name_duplicate(
lista[3],
'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday),
'')
# print lista[0],'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday)
delete(output)
iraf.imutil.imarith("temp_on_bias.fits", "-", "temp_off_bias.fits",
output)
iraf.noao.imred.generic.normalize(output) # normalize the flat-field
correctcard(output)
delete("temp_on*.fits") # delete the temporary images
delete("temp_off*.fits")
print 'flat -> ' + str(output)
else:
print 'skip redoing the flat'
return output
def makefringing(listimg, _output, _xorder, _yorder, _interactive, combine='average', rejection='avsigclip'):
# print "LOGX:: Entering `makefringing` method/function in %(__file__)s" %
# globals()
import os
import string
import re
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
from pyraf import iraf
from pyfits import open as popen
from numpy import median, where, mean
iraf.noao(_doprint=0)
iraf.nproto(_doprint=0)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 100:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? ')
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
listmask = []
ff = open('_listmask', 'w')
gg = open('_listobg', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
gg.write(img + '\n')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
listmask.append('mask_' + img)
os.system('rm -rf mask_' + img)
os.system('rm -rf t_' + img)
os.system('rm -rf z_' + img)
iraf.imsurfit(img, 't_' + img, xorder=_xorder,
yorder=_yorder, type_ou='residual', regions='all')
iraf.imarith('t_' + img, '/', _exptime, 'z_' + img)
os.system('rm -rf t_' + img)
gg.close()
ff.close()
hh.close()
os.system('rm ' + _output)
iraf.objmasks(images='@_listobgz', objmasks='@_listmask', omtype='boolean',
blksize=-16, convolv='block 3 3', hsigma=5, lsigma=3, minpix=10, ngrow=2, agrow=4.)
iraf.imcombine('@_listobgz', output=_output, masktyp='!OBJMASK', maskval=0, combine=_combine, reject=_rejection,
scale='none', statsec='[100:800,100:800]', offsets='', rdnoise='', gain='', nlow=1, nhigh=1, logfile='imcombinelog')
for img in listimg:
os.system('rm -rf mask_' + img)
os.system('rm -rf z_' + img)
os.system('rm _listmask')
os.system('rm _listobg')
iraf.display(_output, frame=2, fill='yes')
header.pop('ESO DET OUT1 GAIN')
except:
pass
try:
header.pop('ESO DET OUT1 RON')
except:
pass
header['ESO DET OUT1 GAIN'] = (
1.18, 'Conversion from electrons to ADU')
header['ESO DET OUT1 RON'] = (
11.6, 'Readout noise per output (e-)')
imm.flush()
imm.close()
#################################################
hdr = readhdr(img)
if not readkey3(hdr, 'pixscale'):
ntt.util.updateheader(img, 0, {
'pixscale': [readkey3(hdr, 'binx') * .12, 'pixel/scale (arcsec)']})
if 'FLATCOR' in hdr and os.path.isfile(hdr['FLATCOR']):
hdrn = readhdr(hdr['FLATCOR'])
if 'NCOMBINE' in hdrn:
nflat = hdrn['NCOMBINE']
else:
nflat = 1
else:
nflat = 1
if 'ZEROCOR' in hdr and os.path.isfile(hdr['ZEROCOR']):
hdrb = readhdr(hdr['ZEROCOR'])
if 'NCOMBINE' in hdrb:
nbias = hdrb['NCOMBINE']
def makeillumination(lista,flatfield):#,outputfile,illum_frame):
import os,glob,string,re
from astropy.io import fits as pyfits
import ntt
from ntt.util import readhdr, readkey3, delete, display_image, defsex, name_duplicate, correctcard
from numpy import compress, array, argmax, argmin, min, argsort, float32
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
illum_frame = name_duplicate(
lista[0], 'illum_' + _date + '_' + _filter + '_' + str(MJDtoday), '')
from pyraf import iraf
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
iraf.utilities(_doprint=0)
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.generic(_doprint=0)
toforget = ['digiphot.daophot', 'imutil.imarith',
'image', 'utilities.surfit']
for t in toforget:
iraf.unlearn(t)
n = len(lista)
# start loop to read image names from the input file
lista1 = []
iraf.ccdred.verbose = 'no'
ff = open('templist.lst', 'w')
for i in range(0, len(lista)):
ff.write('C' + lista[i] + '\n')
delete('C' + lista[i])
delete('C' + re.sub('.fits', '_sub.fits', lista[i]))
ntt.sofiphotredudef.crosstalk(lista[i], 'C' + lista[i])
iraf.noao.imred.ccdred.ccdproc('C' + lista[i], output='', overscan="no", trim="yes", ccdtype='', darkcor='no', fixpix='no', zerocor="no", flatcor='yes',
illumco='no', trimsec='[1:1024,1:1007]', biassec='', flat=flatfield, illum='')
correctcard('C' + lista[i])
lista1.append('C' + lista[i])
ff.close()
print '\n### prereducing STD frames to compute illumination correction ........'
lista2, skyfile = ntt.sofiphotredudef.skysub(lista1, readkey3(
readhdr(lista1[0]), 'ron'), readkey3(readhdr(lista1[0]), 'gain'), True)
lista2 = ntt.sofiphotredudef.sortbyJD(lista2)
print '\n### use x on the star and q to continue....'
