def continuum(spec_in, spec_out, order=1, interactive=True):
"""
Continuum normalize spectra.
Parameters
----------
spec_in: str;
Name of the input spectra.
spec_out: str;
Name of the output spectra.
order: int,str;
The order of the polynomials or the number of the spline pieces.
interactive: bool;
Perform the fit interactively using the icfit commands?
"""
iraf.onedspec(_doprint=0)
iraf.onedspec.continuum.unlearn()
iraf.onedspec.continuum.input = spec_in
iraf.onedspec.continuum.output = spec_out
iraf.onedspec.continuum.order = str(order)
if not interactive:
iraf.onedspec.continuum.interactive = 'no'
iraf.onedspec.continuum(mode='h')
def scopy(spec_in, spec_out, wstart, wend):
"""
Cut a spectrum on given wavelengths.
Parameters
==========
spec_in: str;
Name of input spectrum.
sepc_out: str;
name of output spectrum.
wstart: int;
Beginning wavelength
wend: int;
Ending wavelength.
"""
iraf.onedspec(_doprint=0)
iraf.scopy.w1 = wstart
iraf.scopy.w2 = wend
iraf.scopy.input = spec_in
iraf.scopy.output = spec_out
iraf.scopy(mode='h')
def standard(imlist_name, obj, obs):
"""
standard -- Add standard stars to sensitivity file
standard input [records] output
standard wfcdbstd.ms.fits (no) "sao"
extinct = "/iraf/iraf/noao/lib/onedstds/iidscal/feige56.dat"
caldir = "/iraf/iraf/noao/lib/onedstds/iidscal/"
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.onedspec()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
iraf.standard(
input=inim,
output='s' + inim[:-5],
extinct='/iraf/iraf/noao/lib/onedstds/iidscal/feige56.dat',
observatory=obs,
caldir="/iraf/iraf/noao/lib/onedstds/iidscal/",
star_name=obj)
def run_scombine(listin, fn):
namefil = []
namelist = open(listin, "r")
for cols in (raw.strip().split() for raw in namelist):
namefil.append(cols[0])
namelist.close()
obs = []
count = -1
for i in range(len(namefil)):
if namefil[i - 1][0:2] != namefil[i][0:2]:
obs.append([])
count += 1
obs[count].append(namefil[i])
spec = []
for i in range(len(obs[0])):
spec.append([])
for j in range(len(obs)):
spec[i].append(obs[j][i])
iraf.noao()
iraf.onedspec()
for i in range(len(spec)):
temp = open('temp_list', 'w')
for j in range(len(spec[i])):
temp.write(spec[i][j] + '\n')
temp.close()
if (spec[i][0][-6] == 'L') or (spec[i][0][-6] == 'U'):
name_index = '0_' + spec[i][0][-6]
else:
name_index = spec[i][0][-6] + '_' + spec[i][0][-8]
if len(fibnum) != 0:
newname = ob_id[fibnum.index(name_index[0])] + '_' + name_index[-1]
name_index = newname
iraf.scombine('@temp_list', name_index, logfile='combine_log')
fn.append(name_index)
def thar_cal(object_b_fn_ec, object_b_fn_ec_w, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
# Check input file and reference extraction exist before proceeding:
if os.path.isfile(object_b_fn_ec) == True:
# Perform dispersion correction:
iraf.dispcor(input=object_b_fn_ec, output=object_b_fn_ec_w)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print colour.capitalize() + ' object spectrum '
print '(' + str(object_b_fn_ec) + ')'
print 'successfully wavelength calibrated in file '
print str(object_b_fn_ec_w) + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn_ec)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
def StandardTask(self, InputFile, OutputFile, FitsFolder, airmass_value, exptime_value):
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
#From the fits file determine which is the star being treated
StarName = self.StarnameFromFileName(InputFile)
#Get the corresponding Calibration file
CalibrationFile, Bandwidth, Bandsep = self.getCalibrationFile(StarName)
#Incase no output name is given, we generate one with the provided "preffix" (The defaul format is a_std_wolf.dat)
if OutputFile == None:
OutputFile = 'a_std_' + StarName
#Prepare dictionary with the configuration for the tasks
Standard_conf_Comb = self.StandardAttributes(InputFile, OutputFile, CalibrationFile, FitsFolder, airmass_value, exptime_value, Bandwidth, Bandsep)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('standard', Standard_conf_Comb)
print '--- Using the command'
print Command
#Run the task
iraf.onedspec.standard(**Standard_conf_Comb)
return OutputFile
def apall_config(
config_file,
config_default='/Users/ando/andry/research/Make_software/saltrss/apall_conf.yml'
):
config = yaml.safe_load(open(config_default))
custom_config = {}
if os.path.exists(config_file):
custom_config = yaml.safe_load(open(config_file))
config = config_merge(custom_config, config)
sections = [
'iraf.apall',
]
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.apextract.unlearn()
iraf.apall.unlearn()
iraf.apsum.unlearn()
iraf.apextract.setParam('dispaxis', 1)
for section_name in sections:
params = config[section_name].items()
for param_id in params:
eval(section_name).setParam(param_id[0], param_id[1])
return config
def classifyfast(fitsfile, program='snid'):
import floyds
import re, os, sys, string
from numpy import array, argsort
from pyraf import iraf
iraf.onedspec(_doprint=0)
imgasci = re.sub('.fits', '.asci', fitsfile)
floyds.util.delete(imgasci)
iraf.onedspec.wspectext(fitsfile + '[*,1,1]', imgasci, header='no')
if program == 'snid':
print '\n######################\nclassify with snid\n'
os.system('snid plot=0 iquery=0 inter=0 verbose=0 ' + imgasci)
f = open(re.sub('.asci', '_snid.output', imgasci), 'r')
ss = f.readlines()
f.close()
ss = ss[ss.index('### type fraction/redshift/age ###\n') + 2:ss.index(
'### rlap-ordered template listings ###\n') - 2]
bb = {}
for i in ss:
if string.split(i)[0] in ['Ia', 'Ia-norm', 'Ia-91T', 'Ia-91bg', 'Ia-csm', 'Ia-pec', 'Ib', 'Ib-norm',
'Ib-pec', 'IIb', 'Ic', 'Ic-norm', 'Ic-pec', 'Ic-broad', 'II', 'II-pec', 'IIn',
'IIP', 'IIL', 'NotSN', 'AGN', 'GAL', 'LBV']:
bb[string.split(i)[0]] = {'all': string.split(i)[1:], 'frac': string.split(i)[2],
'phase': string.split(i)[6], 'red': string.split(i)[3]}
_type, _frac, _phase = [], [], []
for ii in argsort(array([bb[i]['frac'] for i in bb.keys()], float))[::-1]:
_type.append(bb.keys()[ii])
_frac.append(float(bb[bb.keys()[ii]]['frac']))
_phase.append(float(bb[bb.keys()[ii]]['phase']))
elif program == 'superfit':
print 'classifiy with suprfit'
elif program == 'gelato':
print 'gelato'
else:
print 'warning: program not found'
trigger = False
if _type[0] not in ['AGN', 'NotSN', 'Gal']:
if _phase[0] <= 0:
trigger = True
else:
if 'pec' in _type[0]:
trigger = True
elif _type[0] in ['Ia-csm']:
trigger = True
print '\n#########################\n'
print '\n Type % phase (most probable)'
print '%7s\t%7s\t%7s' % (str(_type[0]), str(_frac[0]), str(_phase[0]))
print '\n Type % phase (second most probable)'
print '%7s\t%7s\t%7s' % (str(_type[1]), str(_frac[1]), str(_phase[1]))
if trigger:
print '\n##################\n INTERESTING SN !!!!\n ACTIVATE FOLLOW-UP WITH FULL LCOGT NETWORK !!!!\n\n'
else:
print '\n##################\n BORING SN ...... \n\n'
# print '\n We report that a spectrum of '+fitsfile+' was obtained robotically on Aug XX with the FLOYDS spectrograph '+
# ' at "Faulkes Telescope XXX". The spectrum (range 320-1000 nm) shows it to be a SN Ia roughly one week before maximum light, and is consistent with the host galaxy (CGCG 425-26) redshift of z=0.027. Classification was performed via supernova spectrum cross correlation using SNID (Blondin & Tonry, 2007, ApJ, 666, 1024).'
return _type, _frac, _phase
def classifyfast(fitsfile, program='snid'):
import floyds
import re, os, sys, string
from numpy import array, argsort
from pyraf import iraf
iraf.onedspec(_doprint=0)
imgasci = re.sub('.fits', '.asci', fitsfile)
floyds.util.delete(imgasci)
iraf.onedspec.wspectext(fitsfile + '[*,1,1]', imgasci, header='no')
if program == 'snid':
print '\n######################\nclassify with snid\n'
os.system('snid plot=0 iquery=0 inter=0 verbose=0 ' + imgasci)
f = open(re.sub('.asci', '_snid.output', imgasci), 'r')
ss = f.readlines()
f.close()
ss = ss[ss.index('### type fraction/redshift/age ###\n') + 2:ss.index(
'### rlap-ordered template listings ###\n') - 2]
bb = {}
for i in ss:
if string.split(i)[0] in ['Ia', 'Ia-norm', 'Ia-91T', 'Ia-91bg', 'Ia-csm', 'Ia-pec', 'Ib', 'Ib-norm',
'Ib-pec', 'IIb', 'Ic', 'Ic-norm', 'Ic-pec', 'Ic-broad', 'II', 'II-pec', 'IIn',
'IIP', 'IIL', 'NotSN', 'AGN', 'GAL', 'LBV']:
bb[string.split(i)[0]] = {'all': string.split(i)[1:], 'frac': string.split(i)[2],
'phase': string.split(i)[6], 'red': string.split(i)[3]}
_type, _frac, _phase = [], [], []
for ii in argsort(array([bb[i]['frac'] for i in bb.keys()], float))[::-1]:
_type.append(bb.keys()[ii])
_frac.append(float(bb[bb.keys()[ii]]['frac']))
_phase.append(float(bb[bb.keys()[ii]]['phase']))
elif program == 'superfit':
print 'classifiy with suprfit'
elif program == 'gelato':
print 'gelato'
else:
print 'warning: program not found'
trigger = False
if _type[0] not in ['AGN', 'NotSN', 'Gal']:
if _phase[0] <= 0:
trigger = True
else:
if 'pec' in _type[0]:
trigger = True
elif _type[0] in ['Ia-csm']:
trigger = True
print '\n#########################\n'
print '\n Type % phase (most probable)'
print '%7s\t%7s\t%7s' % (str(_type[0]), str(_frac[0]), str(_phase[0]))
print '\n Type % phase (second most probable)'
print '%7s\t%7s\t%7s' % (str(_type[1]), str(_frac[1]), str(_phase[1]))
if trigger:
print '\n##################\n INTERESTING SN !!!!\n ACTIVATE FOLLOW-UP WITH FULL LCOGT NETWORK !!!!\n\n'
else:
print '\n##################\n BORING SN ...... \n\n'
# print '\n We report that a spectrum of '+fitsfile+' was obtained robotically on Aug XX with the FLOYDS spectrograph '+
# ' at "Faulkes Telescope XXX". The spectrum (range 320-1000 nm) shows it to be a SN Ia roughly one week before maximum light, and is consistent with the host galaxy (CGCG 425-26) redshift of z=0.027. Classification was performed via supernova spectrum cross correlation using SNID (Blondin & Tonry, 2007, ApJ, 666, 1024).'
return _type, _frac, _phase
def spectraresolution3(img0, ww=25):
import pyfits
import os, string, re, sys
import lickshane
from numpy import arange, mean, compress, array, median
from numpy import interp as ninterp
from pyraf import iraf
iraf.onedspec(_doprint=0)
img = re.sub('arc_', '', img0)
data, hdr = pyfits.getdata(img0, 0, header=True)
crvals = lickshane.util.readkey3(hdr, 'CRVAL1')
cds = lickshane.util.readkey3(hdr, 'CD1_1')
xx = arange(len(data))
yy = data
aa = crvals + (xx) * cds
maxtab, mintab = lickshane.util.peakdet(yy, median(yy) * 10)
if len(maxtab) <= 0:
maxtab, mintab = lickshane.util.peakdet(yy, median(yy))
if len(maxtab) <= 0:
lines = []
else:
lines0, b = zip(*maxtab)
lines = crvals + (array(lines0)) * cds
if len(lines) > 0:
lines = compress((aa[0] < array(lines)) & (array(lines) < aa[-1]),
array(lines))
cursor = ''
yym = ninterp(lines - ww, aa, yy)
yyp = ninterp(lines + ww, aa, yy)
for i in range(0, len(lines)):
cursor = cursor + str(lines[i] - ww) + ' ' + str(
yym[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(
yyp[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 q\n'
ff = open('_cursor', 'w')
ff.write(cursor)
ff.close()
from pyraf import iraf
aaa = iraf.noao.onedspec.bplot(img0,
cursor='_cursor',
spec2='',
new_ima='',
overwri='yes',
Stdout=1)
fw = []
for i in aaa[1:]:
fw.append(float(string.split(string.split(i, '=')[-1], 'k')[0]))
lickshane.util.delete('_cursor')
res = (aa[0] + ((aa[-1] - aa[0]) / 2)) / mean(fw)
else:
res = 9999
return res
def normalize(fn):
iraf.noao()
iraf.onedspec()
for i in range(len(fn)):
if fn[i][-1] == 'L':
nord = '10'
else:
nord = '7'
iraf.continuum(fn[i], fn[i], order=nord, ask='no', logfile='norm_log')
def spectraresolution2(img0, ww=25):
import pyfits
import os, string, re, sys
import floyds
from numpy import arange, mean, compress, array
from numpy import interp as ninterp
from pyraf import iraf
iraf.onedspec(_doprint=0)
id = 'database/id' + re.sub('.fits', '', img0)
img = re.sub('arc_', '', img0)
data, hdr = pyfits.getdata(img0, 0, header=True)
crvals = floyds.util.readkey3(hdr, 'CRVAL1')
cds = floyds.util.readkey3(hdr, 'CD1_1')
xx = arange(len(data))
yy = data
aa = crvals + (xx) * cds
# read identified lines from id file
f = open(id, 'r')
ss = f.readlines()
f.close()
indices = [i for i, x in enumerate(ss) if "begin" in x]
if len(indices) <= 1:
dd = ss[indices[0]:len(ss)]
else:
dd = ss[indices[0]:indices[1]]
# dd=ss[indices[-1]:len(ss)]
start = [i for i, x in enumerate(dd) if "features" in x][0] + 1
stop = [i for i, x in enumerate(dd) if "function" in x][0]
ff = dd[start:stop]
lines = []
if len(ff) > 0:
for i in ff: lines.append(float(string.split(i)[2]))
print lines
lines = compress((aa[0] < array(lines)) & (array(lines) < aa[-1]), array(lines))
cursor = ''
yym = ninterp(lines - ww, aa, yy)
yyp = ninterp(lines + ww, aa, yy)
for i in range(0, len(lines)):
cursor = cursor + str(lines[i] - ww) + ' ' + str(yym[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 q\n'
ff = open('_cursor', 'w')
ff.write(cursor)
ff.close()
from pyraf import iraf
aaa = iraf.noao.onedspec.bplot(img0, cursor='_cursor', spec2='', new_ima='', overwri='yes', Stdout=1)
fw = []
for i in aaa[1:]: fw.append(float(string.split(string.split(i, '=')[-1], 'k')[0]))
floyds.util.delete('_cursor')
res = (aa[0] + ((aa[-1] - aa[0]) / 2)) / mean(fw)
else:
res = 9999
return res
def spectraresolution2(img0, ww=25):
from astropy.io import fits
import os, string, re, sys
import floyds
from numpy import arange, mean, compress, array
from numpy import interp as ninterp
from pyraf import iraf
iraf.onedspec(_doprint=0)
id = 'database/id' + re.sub('.fits', '', img0)
img = re.sub('arc_', '', img0)
data, hdr = fits.getdata(img0, 0, header=True)
crvals = floyds.util.readkey3(hdr, 'CRVAL1')
cds = floyds.util.readkey3(hdr, 'CD1_1')
xx = arange(len(data))
yy = data
aa = crvals + (xx) * cds
# read identified lines from id file
f = open(id, 'r')
ss = f.readlines()
f.close()
indices = [i for i, x in enumerate(ss) if "begin" in x]
if len(indices) <= 1:
dd = ss[indices[0]:len(ss)]
else:
dd = ss[indices[0]:indices[1]]
# dd=ss[indices[-1]:len(ss)]
start = [i for i, x in enumerate(dd) if "features" in x][0] + 1
stop = [i for i, x in enumerate(dd) if "function" in x][0]
ff = dd[start:stop]
lines = []
if len(ff) > 0:
for i in ff: lines.append(float(string.split(i)[2]))
print lines
lines = compress((aa[0] < array(lines)) & (array(lines) < aa[-1]), array(lines))
cursor = ''
yym = ninterp(lines - ww, aa, yy)
yyp = ninterp(lines + ww, aa, yy)
for i in range(0, len(lines)):
cursor = cursor + str(lines[i] - ww) + ' ' + str(yym[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 q\n'
ff = open('_cursor', 'w')
ff.write(cursor)
ff.close()
from pyraf import iraf
aaa = iraf.noao.onedspec.bplot(img0, cursor='_cursor', spec2='', new_ima='', overwri='yes', Stdout=1)
fw = []
for i in aaa[1:]: fw.append(float(string.split(string.split(i, '=')[-1], 'k')[0]))
floyds.util.delete('_cursor')
res = (aa[0] + ((aa[-1] - aa[0]) / 2)) / mean(fw)
else:
res = 9999
return res
def normalize(setup, filelist):
iraf.noao()
iraf.onedspec()
for i in (filelist):
name = setup + '/' + i
iraf.continuum(name,
name,
order=1,
ask='no',
logfile=setup + '/norm_log')
def standard_sens(infile, caldir=')_.caldir', noext=False, overwrite=False):
print('\n#############################')
print('Deriving sensitivity funciton')
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.onedspec()
sys.stdout = sys.__stdout__ # Back to the stadard output
basename = fits.getval(infile, 'FRAMEID')
std = basename + '.std'
if noext:
extinction = ''
else:
extinction = fi.filibdir + 'mkoextinct.dat'
if os.path.isfile(std) and not overwrite:
print('\t The output file of IRAF STANDARD '+\
'task already exits. '+std)
print('\t STANDARD is skipped.')
else:
if overwrite:
print('Removing ' + std)
try:
os.remove(std)
except:
pass
iraf.standard(infile, std, extinction=extinction,\
caldir=caldir, beam_sw='no', aperture='')
print('Output file of IRAF STANDARD task: ' + std)
sens = basename + '.sens.fits'
if os.path.isfile(sens) and not overwrite:
print('\t The output file of IRAF SENSFUNC task already exits. ' +
sens)
print('\t SENSFUNC is skipped.')
else:
if overwrite:
print('Removing ' + sens)
try:
os.remove(sens)
except:
pass
iraf.sensfunc(std, sens, aperture='', ignoreaps='yes',\
extinction=extinction,\
logfile='sensfunc.log')
print('Output file of IRAF SENSFUNC task: ' + sens)
return sens, True
def wavelength_calibration(targetdir):
"""
Does wavelength calibration.
Writes every fit to database so make sure it's using the correct one.