display_image(lista2[0], 2, '', '', False)
delete('tmpone.coo')
iraf.image.tv.imexamine(lista2[0], 2, logfile='tmpone.coo',
keeplog='yes', xformat='', yformat='', wcs='logical')
iraf.tvmark(2, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x0, y0 = string.split(xycoo[0])
x0 = float(x0)
y0 = float(y0)
xcum0 = readkey3(readhdr(lista2[0]), 'xcum')
ycum0 = readkey3(readhdr(lista2[0]), 'ycum')
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.noao.digiphot.daophot.datapars.datamin = -1000
iraf.noao.digiphot.daophot.datapars.datamax = 60000
iraf.noao.digiphot.daophot.daopars.function = 'gauss'
iraf.noao.digiphot.daophot.photpars.zmag = 0
namesex = defsex('default.sex')
for i in range(0, len(lista2)):
j = i + 1
xcum = readkey3(readhdr(lista2[i]), 'xcum')
ycum = readkey3(readhdr(lista2[i]), 'ycum')
xx = x0 - xcum0 + xcum
yy = y0 - ycum0 + ycum
# sex objects
os.system('sex ' + lista2[i] + ' -c ' + namesex + '> _logsex')
delete('_logsex')
xpix = iraf.proto.fields('detections.cat', fields='2', Stdout=1)
ypix = iraf.proto.fields('detections.cat', fields='3', Stdout=1)
cm = iraf.proto.fields('detections.cat', fields='4', Stdout=1)
cm = compress((array(xpix) != ''), array(cm, float))
ypix = compress((array(xpix) != ''), array(ypix, float))
xpix = compress((array(xpix) != ''), array(xpix, float))
if len(xpix) > 300:
num = 300
else:
num = len(xpix) - 1
xpix = xpix[argsort(cm)][0:num]
ypix = ypix[argsort(cm)][0:num]
distance = (ypix - yy)**2 + (xpix - xx)**2
xx1, yy1 = xpix[argmin(distance)], ypix[argmin(distance)]
f = open('tmpone.coo', 'w')
f.write(str(xx1) + ' ' + str(yy1) + '\n')
f.close()
display_image(lista2[i], 1, '', '', False)
iraf.tvmark(1, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
answ = 'n'
while answ != 'y':
answ = raw_input('selected the right one [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['y', 'YES', 'yes', 'Y']:
print lista2[i]
delete('pippo.' + str(j) + '.mag')
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output="pippo." + str(j) + ".mag", verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
else:
print '\n### select the std star'
display_image(lista2[i], 1, '', '', False)
iraf.image.tv.imexamine(lista2[
i], 1, logfile='tmpone.coo', keeplog='yes', xformat='', yformat='', wcs='logical')
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x2, y2 = string.split(xycoo[0])
f = open('tmpone.coo', 'w')
f.write(str(x2) + ' ' + str(y2) + '\n')
f.close()
delete('pippo.' + str(j) + '.mag')
print '###### new selection ' + str(x2), str(y2)
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output='pippo.' + str(j) + '.mag', verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
os.system('ls pippo.*.mag > tempmag.lst')
tmptbl0 = iraf.txdump(textfile="@tempmag.lst",
fields="XCENTER,YCENTER,FLUX", expr='yes', Stdout=1)
ff = open('magnitudini', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
# delete the temporary images and files
delete("temp*.fits")
delete('temp*.lst')
delete(illum_frame)
print '\n### fitting the illumination surface...'
aaa = iraf.utilities.surfit('magnitudini', image=illum_frame, function="polynomial",
xorder=2, yorder=2, xterms="full", ncols=1024, nlines=1024, Stdout=1)
iraf.noao.imred.generic.normalize(illum_frame)
correctcard(lista[0])
data, hdr = pyfits.getdata(illum_frame, 0, header=True)
data0, hdr0 = pyfits.getdata(lista[0], 0, header=True)
delete(illum_frame)
pyfits.writeto(illum_frame, float32(data), hdr0)
flatfield0 = string.split(flatfield, '/')[-1]
ntt.util.updateheader(
illum_frame, 0, {'MKILLUM': [flatfield0, 'flat field']})
display_image(illum_frame, 1, '', '', False)
for i in range(0, len(lista)): # in lista:
img = lista[i]
delete('pippo.' + str(i) + '.mag')
delete('C' + img)
delete('C' + re.sub('.fits', '_sky.fits', img))
# delete('C*.fits.mag.1')
# iraf.hedit(illum_frame,'MKILLUM','Illum. corr. created '+flatfield,add='yes',update='yes',verify='no')
return illum_frame
parent_dir = os.getcwd() + '/'
now = datetime.datetime.now()
JD = julday(now.year, now.month, now.day, now.hour, now.minute)
user = os.environ['USER']
Y, M, D, H, m, s, x, x, x = time.gmtime()
lista = ntt.util.readlist(args[0])
if option.output:
_output = option.output
else:
_output = user + '_' + str(JD) + '_exel.txt'
lista = ntt.sofiphotredudef.sortbyJD(lista)
for img in lista:
hdr = readhdr(img)
print readkey3(hdr, 'ORIGFILE')
print readkey3(hdr, 'ARCFILE')
print readkey3(hdr, 'OBJECT')
print readkey3(hdr, 'date-obs')
OBID = readkey3(hdr, 'HIERARCH ESO OBS ID')
_object = readkey3(hdr, 'object')
_start = readkey3(hdr, 'HIERARCH ESO OBS START')
_end = str(Y) + '-' + str(M) + '-' + str(D) + 'T' + \
str(H) + ':' + str(m) + ':' + str(s)
print 'A \t OB done fully within constraints \nB \t OB mostly with in constrain\nC \t not usefull for science\n'
_QC = raw_input('QC Grade (A,B,C) [A] ?')