This needs to be run in object directory for database
"""
print 'Target directory is ' + targetdir
print 'Doing wavelength calibration...'
if os.getcwd() != targetdir:
print 'Warning: current working directory must be target directory!'
return None
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.unlearn('identify')
iraf.identify.setParam('images','aimcomb.fits')
iraf.identify.setParam('coordli','/home/lc585/Dropbox/IoA/WHT_Proposal_2015a/argon+xenon.dat')
iraf.identify.setParam('niterat',1)
iraf.identify.setParam('function','spline3')
iraf.identify.setParam('order',3)
iraf.identify.setParam('zwidth',200.0) # Zoom graph width in user units
iraf.identify.setParam('database','database')
iraf.identify()
# Update fits header
print '\n' '\n' '\n'
print 'Updating fits header...'
iraf.hedit.setParam('images','imcomb.ms.fits')
iraf.hedit.setParam('fields','REFSPEC1')
iraf.hedit.setParam('value','aimcomb.fits') # should be wavelength calibrated?
iraf.hedit.setParam('add','yes')
iraf.hedit.setParam('verify','yes')
iraf.hedit.setParam('show','yes')
iraf.hedit()
return None
def thar_ref(object_b_fn_ec, wave_ref, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
# iraf.echelle(_doprint=0)
parList = "wavelength_calibration_refspectra.par"
if os.path.isfile(object_b_fn_ec) == True:
if os.path.isfile(wave_ref) == True:
if os.path.isfile(parList) == True:
# Assign reference spectra to input object spectra using IRAF task refspectra:
iraf.refspectra.setParList(ParList="wavelength_calibration_refspectra.par")
iraf.refspectra(input=object_b_fn_ec, references=wave_ref)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print colour.capitalize() + ' object spectrum '
print '(' + str(object_b_fn_ec) + ') '
print 'assigned to ' + str(colour) + ' reference spectrum '
print '(' + str(wave_ref) + ') successfully. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Wavelength reference IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'ThAr reference frame '
print str(wave_ref)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn_ec)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
def sensfunc(standards, obs):
"""
sensfunc -- Determine sensitivity and extinction functions
sensfunc standards sensitivity
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.onedspec()
output_sens = "sens"
iraf.sensfunc(standards=standards,
sensitivity=output_sens,
extinct='/iraf/iraf/noao/lib/onedstds/ctioextinct.dat',
observatory=obs)
iraf.splot(output_sens)
def spectraresolution3(img0, ww=25):
import pyfits
import os, string, re, sys
import floyds
from numpy import arange, mean, compress, array, median
from numpy import interp as ninterp
from pyraf import iraf
iraf.onedspec(_doprint=0)
img = re.sub('arc_', '', img0)
data, hdr = pyfits.getdata(img0, 0, header=True)
crvals = floyds.util.readkey3(hdr, 'CRVAL1')
cds = floyds.util.readkey3(hdr, 'CD1_1')
xx = arange(len(data))
yy = data
aa = crvals + (xx) * cds
maxtab, mintab = floyds.util.peakdet(yy, median(yy) * 10)
if len(maxtab) <= 0: maxtab, mintab = floyds.util.peakdet(yy, median(yy))
if len(maxtab) <= 0:
lines = []
else:
lines0, b = zip(*maxtab)
lines = crvals + (array(lines0)) * cds
if len(lines) > 0:
lines = compress((aa[0] < array(lines)) & (array(lines) < aa[-1]), array(lines))
cursor = ''
yym = ninterp(lines - ww, aa, yy)
yyp = ninterp(lines + ww, aa, yy)
for i in range(0, len(lines)):
cursor = cursor + str(lines[i] - ww) + ' ' + str(yym[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 k\n'
cursor = cursor + str(lines[i] + ww) + ' ' + str(yyp[i]) + ' 1 q\n'
ff = open('_cursor', 'w')
ff.write(cursor)
ff.close()
from pyraf import iraf
aaa = iraf.noao.onedspec.bplot(img0, cursor='_cursor', spec2='', new_ima='', overwri='yes', Stdout=1)
fw = []
for i in aaa[1:]: fw.append(float(string.split(string.split(i, '=')[-1], 'k')[0]))
floyds.util.delete('_cursor')
res = (aa[0] + ((aa[-1] - aa[0]) / 2)) / mean(fw)
else:
res = 9999
return res
def atmofile(imgstd, imgout=''):
# print "LOGX:: Entering `atmofile` method/function in %(__file__)s" %
# globals()
from pyraf import iraf
import os
import ntt
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
if not imgout:
imgout = 'atmo_' + imgstd
os.system('rm -rf ' + imgout)
iraf.noao.onedspec.bplot(imgstd, cursor=_cursor,
spec2=imgstd, new_ima=imgout, overwri='yes')
return imgout
def get_wl_identy(filename, coordlistname):
iraf.onedspec()
print 'run get_wl_identy identify...'
print 'the input file is ' + filename
print 'implot %s' % filename
iraf.implot(image = filename)
print 'run identify...'
iraf.identify(images = filename
, section = 'middle line', database = 'database'
, coordlist = coordlistname, units = '', nsum = 10
, match = -3.0, maxfeatures = 50, zwidth = 100.0
, ftype = 'emission', fwidth = 18.0, cradius = 5.0
, threshold = 0.0, minsep = 2.0, function = 'spline3'
, order = 1, sample = '*', niterate = 0
, low_reject = 3.0, high_reject = 3.0, grow = 0.0
, autowrite = False, graphics = 'stdgraph', cursor = ''
, crval = '', cdelt = '')
def inspect(self, event):
# Pull up the current TDSS spectrum in IRAF SPLOT
####
global TDSS_fiber_indices, TDSS_fiber_index
global plate, mjd
# load packages; splot is in the onedspec package, which is in noao.
# the special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
# set/view IRAF task parameter.
#iraf.onedspec.splot.save_file = "splot_%s.log" % (root,)
# call IRAF task, and specify some parameters.
iraf.onedspec.splot('spPlate-'+str(plate)+'-'+str(mjd)+'.fits[0][*,'+str(TDSS_fiber_index+1)+']')
def run_scombine(listin, fn, setup):
namefil = []
namelist = open(listin, "r")
for cols in (raw.strip().split() for raw in namelist):
namefil.append(cols[0])
namelist.close()
obs = []
count = -1
for i in range(len(namefil)):
if namefil[i - 1][namefil[i - 1].index('/') +
1:][0] != namefil[i][namefil[i].index('/') + 1:][0]:
obs.append([])
count += 1
obs[count].append(namefil[i])
spec = []
for i in range(len(obs[0])):
spec.append([])
for j in range(len(obs)):
Flag = True
try:
val = obs[j][i]
Flag = True
except IndexError:
Flag = False
if Flag:
spec[i].append(obs[j][i])
if len(spec[i]) != len(obs):
spec.pop(i)
iraf.noao()
iraf.onedspec()
for i in range(len(spec)):
temp = open(setup + '/temp_list', 'w')
for j in range(len(spec[i])):
temp.write(spec[i][j] + '\n')
temp.close()
longn = spec[i][0]
specname = longn[(longn.index('/') +
1):][(longn[(longn.index('/') + 1):]).index('/') +
1:]
iraf.scombine('@' + setup + '/temp_list',
setup + '/' + specname,
logfile=setup + '/combine_log')
fn.append(specname)
def atmofile(imgstd, imgout=''):
from pyraf import iraf
import os
import ntt
iraf.noao(_doprint=0, Stdout=0)
iraf.onedspec(_doprint=0, Stdout=0)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
if not imgout:
imgout = 'atmo_' + imgstd
os.system('rm -rf ' + imgout)
iraf.noao.onedspec.bplot(imgstd,
cursor=_cursor,
spec2=imgstd,
new_ima=imgout,
overwri='yes')
return imgout
def calibrate(imlist_name, obs):
"""
calibrate -- Apply extinction corrections and flux calibrations
calibrate input output [records]
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.onedspec()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
#iraf.calibrate(input=inim , output='s'+inim, extinct='/iraf/iraf/noao/lib/onedstds/ctioextinct.dat', flux='yes', observatory=obs)
iraf.calibrate(input=inim,
output='s' + inim,
extinct='yes',
flux='yes',
observatory=obs)
def atmofile(imgstd, imgout=''):
# print "LOGX:: Entering `atmofile` method/function in %(__file__)s" %
# globals()
from pyraf import iraf
import os
import ntt
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
if not imgout:
imgout = 'atmo_' + imgstd
os.system('rm -rf ' + imgout)
iraf.noao.onedspec.bplot(imgstd,
cursor=_cursor,
spec2=imgstd,
new_ima=imgout,
overwri='yes')
return imgout
def test(directory='', fileTag=''):
fluxFiles = sorted(glob.glob(directory+'/*_1_Flux.fits'))
spectraCount = 0
# Load the necessary IRAF packages
iraf.onedspec()
for thisFile in fluxFiles:
# Resulting file names will use the self-reported object name
# (if any) and an index based on the alphabetical order of
# the original ("HI.xxxxx...") filename. Individual CCD files
# (ie: HI.xxxx..._[1,2,3]_Flux.fits") will be left separate.
objName = SD.GetObjectName(thisFile).strip().replace(' ','_')
starName = '{0}{1}_{2:02d}'.format(fileTag, objName, spectraCount)
spectraCount += 1
# makee 'linearize' breaks a 2-d spectrum into it's component
# orders, one order per file.
makeeCall = kMakeeLinearCall + thisFile
sub.call(makeeCall, shell=True)
orderFiles = sorted(glob.glob(directory+'/*_Flux-??.fits'))
for order in orderFiles:
orderNo = int(order[-7:-5])
if starName == '':
fileRoot = order.split('/')[-1]
orderFileName = directory+fileRoot
else:
orderFileName = directory+'/{0}_{1:02d}.fits'.format(starName, orderNo)
if orderNo>6:
u.clearFile(order)
else:
os.system('mv {0} {1}'.format(order, orderFileName))
# Continuum the orders
# if int(orderNo)<5:
# iraf.continuum(input=order, output=orderFileName, functio='chebyshev', interac='no', order=5, low_rej=3.0, high_re=3.5, niterat=10)
# u.clearFile(order)
# contFileName = directory+'/aC_'+starName+'.fits'
# ...and stack them back up.
# iraf.onedspec.scopy(input=directory+'/temp_*', output=contFileName, format='multispec', renumber="yes")
# filesToClean = glob.glob(directory+'/temp_*')
# for fn in filesToClean:
# u.clearFile(fn)
return
def extract(packnam):
obs = packnam[-6]
pack = pyfits.open(packnam)
table = pack[1].data
head = pack[0].header
setup = head['HIERARCH ESO INS EXP MODE']
fib = []
obj = []
valid_obj = []
for i in range(len(table)):
fib.append(table[i][0])
obj.append(table[i][7])
iraf.noao()
iraf.onedspec()
os.makedirs(setup + '/' + obs)
for k in range(len(fib)):
if (obj[k] != 'CALSIM') and (obj[k][0:4] != 'Grid'):
valid_obj.append(setup + '/' + obs + '/' + obj[k])
iraf.scopy(packnam + '[' + str(fib[k]) + ',*]',
setup + '/' + obs + '/' + obj[k])
return valid_obj, setup
def wspectext(spec_in, ascii_out, header=False):
"""
Convert 1D image spectra to an ascii text spectra.
Parameters
----------
spec_in: str;
Name of spectrum to be converted.
ascii_out: str;
Name of output ascii file.
header: bool;
Set header on file if True.
"""
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext.unlearn()
if header:
iraf.onedspec.wspectext.header = 'yes'
iraf.onedspec.wspectext.input = spec_in
iraf.onedspec.wspectext.output = ascii_out
iraf.onedspec.wspectext(mode='h')
def sflip(imlist_name):
"""
아크 이미지랑 사이언스 이미지랑 파장 방향이 다를때, 스펙트럼을 뒤집는다.
세로로 되어 있는 이미지는 뒤집히나마나이므로, 가로방향으로 나오는 1d spectrum을 뒤집으면 된다.
sflip -- Flip data and/or dispersion coordinates in spectra
sflip input output
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.onedspec()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
print('Flip spectrum')
iraf.sflip(input=inim,
output='r' + inim,
coord_flip='yes',
data_flip='yes')
def scopy_flux(flux_sci, flux_scopy_fits, flux_scopy_range, flux_scopy_file):
"""
Combine (average) all spectra (according to bin) in the image for a given spectral range, calculate mean flux.
This is used for 1 mag contours when plotting the velocity fields.
INPUT: FLUX_SCI, FLUX_SCOPY_FITS, FLUX_SCOPY_RANGE
OUTPUT: FLUX_SCOPY_FILE
"""
if os.path.exists(flux_scopy_file):
print('File {} already exists'.format(flux_scopy_file))
return
files_in_dir = glob.glob(flux_sci.format('*'))
assert len(files_in_dir) > 0, 'No files match {}'.format(flux_sci.format('*'))
from pyraf import iraf
iraf.noao()
iraf.onedspec()
flux_scopy_fits_i_data_mean = []
for i in range(len(files_in_dir)):
flux_sci_i = flux_sci.format(i)
flux_scopy_fits_i = flux_scopy_fits.format(i)
if not os.path.exists(flux_scopy_fits_i):
iraf.scopy(flux_sci_i, flux_scopy_fits_i, w1=flux_scopy_range[0], w2=flux_scopy_range[1])
flux_scopy_fits_i_data = fits.getdata(flux_scopy_fits_i, 0)
assert flux_scopy_fits_i_data.ndim != 0, "Scrop'd array is empty"
flux_scopy_fits_i_data_mean.append(flux_scopy_fits_i_data.mean())
np.array(flux_scopy_fits_i_data_mean).tofile(flux_scopy_file, sep='\n')
def main():
# Parse input arguments
usage = 'usage: %prog [options] targets'
p = optparse.OptionParser(usage=usage, version='v'+VERSION)
p.add_option('--debug', action='store_true', help='toggle debug messages')
p.add_option('--verbose', '-v', action='store_true', help='toggle on verbose mode')
p.add_option('--tlist', '-l', action='store', type='string', dest='tlist', help='Target list')
(options, args) = p.parse_args()
if options.debug:
options.verbose = True
print 'options: ', options
print 'args: ', args
(validTargets, invalidTarget) = getTargets(args, options.tlist)
specdict = getSpectra(validTargets)
onedspec()
for target in validTargets:
print target
for spectrum in specdict[target]:
onedspec.splot(spectrum)
def main():
description = "> Performs pre-reduction steps"
usage = "%prog \t [option] \n Recommended syntax: %prog -i -c"
parser = OptionParser(usage=usage, description=description, version="0.1")
option, args = parser.parse_args()
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
mkarc = raw_input("Make arc? ([y]/n): ")
mkflat = raw_input("Make flat? ([y]/n): ")
if len(args) > 1:
files = []
sys.argv.append('--help')
option, args = parser.parse_args()
sys.exit()
elif len(args) == 1:
files = util.readlist(args[0])
sys.exit()
else:
listfile = glob.glob('*.fits')
files_science = []
files_arc = []
files_dflat = []
#print 'checking your files ...'
for img in listfile:
_type = ''
hdr0 = util.readhdr(img)
_type = util.readkey3(hdr0, 'object')
if 'flat' in _type.lower():
files_dflat.append(img)
elif 'arc' not in _type.lower() and 'arc' not in img.lower():
files_science.append(img)
if mkarc != 'n':
mkarc_b = raw_input(
"List blue arc files to combine (.fits will be added): "
).split()
mkarc_r = raw_input(
"List red arc files to combine (.fits will be added): ").split(
)
for arc in mkarc_b:
files_arc.append(arc + '.fits')
for arc in mkarc_r:
files_arc.append(arc + '.fits')
if mkarc != 'n':
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
if instruments.blue_or_red(arcs)[0] == 'blue':
list_arc_b.append(arcs)
elif instruments.blue_or_red(arcs)[0] == 'red':
list_arc_r.append(arcs)
else:
sys.exit()
if mkflat != 'n':
list_flat_b = []
list_flat_r = []
for dflats in files_dflat:
if instruments.blue_or_red(dflats)[0] == 'blue':
list_flat_b.append(dflats)
elif instruments.blue_or_red(dflats)[0] == 'red':
list_flat_r.append(dflats)
else:
sys.exit()
# make pre_reduced if it doesn't exist
if not os.path.isdir('pre_reduced/'):
os.mkdir('pre_reduced/')
# log the existing processed files (need to verify this works if pre_reduced is empty...)
pfiles = []
new_files = []
for root, dirnames, filenames in os.walk('pre_reduced'):
for file in filenames:
if file.startswith('to'):
pfiles.append(file)
print(pfiles)
# loop over each image in pre_reduced
for img in listfile:
hdr = util.readhdr(img)
targ = util.readkey3(hdr, 'object')
# if file is not not a processed file, run the overscan+trim code
if 'to' + img not in pfiles:
# if the file is a science file, grab the name for later
if 'arc' not in targ.lower() and 'flat' not in targ.lower():
new_files.append(img)
print('Adding data for: ' + targ)
inst = instruments.blue_or_red(img)[1]
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
######################################################################
#
# JB: this chunk of code needs attention
# It seems incredibly hacky for anything but Kast...
#
# overscan
if not img.startswith('o') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
util.kastbias(img, 'pre_reduced/o' + img)
elif not img.startswith('o') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
os.system('cp ' + img + ' ' + 'pre_reduced/' + img)
# trim
if not img.startswith('t') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/o' + img,
output='pre_reduced/to' + img,
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
Stdout=1)
elif not img.startswith('t') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/' + img,
output='pre_reduced/to' + img,
overscan='yes',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
biassec=str(_biassec0),
Stdout=1)
# combine the arcs
if mkarc != 'n':
# blue arcs
if len(list_arc_b) > 0:
if len(list_arc_b) == 1:
arc_blue = list_arc_b[0]
os.system('cp ' + 'pre_reduced/to' + arc_blue + ' ' +
'pre_reduced/ARC_blue.fits')
else:
arc_str = ''
for arc in list_arc_b:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_blue.fits'):
os.remove('pre_reduced/ARC_blue.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_blue.fits')
# red arcs
if len(list_arc_r) > 0:
if len(list_arc_r) == 1:
arc_red = list_arc_r[0]
os.system('cp ' + 'pre_reduced/to' + arc_red + ' ' +
'pre_reduced/ARC_red.fits')
else:
arc_str = ''
for arc in list_arc_r:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_red.fits'):
os.remove('pre_reduced/ARC_red.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_red.fits')
# combine the flats
if mkflat != 'n':
inter = 'yes'
# blue flats
if len(list_flat_b) > 0:
br, inst = instruments.blue_or_red(list_flat_b[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_b) == 1:
# Flat_blue = 'pre_reduced/to'+ list_flat_b[0]
Flat_blue = list_flat_b[0]
else:
flat_str = ''
for flat in list_flat_b:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
#subsets = 'no'
if os.path.isfile('pre_reduced/toFlat_blue'):
os.remove('pre_reduced/toFlat_blue')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_blue',
ccdtype='',
rdnoise=3.7,
subsets='no',
process='no')
Flat_blue = 'Flat_blue.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_blue,
normaliz='pre_reduced/to' + Flat_blue,
response='pre_reduced/RESP_blue',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=60,
niterat=20,
grow=0,
graphic='stdgraph')
# red flats
if len(list_flat_r) > 0:
br, inst = instruments.blue_or_red(list_flat_r[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_r) == 1:
# Flat_red = 'pre_reduced/to' + list_flat_r[0]
Flat_red = list_flat_r[0]
else:
flat_str = ''
for flat in list_flat_r:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
if os.path.isfile('pre_reduced/toFlat_red'):
os.remove('pre_reduced/toFlat_red')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_red',
ccdtype='',
rdnoise=3.8,
subsets='yes',
process='no')
Flat_red = 'Flat_red.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_red,
normaliz='pre_reduced/to' + Flat_red,
response='pre_reduced/RESP_red',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=80,
niterat=20,
grow=0,
graphic='stdgraph')
# science files should have 't' in front now
# this just gets the base name, to prefix assumed below
if new_files is not None:
files_science = new_files
# get all the science objects for the night
science_targets = []
for obj in files_science:
hdr = util.readhdr(obj)
_type = util.readkey3(hdr, 'object')
science_targets.append(_type)
# make a dir for each sci object
science_targets = set(science_targets)
for targ in science_targets:
if not os.path.isdir('pre_reduced/' + targ + '/'):
os.mkdir('pre_reduced/' + targ + '/')
# copy the files into the obj dir
for obj in files_science:
hdr = util.readhdr(obj)
targ = util.readkey3(hdr, 'object')
if not obj.startswith('to'):
os.system('cp ' + 'pre_reduced/to' + obj + ' ' + 'pre_reduced/' +
targ + '/')
else:
os.system('cp ' + 'pre_reduced/' + obj + ' ' + 'pre_reduced/' +
targ + '/')
rawfiles = glob.glob('*.fits')
ofiles = glob.glob('pre_reduced/o' + '*.fits')
tfiles = glob.glob('pre_reduced/to' + '*.fits')