if not _QC:
_QC = 'A'
print 'seeing,clouds,wind (1.0,clear,low)'
_observcond = raw_input('observing conditions [1.5,clear,10km/s]?')
if not _observcond:
def makefringing(listimg,
_output,
_xorder,
_yorder,
_interactive,
combine='average',
rejection='avsigclip'):
# print "LOGX:: Entering `makefringing` method/function in %(__file__)s" %
# globals()
import os
import string
import re
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
from pyraf import iraf
from pyfits import open as popen
from numpy import median, where, mean
iraf.noao(_doprint=0)
iraf.nproto(_doprint=0)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 100:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? '
)
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
listmask = []
ff = open('_listmask', 'w')
gg = open('_listobg', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
gg.write(img + '\n')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
listmask.append('mask_' + img)
os.system('rm -rf mask_' + img)
os.system('rm -rf t_' + img)
os.system('rm -rf z_' + img)
iraf.imsurfit(img,
't_' + img,
xorder=_xorder,
yorder=_yorder,
type_ou='residual',
regions='all')
iraf.imarith('t_' + img, '/', _exptime, 'z_' + img)
os.system('rm -rf t_' + img)
gg.close()
ff.close()
hh.close()
os.system('rm ' + _output)
iraf.objmasks(images='@_listobgz',
objmasks='@_listmask',
omtype='boolean',
blksize=-16,
convolv='block 3 3',
hsigma=5,
lsigma=3,
minpix=10,
ngrow=2,
agrow=4.)
iraf.imcombine('@_listobgz',
output=_output,
masktyp='!OBJMASK',
maskval=0,
combine=_combine,
reject=_rejection,
scale='none',
statsec='[100:800,100:800]',
offsets='',
rdnoise='',
gain='',
nlow=1,
nhigh=1,
logfile='imcombinelog')
for img in listimg:
os.system('rm -rf mask_' + img)
os.system('rm -rf z_' + img)
os.system('rm _listmask')
os.system('rm _listobg')
iraf.display(_output, frame=2, fill='yes')
def makefringingmask(listimg,
_output,
_interactive,
_combine='average',
_rejection='avsigclip'):
# print "LOGX:: Entering `makefringingmask` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
import glob
import os
import sys
import re
import string
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imutil(_doprint=0)
iraf.nproto(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['nproto.objmasks', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 10:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? '
)
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
hdr0 = readhdr(listimg[0])
_date = readkey3(hdr0, 'date-obs')
_filter = readkey3(hdr0, 'filter')
_exptime = readkey3(hdr0, 'exptime')
_instrume = readkey3(hdr0, 'instrume')
_ron = readkey3(hdr0, 'ron')
_gain = readkey3(hdr0, 'gain')
badpixelmask = 'bad_pixel_mask.pl'
if not os.path.isfile(badpixelmask):
os.system('cp ' + ntt.__path__[0] + '/archive/' + _instrume +
'/badpixels/badpixel_20100210.pl ' + badpixelmask)
ff = open('_listmask', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
delete('mask_' + img)
aaa = iraf.hedit(img,
delete='yes',
field='OBJMASK',
up='yes',
verify='no',
Stdout=1)
aaa = iraf.hedit(img,
delete='yes',
field='BPM',
up='yes',
verify='no',
Stdout=1)
delete('z_' + img)
iraf.imutil.imexpr(expr='(a - median(a))/' + str(_exptime),
a=img,
output='z_' + img,
verbose='no')
ntt.util.updateheader('z_' + img, 0, {'EXPTIME': [1, '']})
ff.close()
hh.close()
if not _output:
_output = 'fringing_' + str(_date) + '_' + str(_filter) + '.fits'
delete(_output)
print ' making mask for each frame .......'
ccc = iraf.nproto.objmasks(images='@_listobgz',
objmasks='@_listmask',
omtype='boolean',
blksize=-16,
convolv='block 3 3',
hsigma=5,
lsigma=3,
minpix=10,
ngrow=2,
agrow=4.,
Stdout=1)
print 'combining all frames, masking the objects .....'