# delete raw files from the pre_reduced dir
# there shouldn't be any there though?
# maybe if the overscan isn't implemented for that detector
for img in rawfiles:
util.delete('pre_reduced/' + img)
# delete the ofiles from pre_reduced dir
for img in ofiles:
util.delete(img)
def reduce(imglist,files_arc, _cosmic, _interactive_extraction,_arc):
import string
import os
import re
import sys
os.environ["PYRAF_BETA_STATUS"] = "1"
try: from astropy.io import fits as pyfits
except: import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = ['ccdproc', 'imcopy', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'longslit.fitcoords', 'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
if util.readkey3(hdr, 'VERSION') == 'kastb':
list_arc_b.append(arcs)
elif util.readkey3(hdr, 'VERSION') == 'kastr':
list_arc_r.append(arcs)
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
asci_files = []
newlist = [[],[]]
print '\n### images to reduce :',imglist
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
if util.readkey3(hdr, 'VERSION') == 'kastb':
inst = instruments.kast_blue
elif util.readkey3(hdr, 'VERSION') == 'kastr':
inst = instruments.kast_red
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print '\n### now processing :',timg,' for -> ',inst.get('name')
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
zero_file = inst.get('archive_zero_file')
os.system('cp ' + zero_file + ' .')
zero_file = string.split(zero_file, '/')[-1]
flat_file = inst.get('archive_flat_file')
os.system('cp ' + flat_file + ' .')
flat_file = string.split(flat_file, '/')[-1]
iraf.ccdproc(timg, output='', overscan='yes', trim='yes', zerocor="no", flatcor="no", readaxi='line',
trimsec=str(_trimsec0),biassec=str(_biassec0), Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="yes", flatcor="no", readaxi='line',
zero=zero_file,order=3, Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="no", flatcor="yes", readaxi='line',
flat=flat_file, Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
print '\n### starting cosmic removal'
if _cosmic:
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array, gain=inst.get('gain'), readnoise=inst.get('read_noise'), sigclip = 4.5, sigfrac = 0.5, objlim = 1.0)
c.run(maxiter = 4)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
print '\n### cosmic removal finished'
img='cosmic_' + img
if inst.get('name') == 'kast_blue':
arcfile = list_arc_b[0]
elif inst.get('name') == 'kast_red':
arcfile = list_arc_r[0]
if not arcfile.endswith(".fits"):
arcfile=arcfile+'.fits'
if os.path.isfile(arcfile):
util.delete('t' + arcfile)
iraf.ccdproc(arcfile, output= 't' + arcfile, overscan='yes', trim='yes', zerocor="no", flatcor="no",
readaxi='line', trimsec=str(_trimsec0), biassec=str(_biassec0), Stdout=1)
arcfile = 't' + arcfile
else:
print '\n### warning no arcfile \n exit '
sys.exit()
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
iraf.specred.apall(arcfile, output='', line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.identify(images=arc_ex, section=inst.get('section'),coordli=inst.get('line_list'),function = 'spline3',order=3, mode='h')
else:
arcref = inst.get('archive_arc_extracted')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
print '\n### arc referenece : ',arcref
iraf.specred.apall(arcfile, output=arc_ex, line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.reidentify(referenc=arcref, images=arc_ex, interac='NO', section=inst.get('section'),
coordli=inst.get('line_list'), shift='INDEF', search='INDEF',
mode='h', verbose='YES', step=0,nsum=5, nlost=2, cradius=10, refit='yes',overrid='yes',newaps='no')
#print '\n### checking sky lines '
#_skyfile = inst.get('sky_file')
#shift = util.skyfrom2d(img, _skyfile,'True')
#print '\n### I found a shift of : ',shift
print '\n### extraction using apall'
result = []
hdr_image = util.readhdr(img)
_type=util.readkey3(hdr_image, 'object')
if _type.startswith("arc") or _type.startswith("dflat") or _type.startswith("Dflat") or _type.startswith("Dbias") or _type.startswith("Bias"):
print '\n### warning problem \n exit '
sys.exit()
else:
imgex = util.extractspectrum(
img, dv, inst, _interactive, 'obj')
print '\n### applying wavelength solution'
iraf.disp(inlist=imgex, reference=arc_ex)
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' .')
sensfile = string.split(sensfile, '/')[-1]
if sensfile:
print '\n### sensitivity function : ',sensfile
imgf = re.sub('.fits', '_f.fits', img)
_extinction = inst.get('extinction_file')
_observatory = inst.get('observatory')
_exptime = util.readkey3(hdr, 'EXPTIME')
_airmass = util.readkey3(hdr, 'AIRMASS')
util.delete(imgf)
dimgex='d'+imgex
iraf.specred.calibrate(input=dimgex, output=imgf, sensiti=sensfile, extinct='yes',
extinction=_extinction,flux='yes', ignorea='yes', airmass=_airmass, exptime=_exptime,
fnu='no')
imgout = imgf
imgasci = re.sub('.fits', '.asci', imgout)
errasci = re.sub('.fits', '_err.asci', imgout)
util.delete(imgasci)
iraf.onedspec.wspectext(imgout + '[*,1,1]', imgasci, header='no')
iraf.onedspec.wspectext(imgout + '[*,1,4]', errasci, header='no')
spec = np.transpose(np.genfromtxt(imgasci))
err = np.transpose(np.genfromtxt(errasci))
util.delete(errasci)
final = np.transpose([spec[0], spec[1], err[1]])
np.savetxt(imgasci, final)
result = result + [imgout, imgasci]
result = result + [imgex] + [timg]
asci_files.append(imgasci)
if not os.path.isdir(_object0 + '/'):
os.mkdir(_object0 + '/')
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
else:
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
if not _arc_identify:
util.delete(arcref)
util.delete(sensfile)
util.delete(zero_file)
util.delete(flat_file)
util.delete(arc_ex)
util.delete(arcfile)
util.delete('logfile')
util.delete(dimgex)
util.delete('cosmic_*')
print '\n### now i will merge ...'
if len(asci_files) > 1:
final = cs.combine_blue_red(asci_files[0], asci_files[1], _object0)
print '\n### final result in folder ',_object0,' is ',_object0+'_merged.asci'
return result
def combineARCSpectra(args):
import sys
import glob
import os
from pyraf import iraf
from shutil import copyfile
from operator import itemgetter
import pandas as pd
# Setup relavent IRAF tasks...
iraf.image()
iraf.imutil()
iraf.immatch()
iraf.onedspec()
# Grab list of file names from argument list
# and convert it into a python list...
#fnameList = 'WDC0442-0536.0086b.ms.fits,WDC0442-0536.0087b.ms.fits,WDC0442-0536.0088b.ms.fits'
fnameList = args.fileList
fnameList = fnameList.split(',')
# Clean up file name list to exclude any excess blank spaces...
fileNameList = []
for fname in fnameList:
fileName = fname.strip()
fileNameList.append(fileName)
# Start a new file name list, for the imsliced
# files to be imcombined...
newFileNameList = []
# Loop through the input file name list...
for fileName in fileNameList:
print fileName
# Create an output file name...
baseFileName = os.path.splitext(fileName)[0] + '.tmp'
outputFileName = baseFileName + '.fits'
# Delete the any temporary files called tempA00?.fits,
# and then run imslice on fileName...
iraf.imdel('tempA00?.fits')
iraf.imslice(fileName, 'tempA', 2)
# Delete the any temporary files called tempB00?.fits,
# and then run imslice on tempA001.fits (an output
# from the first imslice)...
iraf.imdel('tempB00?.fits')
iraf.imslice('tempA001.fits', 'tempB', 2)
# Rename the output file tempB001.fits from the second
# imslice procedure; tempB001.fits contains the 1D
# version of the target spectrum. (The other file,
# tempB002.fits, which we ignore, contains the 1D
# version of the sky background spectrum.)
os.rename('tempB001.fits', outputFileName)
# Append the output file name to the new file name list...
newFileNameList.append(outputFileName)
# Clean up unneeded temporary files...
iraf.imdel('tempA00?.fits')
iraf.imdel('tempB00?.fits')
# Print the list of file names that will be fed to imcombine...
print newFileNameList
# Here, we convert the python list into an IRAF list...
inlist = ''
for newFileName in newFileNameList:
inlist = inlist + ',' + newFileName
inlist = inlist[1:]
print inlist
# Run imcombine to create a median-combined spectrum
# and its sigma image...
medianFile = newFileNameList[0].split('.')[0] + '.median.b.ms.fits'
sigmaFile = 'sigma-' + newFileNameList[0].split(
'.')[0] + '.median.b.ms.fits'
iraf.imdel(medianFile)
iraf.imdel(sigmaFile)
iraf.imcombine(inlist,
medianFile,
combine='median',
scale='median',
sigma=sigmaFile)
# Create ASCII text file equivalents...
medianFileTxt = newFileNameList[0].split('.')[0] + '.median.b.ms.txt'
sigmaFileTxt = 'sigma-' + newFileNameList[0].split(
'.')[0] + '.median.b.ms.txt'
iraf.onedspec.wspectext(medianFile, medianFileTxt)
iraf.onedspec.wspectext(sigmaFile, sigmaFileTxt)
# Combine median and sigma text files...
df1 = pd.read_csv(medianFileTxt,
header=None,
names=['wave', 'flux'],
delim_whitespace=True)
df2 = pd.read_csv(sigmaFileTxt,
header=None,
names=['wave', 'flux_err'],
delim_whitespace=True)
df12 = df1.merge(df2, on='wave')
outputFile = newFileNameList[0].split('.')[0] + '.median.flm'
df12.to_csv(outputFile, index=False, sep=' ')
# Run imcombine to create a mean-combined spectrum
# and its sigma image...
meanFile = newFileNameList[0].split('.')[0] + '.mean.b.ms.fits'
sigmaFile = 'sigma-' + newFileNameList[0].split('.')[0] + '.mean.b.ms.fits'
iraf.imdel(meanFile)
iraf.imdel(sigmaFile)
iraf.imcombine(inlist,
meanFile,
combine='average',
scale='median',
sigma=sigmaFile)
# Create ASCII text file equivalents...
meanFileTxt = newFileNameList[0].split('.')[0] + '.mean.b.ms.txt'
sigmaFileTxt = 'sigma-' + newFileNameList[0].split(
'.')[0] + '.mean.b.ms.txt'
iraf.onedspec.wspectext(meanFile, meanFileTxt)
iraf.onedspec.wspectext(sigmaFile, sigmaFileTxt)
# Combine mean and sigma text files...
df1 = pd.read_csv(meanFileTxt,
header=None,
names=['wave', 'flux'],
delim_whitespace=True)
df2 = pd.read_csv(sigmaFileTxt,
header=None,
names=['wave', 'flux_err'],
delim_whitespace=True)
df12 = df1.merge(df2, on='wave')
outputFile = newFileNameList[0].split('.')[0] + '.mean.flm'
df12.to_csv(outputFile, index=False, sep=' ')
return 0
def load_modules():
# Define a function to load all of the modules so that they don't' import
# unless we need them
global iraf
from pyraf import iraf
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
global sys
import sys
global os
import os
global shutil
import shutil
global glob
from glob import glob
global pyfits
import pyfits
global np
import numpy as np
global lacosmicx
import lacosmicx
global interp
from scipy import interp
global signal
from scipy import signal
global ndimage
from scipy import ndimage
global interpolate
from scipy import interpolate
global WCS
from astropy.wcs import WCS
global optimize
from scipy import optimize
global ds9
import pyds9 as ds9
global GaussianProcess
from sklearn.gaussian_process import GaussianProcess
global pandas
import pandas
iraf.onedspec()
iraf.twodspec()
iraf.longslit()
iraf.apextract()
iraf.imutil()
iraf.rvsao(motd='no')
lamplist = [os.path.basename(x) for x in glob.glob(
dbpath + '/lamp_XL*fits')]
linelists = 'linelists$fear.dat'
elif teles == "LJT":
print("Settings for LJT will be used.")
lamplist = [os.path.basename(x) for x in glob.glob(
dbpath + '/lamp_LJ*fits')]
linelists = 'linelists$henear.dat'
else:
print("Error detected.")
print('Possible lamp spectrum(a) for references:\n' +
", ".join(p for p in lamplist))
refspec = str(
raw_input("Enter filename of the lamp spectrum you want to use: "))
refspec1 = refspec.strip('.fits') + '.fits'
copy2(dbpath + '/' + refspec1, CWD)
copy2(dbpath + '/id' + refspec.strip('.fits'), CWD + '/database')
iraf.onedspec()
iraf.onedspec.reidentify.unlearn()
# iraf.onedspec.reidentify.fwidth = 10
iraf.onedspec.reidentify.coordli = linelists
iraf.onedspec.reidentify(reference=refspec1,
images='af*fits')
iraf.onedspec.identify.unlearn()
iraf.onedspec.identify.fwidth = 10
iraf.onedspec.identify.coordli = linelists
iraf.onedspec.identify(images='af*fits')
print('---DONE---')
# <codecell> flat # <codecell> #create flats with different aperture widths iraf.apall(input=flat, output='flat_1', referen=flat, format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-1, upper=1, bkg='no', nsum=-10, ylevel="INDEF", llimit=-1, ulimit=1,Stdout="/dev/null") # iraf.apall(input='tmp/masterflat.fits', output='tmp/flat_2', referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-2, upper=2, bkg='no', nsum=-10, ylevel="INDEF", llimit=-2, ulimit=2,Stdout="/dev/null") # iraf.apall(input='tmp/masterflat.fits', output='tmp/flat_3', referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-3, upper=3, bkg='no', nsum=-10, ylevel="INDEF", llimit=-3, ulimit=3,Stdout="/dev/null") # <codecell> #normalize different flats iraf.noao(_doprint=0,Stdout="/dev/null") iraf.onedspec(_doprint=0,Stdout="/dev/null") iraf.continuum(input=flat, output='flat_1_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # iraf.continuum(input='tmp/flat_2', output='tmp/flat_2_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # iraf.continuum(input='tmp/flat_3', output='tmp/flat_3_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # <codecell> #combine normalized flats # iraf.images(_doprint=0,Stdout="/dev/null") # iraf.imutil(_doprint=0,Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_1_norm', op='*', operand2=0.43, result='tmp/flat_1_norm2',Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_2_norm', op='*', operand2=0.41, result='tmp/flat_2_norm2',Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_3_norm', op='*', operand2=0.16, result='tmp/flat_3_norm2',Stdout="/dev/null") # <codecell>
os.chdir(folderroot)
import numpy as np
from astropy.io import fits
from matplotlib import pyplot as plt
import shutil
import sys
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.kpnoslit(_doprint=0)
iraf.astutil(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec.longslit.dispaxis = 2
#read object keywords
for file in os.listdir(os.getcwd()):
if file.endswith('.fits'):
testfile = file
hduo = fits.open(testfile)
#name targets (science & standard)
target = hduo[0].header['OBJECT']
#target2 = 'SP0644p375'
#std = 'SP0305+261'
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
def load_modules():
# Define a function to load all of the modules so that they don't' import
# unless we need them
global iraf
from pyraf import iraf
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
global sys
import sys
global os
import os
global shutil
import shutil
global glob
from glob import glob
global pyfits
import pyfits
global np
import numpy as np
global lacosmicx
import lacosmicx
global interp
from scipy import interp
global signal
from scipy import signal
global ndimage
from scipy import ndimage
global interpolate
from scipy import interpolate
global WCS
from astropy.wcs import WCS
global optimize
from scipy import optimize
global ds9
import ds9
global GaussianProcess
from sklearn.gaussian_process import GaussianProcess
global pandas
import pandas
iraf.onedspec()
iraf.twodspec()
iraf.longslit()
iraf.apextract()
iraf.imutil()
print 'Trying /home/lemoen/'
os.chdir('/home/lemoen/')
except:
print 'We must be on corvus then... trying /home/ezietsman/'
os.chdir('/home/ezietsman/')
from pyraf import iraf
os.chdir(cd)
try:
os.remove('splot.log')
except:
pass
# load NOAO package
iraf.noao()
iraf.astutil()
iraf.onedspec()
times = []
results = []
fwhm = []
for i in range(61,395):
print i
# open spectrum and calculate continuum level near Ha line then write to cursor file
data = pf.getdata('fec2117_%04d.fits'%i)
head = pf.getheader('fec2117_%04d.fits'%i)
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
"""
import os
import shutil
import numpy as np
import pyfits as pf
from pyraf import iraf, iraffunctions
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from config import *
from run_ppxf import wavelength_array
iraf.onedspec(_doprint=0, Stdout=1)
def select_specs(specs):
specs = np.array(specs, dtype=str)
exptimes = np.zeros(len(specs))
for i, spec in enumerate(specs):
exptimes[i] = pf.getval(spec, "exptime")
index = exptimes > 100.