iraf.imcombine('@_listobgz',
output=_output,
masktyp='!OBJMASK',
maskval=0,
combine=_combine,
reject=_rejection,
scale='none',
statsec='[100:800,100:800]',
rdnoise='',
gain='',
nlow=1,
nhigh=1,
logfile='imcombinelog')
ntt.util.phase3header(_output)
ntt.util.updateheader(_output, 0,
{'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(_output, 0, {'FILETYPE': [11231, 'fringing frame']})
return _output
def fringing2(img, fmask, _interactive, _verbose=False):
# print "LOGX:: Entering `fringing2` method/function in %(__file__)s" %
# globals()
from ntt.util import delete, display_image, updateheader, readhdr, readkey3
from ntt.efoscphotredudef import searchfringe
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
import ntt
import os
import string
import re
from numpy import median, where
from pyraf import iraf
iraf.nproto(_doprint=0)
iraf.unlearn('nproto.objmasks')
if _verbose:
ver = 'yes'
else:
ver = 'no'
hdr = readhdr(img)
_filter = readkey3(hdr, 'filter')
_exptime = readkey3(hdr, 'exptime')
_date = readkey3(hdr, 'date-night')
imgout = img
maskname = ''
if not readkey3(hdr, 'FRICOR'):
if fmask:
fmask = searchfringe(img, fmask)[0]
if fmask:
print '###### use fringing mask from user ' + fmask
else:
fmask = searchfringe(img, '')[0]
if fmask:
print '###### use fringing mask from archive ' + fmask
if fmask:
imgout = re.sub('.fits', '_fr.fits', img)
_trim = readkey3(hdr, 'TRIM')
_trimmask = readkey3(readhdr(fmask), 'TRIM')
maskname = 'fmask_' + str(_date) + '_' + \
_filter + '_' + str(MJDtoday) + '.fits'
delete(maskname)
if _trim and not _trimmask:
_trim = '[' + string.split(_trim, '[')[1]
iraf.ccdred.ccdproc(fmask, output=maskname, overscan="no", trim="yes", zerocor="no",
trimsec=_trim, flatcor="no", zero="", Stdout=1)
elif _trim and _trimmask:
if _trim == _trimmask:
os.system('cp ' + str(fmask) + ' ' + str(maskname))
else:
sys.exit('ERROR: fringing correction can be applied only to UNTRIMMED images or '
'images with this trim ' + str(_trimmask))
elif not _trim and _trimmask:
sys.exit(
'ERROR: image is not trimmed while selected fringing mask is trimmed: ' + str(_trimmask))
else:
os.system('cp ' + str(fmask) + ' ' + str(maskname))
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
delete('mask_' + img)
imgcut = re.sub('.fits', '', img)
xxx = iraf.nproto.objmasks(images=imgcut, objmasks='mask_' + img, omtype='boolean',
blksize=-16, convolv='block 3 3', hsigma=5, lsigma=3, minpix=10, ngrow=2,
agrow=4., Stdout=1)
matimg = pyfits.open(img)[0].data
matmask = pyfits.open('mask_' + img)[1].data
matfrin = pyfits.open(maskname)[0].data
indices = where(matmask < 1)
scalevalue = median(
(matimg[indices] - median(matimg[indices])) / (matfrin[indices] - median(matfrin)))
delete(imgout)
iraf.imutil.imexpr(expr='a - (' + str(scalevalue) + '* (b - ' + str(median(matfrin)) + ') )', a=img,
b=maskname, output=imgout, verbose='no')
print '\n### fringing correction ..... done'
iraf.hedit(images=imgout, fields='OBJMASK', value='',
delete='yes', update='yes', verify='no')
ntt.util.updateheader(imgout, 0, {
'FRICOR': [str(scalevalue) + ' * (' + str(maskname) + ' - ' + str(median(matfrin)) + ')', '']})
ntt.util.updateheader(
imgout, 0, {'FILETYPE': [12205, 'pre-reduced image fringing corrected']})
ntt.util.updateheader(imgout, 0, {'PROV1': [readkey3(
readhdr(imgout), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader(
imgout, 0, {'TRACE1': [img, 'Originating file']})
stringa = '%7.7s * (fmask_%8s.fits - %5.5s )' % (
str(scalevalue), _date, str(median(matfrin)))
else:
print '\n### fringing mask not available for this filter'
else:
print '\n### fringing correction already applyed to this image'
return imgout, maskname
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 makeflat(lista):
# print "LOGX:: Entering `makeflat` method/function in %(__file__)s" %
# globals()
flat = ''
import datetime
import glob
import os
import ntt
from ntt.util import readhdr, readkey3, delete, name_duplicate, updateheader, correctcard
from pyraf import iraf
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
iraf.imgeom(_doprint=0)
# iraf.blkavg(_doprint=0)
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.generic(_doprint=0)
toforget = ['imgeom.blkavg', 'imutil.imarith',
'immatch.imcombine', 'noao.imred']
for t in toforget:
iraf.unlearn(t)
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
output = name_duplicate(
lista[3], 'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday), '')
if os.path.isfile(output):
answ = raw_input('file already prooduced, do again [y/[n]] ? ')
if not answ:
answ = 'n'
else:
answ = 'y'
if answ in ['yes', 'y', 'YES', 'Y', 'Yes']:
delete("temp_off.fits,temp_off_mask.fits,temp_on_mask.fits,temp_on.fits")
iraf.image.immatch.imcombine(
lista[0] + ',' + lista[7], output="temp_off.fits")
iraf.image.immatch.imcombine(
lista[1] + ',' + lista[6], output="temp_off_mask.fits")
iraf.image.immatch.imcombine(
lista[2] + ',' + lista[5], output="temp_on_mask.fits")
iraf.image.immatch.imcombine(
lista[3] + ',' + lista[4], output="temp_on.fits")
# create the bias correction for the flat-on according to the
# Lidman technique0
delete("temp_onA.fits,temp_onC.fits,temp_onB.fits,temp_onAC.fits,temp_onACB.fits,temp_onACB_2D.fits")
delete("temp_on_bias.fits")
iraf.imgeom.blkavg(