return specs[index].tolist()
if __name__ == "__main__":
wdir = os.path.join(home, "data/reduced")
outroot = wdir.replace("reduced", "combined")
outroot2 = wdir.replace("reduced", "single")
if not os.path.exists(outroot):
os.mkdir(outroot)
def telluric_atmo(imgstd):
# print "LOGX:: Entering `telluric_atmo` method/function in %(__file__)s"
# % globals()
import numpy as np
import ntt
from pyraf import iraf
try: import pyfits
except: from astropy.io import fits as pyfits
iraf.images(_doprint=0)
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
toforget = ['imfilter.gauss', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
_grism = ntt.util.readkey3(ntt.util.readhdr(imgstd), 'grism')
imgout = 'invers_atmo_' + imgstd
ntt.util.delete(imgout)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
iraf.noao.onedspec.bplot(imgstd, cursor=_cursor,
spec2=imgstd, new_ima=imgout, overwri='yes')
xxstd, ffstd = ntt.util.readspectrum(imgout)
if _grism in ['Gr13', 'Gr16']:
llo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
elif _grism in ['Gr11']:
llo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
if _grism in ['Gr13', 'Gr16', 'Gr11']:
_skyfileh2o = 'direc$standard/ident/ATLAS_H2O.fits'
_skyfileo2 = 'direc$standard/ident/ATLAS_O2.fits'
atlas_smooto2 = '_atlas_smoot_o2.fits'
atlas_smooth2o = '_atlas_smoot_h2o.fits'
_sigma = 200
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
iraf.imfilter.gauss(_skyfileh2o, output=atlas_smooth2o, sigma=_sigma)
iraf.imfilter.gauss(_skyfileo2, output=atlas_smooto2, sigma=_sigma)
llskyh2o, ffskyh2o = ntt.util.readspectrum(atlas_smooth2o)
llskyo2, ffskyo2 = ntt.util.readspectrum(atlas_smooto2)
ffskyo2cut = np.interp(llo2, llskyo2, ffskyo2)
ffskyh2ocut = np.interp(llh2o, llskyh2o, ffskyh2o)
_scaleh2o = []
integral_h2o = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyh2ocut = list((np.array(ffskyh2ocut) * j) + 1 - j)
diff_h2o = abs(_ffskyh2ocut - ffh2o)
integraleh2o = np.trapz(diff_h2o, llh2o)
integral_h2o.append(integraleh2o)
_scaleh2o.append(j)
_scaleo2 = []
integral_o2 = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyo2cut = list((np.array(ffskyo2cut) * j) + 1 - j)
diff_o2 = abs(_ffskyo2cut - ffo2)
integraleo2 = np.trapz(diff_o2, llo2)
integral_o2.append(integraleo2)
_scaleo2.append(j)
sh2o = _scaleh2o[np.argmin(integral_h2o)]
so2 = _scaleo2[np.argmin(integral_o2)]
telluric_features = ((np.array(ffskyh2o) * sh2o) +
1 - sh2o) + ((np.array(ffskyo2) * so2) + 1 - so2) - 1
telluric_features = np.array([1] + list(telluric_features) + [1])
llskyo2 = np.array([1000] + list(llskyo2) + [15000])
telluric_features_cut = np.interp(xxstd, llskyo2, telluric_features)
_imgout = 'atmo_' + imgstd
data1, hdr = pyfits.getdata(imgstd, 0, header=True)
data1[0] = np.array(telluric_features_cut)
data1[1] = data1[1] / data1[1]
data1[2] = data1[2] / data1[2]
data1[3] = data1[3] / data1[3]
ntt.util.delete(_imgout)
pyfits.writeto(_imgout, np.float32(data1), hdr)
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
ntt.util.delete(imgout)
else:
_imgout = ''
print '### telluric correction with model not possible '
return _imgout
def efoscspec1Dredu(files, _interactive, _ext_trace, _dispersionline, liststandard, listatmo0, _automaticex,
_verbose=False):
# print "LOGX:: Entering `efoscspec1Dredu` method/function in
# %(__file__)s" % globals()
import ntt
try: import pyfits
except: from astropy.io import fits as pyfits
import re
import string
import sys
import os
import numpy as np
os.environ["PYRAF_BETA_STATUS"] = "1"
_extinctdir = 'direc$standard/extinction/'
_extinction = 'lasilla2.txt'
_observatory = 'lasilla'
import datetime
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
dv = ntt.dvex()
scal = np.pi / 180.
_gain = ntt.util.readkey3(ntt.util.readhdr(
re.sub('\n', '', files[0])), 'gain')
_rdnoise = ntt.util.readkey3(
ntt.util.readhdr(re.sub('\n', '', files[0])), 'ron')
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
objectlist = {}
for img in files:
hdr = ntt.util.readhdr(img)
img = re.sub('\n', '', img)
ntt.util.correctcard(img)
_ra = ntt.util.readkey3(hdr, 'RA')
_dec = ntt.util.readkey3(hdr, 'DEC')
_object = ntt.util.readkey3(hdr, 'object')
_grism = ntt.util.readkey3(hdr, 'grism')
_filter = ntt.util.readkey3(hdr, 'filter')
_slit = ntt.util.readkey3(hdr, 'slit')
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'
else:
_type = 'obj'
if min(dd) < 100:
ntt.util.updateheader(
img, 0, {'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
if _type not in objectlist:
objectlist[_type] = {}
if (_grism, _filter, _slit) not in objectlist[_type]:
objectlist[_type][_grism, _filter, _slit] = [img]
else:
objectlist[_type][_grism, _filter, _slit].append(img)
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.verbose = 'no'
iraf.specred.dispaxi = 2
iraf.set(direc=ntt.__path__[0] + '/')
sens = {}
print objectlist
outputfile = []
if 'obj' in objectlist.keys():
tpe = 'obj'
elif 'stdsens' in objectlist.keys():
tpe = 'stdsens'
else:
sys.exit('error: no objects and no standards in the list')
for setup in objectlist[tpe]:
extracted = []
listatmo = []
if setup not in sens:
sens[setup] = []
if tpe == 'obj':
print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][setup], '\n'
for img in objectlist['obj'][setup]:
# hdr=readhdr(img)
print '\n\n### next object= ', img, ' ', ntt.util.readkey3(ntt.util.readhdr(img), 'object'), '\n'
if os.path.isfile(re.sub('.fits', '_ex.fits', img)):
if ntt.util.readkey3(ntt.util.readhdr(re.sub('.fits', '_ex.fits', img)), 'quality') == 'Rapid':
ntt.util.delete(re.sub('.fits', '_ex.fits', img))
imgex = ntt.util.extractspectrum(img, dv, _ext_trace, _dispersionline, _interactive, 'obj',
automaticex=_automaticex)
if not os.path.isfile(imgex):
sys.exit('### error, extraction not computed')
if not ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') != 0.0:
# if not readkey3(readhdr(imgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(imgex, _interactive)
extracted.append(imgex)
if imgex not in outputfile:
outputfile.append(imgex)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(imgex, 0, {
'PRODCATG': ['SCIENCE.' +
ntt.util.readkey3(ntt.util.readhdr(imgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(
imgex, 0, {'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' + re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '', imgex) not in outputfile:
outputfile.append(
'database/ap' + re.sub('_ex.fits', '', imgex))
print '\n### all object with this setup extracted\n'
if liststandard:
standardlist = liststandard
_type = 'stdfromdreducer'
else:
try:
standardlist = objectlist['stdsens'][setup]
_type = 'stdsens'
except:
standardlist = ''
_type = ''
if _type == 'stdfromdreducer' and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], standardlist)[0]
print '\n### using standard from reducer ' + str(_outputsens2)
elif _type not in ['stdsens', 'stdfromdreducer'] and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], '')[0]
os.system('cp ' + _outputsens2 + ' .')
_outputsens2 = string.split(_outputsens2, '/')[-1]
print '\n### no standard in the list, using standard from archive'
else:
for simg in standardlist:
print '\n### standard for setup ' + \
str(setup) + ' = ', simg, ' ', ntt.util.readkey3(
ntt.util.readhdr(simg), 'object'), '\n'
simgex = ntt.util.extractspectrum(
simg, dv, False, False, _interactive, 'std', automaticex=_automaticex)
ntt.util.updateheader(
simgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(simgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(ntt.util.readhdr(simgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(
simgex, 0, {'TRACE1': [simg, 'Originating file']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') != 0.0:
# if not readkey3(readhdr(simgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(simgex, _interactive)
atmofile = ntt.efoscspec1Ddef.telluric_atmo(
simgex) # atmo file2
ntt.util.updateheader(
atmofile, 0, {'TRACE1': [simgex, 'Originating file']})
ntt.util.updateheader(
atmofile, 0, {'FILETYPE': [21211, 'telluric correction 1D spectrum ']})
if tpe != 'obj' and atmofile not in outputfile:
outputfile.append(atmofile)
if not listatmo0:
listatmo.append(atmofile)
sens[setup].append(simgex)
if simgex not in outputfile:
outputfile.append(simgex)
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', simg)):
print '\n### extract standard frame with blocking filter to correct for second order contamination\n'
simg2 = re.sub('Free', 'GG495', simg)
simgex2 = ntt.util.extractspectrum(simg2, dv, False, False, _interactive, 'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex2, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(simgex2, 0, {
'PRODCATG': ['SCIENCE.' +
ntt.util.readkey3(
ntt.util.readhdr(simgex2), 'tech').upper(), 'Data product category']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') != 0.0:
# if not readkey3(readhdr(simgex2),'shift'):
ntt.efoscspec1Ddef.checkwavestd(
simgex2, _interactive)
ntt.util.updateheader(
simgex2, 0, {'TRACE1': [simg2, 'Originating file']})
print '\n### standard available: ', sens[setup]
if tpe == 'obj':
if len(sens[setup]) > 1:
goon = 'no'
while goon != 'yes':
stdused = raw_input(
'\n### more than one standard for this setup, which one do you want to use [' + sens[setup][
0] + '] ?')