input="temp_on.fits[500:600,*]", output="temp_onA.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_on_mask.fits[500:600,*]", output="temp_onC.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_on_mask.fits[50:150,*]", output="temp_onB.fits", option="average", b1=101, b2=1)
iraf.imutil.imarith("temp_onA.fits", "-",
"temp_onC.fits", "temp_onAC.fits")
iraf.imutil.imarith("temp_onAC.fits", "+",
"temp_onB.fits", "temp_onACB.fits")
iraf.imgeom.blkrep(input="temp_onACB.fits",
output="temp_onACB_2D.fits", b1=1024, b2=1)
iraf.imutil.imarith("temp_on.fits", "-",
"temp_onACB_2D.fits", "temp_on_bias.fits")
# same as above for the flat-off
delete("temp_offA.fits,temp_offC.fits,temp_offB.fits,temp_offAC.fits,temp_offACB.fits,temp_offACB_2D.fits")
delete("temp_off_bias.fits")
iraf.imgeom.blkavg(
input="temp_off.fits[500:600,*]", output="temp_offA.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_off_mask.fits[500:600,*]", output="temp_offC.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_off_mask.fits[50:150,*]", output="temp_offB.fits", option="average", b1=101, b2=1)
iraf.imutil.imarith("temp_offA.fits", "-",
"temp_offC.fits", "temp_offAC.fits")
iraf.imutil.imarith("temp_offAC.fits", "+",
"temp_offB.fits", "temp_offACB.fits")
iraf.imgeom.blkrep(input="temp_offACB.fits",
output="temp_offACB_2D.fits", b1=1024, b2=1)
iraf.imutil.imarith("temp_off.fits", "-",
"temp_offACB_2D.fits", "temp_off_bias.fits")
# create the corrected flat-field
# output=name_duplicate("temp_on_bias.fits",'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday),'')
output = name_duplicate(
lista[3], 'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday), '')
# print lista[0],'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday)
delete(output)
iraf.imutil.imarith("temp_on_bias.fits", "-",
"temp_off_bias.fits", output)
iraf.noao.imred.generic.normalize(output) # normalize the flat-field
correctcard(output)
delete("temp_on*.fits") # delete the temporary images
delete("temp_off*.fits")
print 'flat -> ' + str(output)
else:
print 'skip redoing the flat'
return output
def makefringingmask(listimg, _output, _interactive, _combine='average', _rejection='avsigclip'):
# print "LOGX:: Entering `makefringingmask` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import readhdr, readkey3, delete, updateheader
import glob
import os
import sys
import re
import string
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imutil(_doprint=0)
iraf.nproto(_doprint=0)
iraf.proto(_doprint=0)
toforget = ['nproto.objmasks', 'proto.fixpix']
for t in toforget:
iraf.unlearn(t)
if _interactive == True:
listimg2 = []
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
if float(_exptime) >= 10:
answ = 'xxx'
while answ.lower() not in ['y', 'n', 's', 'a']:
iraf.display(img, frame=1, fill='yes')
answ = raw_input(
'use this image (yes,no,stop (not more images),all) [[y]/n/s/a] ? ')
if not answ:
answ = 'y'
if answ.lower() == 'y':
listimg2.append(img)
elif answ.lower() == 'a':
listimg2 = listimg[:]
if answ.lower() in ['a', 's']:
break
listimg = listimg2[:]
iraf.nproto.objmasks1.fitxord = 1
iraf.nproto.objmasks1.fityord = 1
hdr0 = readhdr(listimg[0])
_date = readkey3(hdr0, 'date-obs')
_filter = readkey3(hdr0, 'filter')
_exptime = readkey3(hdr0, 'exptime')
_instrume = readkey3(hdr0, 'instrume')
_ron = readkey3(hdr0, 'ron')
_gain = readkey3(hdr0, 'gain')
badpixelmask = 'bad_pixel_mask.pl'
if not os.path.isfile(badpixelmask):
os.system('cp ' + ntt.__path__[0] + '/archive/' + _instrume +
'/badpixels/badpixel_20100210.pl ' + badpixelmask)
ff = open('_listmask', 'w')
hh = open('_listobgz', 'w')
for img in listimg:
_exptime = readkey3(readhdr(img), 'exptime')
hh.write('z_' + img + '\n')
ff.write('mask_' + img + '\n')
delete('mask_' + img)
aaa = iraf.hedit(img, delete='yes', field='OBJMASK',
up='yes', verify='no', Stdout=1)
aaa = iraf.hedit(img, delete='yes', field='BPM',
up='yes', verify='no', Stdout=1)
delete('z_' + img)
iraf.imutil.imexpr(expr='(a - median(a))/' + str(_exptime),
a=img, output='z_' + img, verbose='no')
ntt.util.updateheader('z_' + img, 0, {'EXPTIME': [1, '']})
ff.close()
hh.close()
if not _output:
_output = 'fringing_' + str(_date) + '_' + str(_filter) + '.fits'
delete(_output)
print ' making mask for each frame .......'
ccc = iraf.nproto.objmasks(images='@_listobgz', objmasks='@_listmask', omtype='boolean',
blksize=-16, convolv='block 3 3', hsigma=5, lsigma=3, minpix=10, ngrow=2, agrow=4., Stdout=1)
print 'combining all frames, masking the objects .....'
iraf.imcombine('@_listobgz', output=_output, masktyp='!OBJMASK', maskval=0, combine=_combine, reject=_rejection,
scale='none', statsec='[100:800,100:800]', rdnoise='', gain='', nlow=1, nhigh=1, logfile='imcombinelog')
ntt.util.phase3header(_output)
ntt.util.updateheader(
_output, 0, {'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(_output, 0, {'FILETYPE': [11231, 'fringing frame']})
return _output
def efoscfastredu(imglist, _listsens, _listarc, _ext_trace, _dispersionline,
_cosmic, _interactive):
# print "LOGX:: Entering `efoscfastredu` method/function in %(__file__)s"
# % globals()
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
from ntt.util import readhdr, readkey3
import ntt
import numpy as np
dv = ntt.dvex()
scal = np.pi / 180.
if not _interactive:
_interactive = False
_inter = 'NO'
else:
_inter = 'YES'
from pyraf import iraf
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.ccdred(_doprint=0, Stdout=0)
iraf.twodspec(_doprint=0, Stdout=0)
iraf.longslit(_doprint=0, Stdout=0)
iraf.onedspec(_doprint=0, Stdout=0)
iraf.specred(_doprint=0, Stdout=0)
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' # not print steps
iraf.specred.verbose = 'no' # not print steps
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
_gain = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'gain')
_ron = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'ron')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
iraf.specred.dispaxi = 2
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
iraf.set(direc=ntt.__path__[0] + '/')
for img in imglist:
hdr = ntt.util.readhdr(img)
_tech = ntt.util.readkey3(hdr, 'tech')
if _tech != 'SPECTRUM':
sys.exit('error: ' + str(img) + ' is not a spectrum ')
print '\n#### image name = ' + img + '\n'
_grism0 = readkey3(hdr, 'grism')
_filter0 = readkey3(hdr, 'filter')
_slit0 = readkey3(hdr, 'slit')
_object0 = readkey3(hdr, 'object')
_date0 = readkey3(hdr, 'date-night')
setup = (_grism0, _filter0, _slit0)
_biassec0 = '[3:1010,1026:1029]'
if _grism0 == 'Gr16':
_trimsec0 = '[100:950,1:950]'
elif _grism0 == 'Gr13':
if _filter0 == 'Free':
_trimsec0 = '[100:950,1:1015]'
elif _filter0 == 'GG495':
_trimsec0 = '[100:950,208:1015]'
elif _filter0 == 'OG530':
_trimsec0 = '[100:950,300:1015]'
elif _grism0 == 'Gr11':
_trimsec0 = '[100:950,5:1015]'
else:
_trimsec0 = '[100:950,5:1015]'
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = 't' + str(_object0) + '_' + str(_date0)
for _set in setup:
nameout0 = nameout0 + '_' + _set
nameout0 = ntt.util.name_duplicate(img, nameout0, '')
timg = nameout0
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
iraf.ccdproc(timg,
output='',
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='column',
trimsec=str(_trimsec0),
biassec=_biassec0,
Stdout=1)
img = timg
if _listarc:
arcfile = ntt.util.searcharc(img, _listarc)[0]
else:
arcfile = ''
if not arcfile:
arcfile = ntt.util.searcharc(img, '')[0]
else:
iraf.ccdproc(arcfile,
output='t' + arcfile,
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='column',
trimsec=str(_trimsec0),
biassec=str(_biassec0),
Stdout=1)
arcfile = 't' + arcfile
if _cosmic:
# print cosmic rays rejection
ntt.cosmics.lacos(img,
output='',
gain=_gain,
readn=_ron,
xorder=9,
yorder=9,
sigclip=4.5,
sigfrac=0.5,
objlim=1,
verbose=True,
interactive=False)
print '\n### cosmic rays rejections ........ done '
if not arcfile:
print '\n### warning no arcfile \n exit '
else:
arcref = ntt.util.searcharc(img, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
arcref = string.split(arcref, '/')[-1]
if arcref:
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(
ntt.util.searcharc(img, '')[1] + '/database/id' +
re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(img, '')[1] +
'/database/id' + re.sub('.fits', '', arcref) +
' database/')
iraf.longslit.reidentify(
referenc=arcref,
images=arcfile,
interac=_inter,
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
overrid='yes',
step=0,
newaps='no',
nsum=5,
nlost=2,
mode='h',
verbose='no')
else:
iraf.longslit.identify(
images=arcfile,
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
nsum=10,
fwidth=7,
order=3,
mode='h')
iraf.longslit.reident(
referenc=arcfile,
images=arcfile,
interac='NO',
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
overrid='yes',
step=10,
newaps='yes',
nsum=5,
nlost=2,
mode='h',
verbose='no')
qqq = iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile),
fitname=re.sub('.fits', '', arcfile),
interac='no',
combine='yes',
databas='database',
function='legendre',
yorder=4,
logfile='logfile',
plotfil='',
mode='h')
iraf.specred.transform(input=img,
output=img,
minput='',
fitnames=re.sub('.fits', '', arcfile),
databas='database',
x1='INDEF',
x2='INDEF',
y1='INDEF',
y2='INDEF',
flux='yes',
mode='h',
logfile='logfile')
# ###################### check wavelength calibration ############
_skyfile = ntt.__path__[0] + '/standard/ident/sky_' + setup[
0] + '_' + setup[1] + '.fits'
shift = ntt.efoscspec2Ddef.skyfrom2d(img, _skyfile)
print '\n### check in wavelengh performed ...... spectrum shifted of ' + str(
shift) + ' Angstrom \n'
zro = pyfits.open(img)[0].header.get('CRVAL2')
ntt.util.updateheader(img, 0, {'CRVAL2': [zro + int(shift), '']})
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
hdrt = readhdr(img)
_ra = readkey3(hdrt, 'RA')
_dec = readkey3(hdrt, 'DEC')
_object = readkey3(hdrt, 'object')
dd = np.arccos(
np.sin(_dec * scal) * np.sin(decstd * scal) +
np.cos(_dec * scal) * np.cos(decstd * scal) * np.cos(
(_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
ntt.util.updateheader(img, 0,
{'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
else:
_type = 'obj'
print '\n### EXTRACTION USING IRAF TASK APALL \n'
result = []
if _type == 'obj':
imgex = ntt.util.extractspectrum(img, dv, _ext_trace,
_dispersionline, _interactive,
_type)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(
imgex, 0, {
'PRODCATG': [
'SCIENCE.' +
readkey3(readhdr(imgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(imgex, 0, {'TRACE1': [img, '']})
result.append(imgex)
if _listsens:
sensfile = ntt.util.searchsens(img, _listsens)[0]
else:
sensfile = ''
if not sensfile:
sensfile = ntt.util.searchsens(img, '')[0]
if sensfile:
imgf = re.sub('.fits', '_f.fits', img)
_extinctdir = 'direc$standard/extinction/'
_extinction = 'extinction_lasilla.dat'
_observatory = 'lasilla'
_exptime = readkey3(hdrt, 'exptime')
_airmass = readkey3(hdrt, 'airmass')
ntt.util.delete(imgf)
iraf.specred.calibrate(input=imgex,
output=imgf,
sensiti=sensfile,
extinct='yes',
flux='yes',
ignorea='yes',
extinction=_extinctdir +
_extinction,
observatory=_observatory,
airmass=_airmass,
exptime=_exptime,
fnu='no')
hedvec = {
'SENSFUN': [
string.split(sensfile, '/')[-1],
'sensitivity function'
],
'FILETYPE':
[22208, '1D wavelength and flux calibrated spectrum '],
'SNR':
[ntt.util.StoN2(imgf, False), 'Average S/N ratio'],
'BUNIT':
['erg/cm2/s/Angstrom', 'Flux Calibration Units'],
'TRACE1': [imgex, '']
}
ntt.util.updateheader(imgf, 0, hedvec)
imgout = imgf
imgd = ntt.efoscspec1Ddef.fluxcalib2d(img, sensfile)
ntt.util.updateheader(
imgd, 0, {
'FILETYPE': [
22209,
'2D wavelength and flux calibrated spectrum '
]
})
ntt.util.updateheader(imgd, 0, {'TRACE1': [img, '']})
imgasci = re.sub('.fits', '.asci', imgout)
ntt.util.delete(imgasci)
iraf.onedspec.wspectext(imgout + '[*,1,1]',
imgasci,
header='no')
result = result + [imgout, imgd, imgasci]
else:
imgex = ntt.util.extractspectrum(img, dv, _ext_trace,
_dispersionline, _interactive,
'std')
imgout = ntt.efoscspec1Ddef.sensfunction(
imgex, 'spline3', 6, _inter)
result = result + [imgout]
for img in result:
if img[-5:] == '.fits':
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.airmass(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'quality': ['Rapid', 'Final or Rapid reduction']})
return result
def efoscfastredu(imglist, _listsens, _listarc, _ext_trace, _dispersionline, _cosmic, _interactive):
# print "LOGX:: Entering `efoscfastredu` method/function in %(__file__)s"
# % globals()
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
from ntt.util import readhdr, readkey3
import ntt
import numpy as np
dv = ntt.dvex()
scal = np.pi / 180.
if not _interactive:
_interactive = False
_inter = 'NO'
else:
_inter = 'YES'
from pyraf import iraf
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)
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' # not print steps
iraf.specred.verbose = 'no' # not print steps
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
_gain = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'gain')
_ron = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'ron')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
iraf.specred.dispaxi = 2
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
iraf.set(direc=ntt.__path__[0] + '/')
for img in imglist:
hdr = ntt.util.readhdr(img)
_tech = ntt.util.readkey3(hdr, 'tech')
if _tech != 'SPECTRUM':
sys.exit('error: ' + str(img) + ' is not a spectrum ')
print '\n#### image name = ' + img + '\n'
_grism0 = readkey3(hdr, 'grism')
_filter0 = readkey3(hdr, 'filter')
_slit0 = readkey3(hdr, 'slit')
_object0 = readkey3(hdr, 'object')
_date0 = readkey3(hdr, 'date-night')
setup = (_grism0, _filter0, _slit0)
_biassec0 = '[3:1010,1026:1029]'
if _grism0 == 'Gr16':
_trimsec0 = '[100:950,1:950]'
elif _grism0 == 'Gr13':
if _filter0 == 'Free':
_trimsec0 = '[100:950,1:1015]'
elif _filter0 == 'GG495':
_trimsec0 = '[100:950,208:1015]'
elif _filter0 == 'OG530':
_trimsec0 = '[100:950,300:1015]'
elif _grism0 == 'Gr11':
_trimsec0 = '[100:950,5:1015]'
else:
_trimsec0 = '[100:950,5:1015]'
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = 't' + str(_object0) + '_' + str(_date0)
for _set in setup:
nameout0 = nameout0 + '_' + _set
nameout0 = ntt.util.name_duplicate(img, nameout0, '')
timg = nameout0
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
iraf.ccdproc(timg, output='', overscan='no', trim='yes', zerocor="no", flatcor="no", readaxi='column',
trimsec=str(_trimsec0), biassec=_biassec0, Stdout=1)
img = timg
if _listarc:
arcfile = ntt.util.searcharc(img, _listarc)[0]
else:
arcfile = ''
if not arcfile:
arcfile = ntt.util.searcharc(img, '')[0]
else:
iraf.ccdproc(arcfile, output='t' + arcfile, overscan='no', trim='yes', zerocor="no", flatcor="no",
readaxi='column', trimsec=str(_trimsec0), biassec=str(_biassec0), Stdout=1)
arcfile = 't' + arcfile
if _cosmic:
# print cosmic rays rejection
ntt.cosmics.lacos(img, output='', gain=_gain, readn=_ron, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5,
objlim=1, verbose=True, interactive=False)
print '\n### cosmic rays rejections ........ done '
if not arcfile:
print '\n### warning no arcfile \n exit '
else:
arcref = ntt.util.searcharc(img, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
arcref = string.split(arcref, '/')[-1]
if arcref:
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(ntt.util.searcharc(img, '')[1] + '/database/id' + re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(img, '')[1] + '/database/id' + re.sub('.fits', '',
arcref) + ' database/')
iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac=_inter, section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', overrid='yes', step=0,
newaps='no', nsum=5, nlost=2, mode='h', verbose='no')
else:
iraf.longslit.identify(images=arcfile, section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', nsum=10, fwidth=7,
order=3, mode='h')
iraf.longslit.reident(referenc=arcfile, images=arcfile, interac='NO', section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', overrid='yes', step=10,
newaps='yes', nsum=5, nlost=2, mode='h', verbose='no')
qqq = iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile), fitname=re.sub('.fits', '', arcfile),
interac='no', combine='yes', databas='database',
function='legendre', yorder=4, logfile='logfile', plotfil='', mode='h')
iraf.specred.transform(input=img, output=img, minput='', fitnames=re.sub('.fits', '', arcfile),
databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
# ###################### check wavelength calibration ############
_skyfile = ntt.__path__[
0] + '/standard/ident/sky_' + setup[0] + '_' + setup[1] + '.fits'
shift = ntt.efoscspec2Ddef.skyfrom2d(img, _skyfile)
print '\n### check in wavelengh performed ...... spectrum shifted of ' + str(shift) + ' Angstrom \n'
zro = pyfits.open(img)[0].header.get('CRVAL2')
ntt.util.updateheader(img, 0, {'CRVAL2': [zro + int(shift), '']})
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
hdrt = readhdr(img)
_ra = readkey3(hdrt, 'RA')
_dec = readkey3(hdrt, 'DEC')
_object = readkey3(hdrt, 'object')
dd = np.arccos(np.sin(_dec * scal) * np.sin(decstd * scal) + np.cos(_dec * scal) *
np.cos(decstd * scal) * np.cos((_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
ntt.util.updateheader(
img, 0, {'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
else:
_type = 'obj'
print '\n### EXTRACTION USING IRAF TASK APALL \n'
result = []
if _type == 'obj':
imgex = ntt.util.extractspectrum(
img, dv, _ext_trace, _dispersionline, _interactive, _type)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(imgex, 0, {
'PRODCATG': ['SCIENCE.' + readkey3(readhdr(imgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(imgex, 0, {'TRACE1': [img, '']})
result.append(imgex)
if _listsens:
sensfile = ntt.util.searchsens(img, _listsens)[0]
else:
sensfile = ''
if not sensfile:
sensfile = ntt.util.searchsens(img, '')[0]
if sensfile:
imgf = re.sub('.fits', '_f.fits', img)
_extinctdir = 'direc$standard/extinction/'
_extinction = 'extinction_lasilla.dat'
_observatory = 'lasilla'
_exptime = readkey3(hdrt, 'exptime')
_airmass = readkey3(hdrt, 'airmass')
ntt.util.delete(imgf)
iraf.specred.calibrate(input=imgex, output=imgf, sensiti=sensfile, extinct='yes',
flux='yes', ignorea='yes', extinction=_extinctdir + _extinction,
observatory=_observatory, airmass=_airmass, exptime=_exptime,
fnu='no')
hedvec = {'SENSFUN': [string.split(sensfile, '/')[-1], 'sensitivity function'],
'FILETYPE': [22208, '1D wavelength and flux calibrated spectrum '],
'SNR': [ntt.util.StoN2(imgf, False), 'Average S/N ratio'],
'BUNIT': ['erg/cm2/s/Angstrom', 'Flux Calibration Units'], 'TRACE1': [imgex, '']}
ntt.util.updateheader(imgf, 0, hedvec)
imgout = imgf
imgd = ntt.efoscspec1Ddef.fluxcalib2d(img, sensfile)
ntt.util.updateheader(
imgd, 0, {'FILETYPE': [22209, '2D wavelength and flux calibrated spectrum ']})
ntt.util.updateheader(imgd, 0, {'TRACE1': [img, '']})
imgasci = re.sub('.fits', '.asci', imgout)
ntt.util.delete(imgasci)
iraf.onedspec.wspectext(
imgout + '[*,1,1]', imgasci, header='no')
result = result + [imgout, imgd, imgasci]
else:
imgex = ntt.util.extractspectrum(
img, dv, _ext_trace, _dispersionline, _interactive, 'std')
imgout = ntt.efoscspec1Ddef.sensfunction(
imgex, 'spline3', 6, _inter)
result = result + [imgout]
for img in result:
if img[-5:] == '.fits':
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.airmass(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'quality': ['Rapid', 'Final or Rapid reduction']})
return result