if not stdused:
stdused = sens[setup][0]
if os.path.isfile(stdused):
goon = 'yes'
else:
stdused = sens[setup][0]
stdvec = [stdused]
else:
stdvec = sens[setup]
for stdused in stdvec:
stdusedclean = re.sub('_ex', '_clean', stdused)
ntt.util.delete(stdusedclean)
iraf.specred.sarith(
input1=stdused, op='/', input2=atmofile, output=stdusedclean, format='multispec')
_outputsens2 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean, 'spline3', 16, _interactive)
ntt.util.updateheader(_outputsens2, 0, {'FILETYPE': [
21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens2, 0, {'TRACE1': [stdused, 'Originating file']})
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', stdused)):
print '\n### compute sensitivity function of grim 13 with blocking filter ' \
'to correct for second order contamination \n'
stdused2 = re.sub('Free', 'GG495', stdused)
if not ntt.util.readkey3(ntt.util.readhdr(stdused2), 'STDNAME'):
ntt.util.updateheader(stdused2, 0, {
'STDNAME': [ntt.util.readkey3(ntt.util.readhdr(stdused), 'STDNAME'), '']})
atmofile2 = ntt.efoscspec1Ddef.telluric_atmo(
stdused2) # atmo file2
stdusedclean2 = re.sub('_ex', '_clean', stdused2)
ntt.util.delete(stdusedclean2)
iraf.specred.sarith(input1=stdused2, op='/', input2=atmofile2, output=stdusedclean2,
format='multispec')
_outputsens3 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean2, 'spline3', 16, _interactive)
ntt.util.updateheader(_outputsens3, 0, {'FILETYPE': [
21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens3, 0, {'TRACE1': [stdused2, 'Originating file']})
_outputsens2 = correctsens(_outputsens2, _outputsens3)
if _outputsens2 not in outputfile:
outputfile.append(_outputsens2)
if _outputsens2 and tpe == 'obj':
####################################################
for img in objectlist['obj'][setup]: # flux calibrate 2d images
imgd = fluxcalib2d(img, _outputsens2)
ntt.util.updateheader(
imgd, 0, {'FILETYPE': [22209, '2D wavelength and flux calibrated spectrum ']})
ntt.util.updateheader(
imgd, 0, {'TRACE1': [img, 'Originating files']})
iraf.hedit(imgd, 'PRODCATG', delete='yes',
update='yes', verify='no')
if imgd not in outputfile:
outputfile.append(imgd)
####################################################
# flux calib in the standard way
if not listatmo and listatmo0:
listatmo = listatmo0[:]
for _imgex in extracted:
_airmass = ntt.util.readkey3(
ntt.util.readhdr(_imgex), 'airmass')
_exptime = ntt.util.readkey3(
ntt.util.readhdr(_imgex), 'exptime')
_imgf = re.sub('_ex.fits', '_f.fits', _imgex)
ntt.util.delete(_imgf)
qqq = iraf.specred.calibrate(input=_imgex, output=_imgf, sensiti=_outputsens2, extinct='yes',
flux='yes',
extinction=_extinctdir + _extinction, observatory=_observatory,
airmass=_airmass, ignorea='yes', exptime=_exptime, fnu='no')
hedvec = {'SENSFUN': [_outputsens2, ''],
'FILETYPE': [22208, '1D wavelength and flux calibrated spectrum', ''],
# 'SNR':[ntt.util.StoN(_imgf,50),'Average signal to noise ratio per pixel'],
'SNR': [ntt.util.StoN2(_imgf, False), 'Average signal to noise ratio per pixel'],
'BUNIT': ['erg/cm2/s/Angstrom', 'Physical unit of array values'],
'TRACE1': [_imgex, 'Originating file'],
'ASSON1': [re.sub('_f.fits', '_2df.fits', _imgf), 'Name of associated file'],
'ASSOC1': ['ANCILLARY.2DSPECTRUM', 'Category of associated file']}
ntt.util.updateheader(_imgf, 0, hedvec)
if _imgf not in outputfile:
outputfile.append(_imgf)
if listatmo:
atmofile = ntt.util.searcharc(_imgex, listatmo)[0]
if atmofile:
_imge = re.sub('_f.fits', '_e.fits', _imgf)
ntt.util.delete(_imge)
iraf.specred.sarith(input1=_imgf, op='/', input2=atmofile, output=_imge, w1='INDEF', w2='INDEF',
format='multispec')
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,2]', output=_imge + '[*,1,2]', verbose='no')
except:
pass
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,3]', output=_imge + '[*,1,3]', verbose='no')
except:
pass
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,4]', output=_imge + '[*,1,4]', verbose='no')
except:
pass
if _imge not in outputfile:
outputfile.append(_imge)
ntt.util.updateheader(
_imge, 0, {'FILETYPE': [22210, '1D, wave, flux calib, telluric corr.']})
if atmofile not in outputfile:
outputfile.append(atmofile)
ntt.util.updateheader(
_imge, 0, {'ATMOFILE': [atmofile, '']})
ntt.util.updateheader(
_imge, 0, {'TRACE1': [_imgf, 'Originating file']})
imgin = _imge
else:
imgin = _imgf
else:
imgin = _imgf
imgasci = re.sub('.fits', '.asci', imgin)
ntt.util.delete(imgasci)
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext(
imgin + '[*,1,1]', imgasci, header='no')
if imgasci not in outputfile:
outputfile.append(imgasci)
print '\n### adding keywords for phase 3 ....... '
for img in outputfile:
if str(img)[-5:] == '.fits':
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(img, 0, {'quality': ['Final', '']})
except:
print 'Warning: ' + img + ' is not a fits file'
try:
if int(re.sub('\.', '', str(pyfits.__version__))[:2]) <= 30:
aa = 'HIERARCH '
else:
aa = ''
except:
aa = ''
imm = pyfits.open(img, mode='update')
hdr = imm[0].header
if aa + 'ESO DPR CATG' in hdr:
hdr.pop(aa + 'ESO DPR CATG')
if aa + 'ESO DPR TECH' in hdr:
hdr.pop(aa + 'ESO DPR TECH')
if aa + 'ESO DPR TYPE' in hdr:
hdr.pop(aa + 'ESO DPR TYPE')
imm.flush()
imm.close()
print outputfile
reduceddata = ntt.rangedata(outputfile)
f = open('logfile_spec1d_' + str(reduceddata) +
'_' + str(datenow) + '.raw.list', 'w')
for img in outputfile:
try:
f.write(ntt.util.readkey3(ntt.util.readhdr(img), 'arcfile') + '\n')
except:
pass
f.close()
return outputfile, 'logfile_spec1d_' + str(reduceddata) + '_' + str(datenow) + '.raw.list'
import matplotlib.pyplot as plt from datetime import date import operator import time import traceback from lmfit import minimize, Parameters, report_fit #import cosmics iraf.noao(_doprint=0,Stdout="/dev/null") iraf.rv(_doprint=0,Stdout="/dev/null") iraf.imred(_doprint=0,Stdout="/dev/null") iraf.ccdred(_doprint=0,Stdout="/dev/null") iraf.images(_doprint=0,Stdout="/dev/null") iraf.immatch(_doprint=0,Stdout="/dev/null") iraf.onedspec(_doprint=0,Stdout="/dev/null") iraf.twodspec(_doprint=0,Stdout="/dev/null") iraf.apextract(_doprint=0,Stdout="/dev/null") iraf.imutil(_doprint=0,Stdout="/dev/null") iraf.echelle(_doprint=0,Stdout="/dev/null") iraf.astutil(_doprint=0,Stdout="/dev/null") iraf.apextract.dispaxi=1 iraf.echelle.dispaxi=1 #fixes a bug with latest versions of iraf iraf.ccdred.instrum='blank.txt' os.environ['PYRAF_BETA_STATUS'] = '1' # REFS NEW: ecEC59550 ecEC59758 ecEC59795 ecEC59842 ecEC59844 ecEC59864 ecEC59866 ecEC59881 ecEC59883 ecEC59885
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 telluric_correction(targetdir,telluricdir,stype):
"""
Removes telluric lines from 1D spectrum
Assumes both science and telluric spectrum have been extracted and
wavelength calibrated, i.e. file dimcomb.ms.fits exists in telluric
directory.
"""
print 'Target directory is ' + targetdir
if os.path.exists( os.path.join(targetdir,'dimcomb.ms.fits') ):
print "Wavelength calibrated target spectrum 'dimcomb.ms.fits' exists"
print 'Telluric directory is ' + telluricdir
if os.path.exists( os.path.join(telluricdir,'dimcomb.ms.fits') ):
print "Wavelength calibrated telluric spectrum 'dimcomb.ms.fits' exists"
print 'Generating black-body spectrum...'
print 'Telluric star type ' + stype
# from this site http://www.gemini.edu/sciops/instruments/nir/photometry/temps_colors.txt
if stype == 'A0V': bbtemp = 9480.0
if stype == 'A1V': bbtemp = 9230.0
if stype == 'A2V': bbtemp = 8810.0
if stype == 'A3V': bbtemp = 8270.0
if stype == 'A4V': bbtemp = 8200.0
if stype == 'A5V': bbtemp = 8160.0
print 'Fitting with blackbody, temperature = ' + str(bbtemp) + 'K'
iraf.noao(_doprint=0)
iraf.artdata (_doprint=0)
if os.path.exists( os.path.join(telluricdir,'blackbody.fits') ):
os.remove( os.path.join(telluricdir,'blackbody.fits') )
iraf.mk1dspec.setParam('input', os.path.join(telluricdir,'blackbody.fits') )
iraf.mk1dspec.setParam('title','blackbody')
iraf.mk1dspec.setParam('ncols',1024)
iraf.mk1dspec.setParam('wstart',13900)
iraf.mk1dspec.setParam('wend',24000)
iraf.mk1dspec.setParam('temperature',bbtemp)
iraf.mk1dspec()
print 'Generated blackbody spectrum'
# Divide telluric star spectrum by black-body
if os.path.exists( os.path.join(telluricdir,'tdimcomb.ms.fits') ):
os.remove( os.path.join(telluricdir,'tdimcomb.ms.fits') )
iraf.onedspec(_doprint=0)
"""
To divide by blackbody I want to turn off the cross-correlation - which I
think would only work if both spectra had similar features. I don't want to scale
the spectrum - which scales the airmass using Beer's Law - or shift it. If the temperature
is correct I should be able to simply divide. If not then I should change
the temperature
"""
iraf.telluric.setParam('input',os.path.join(telluricdir,'dimcomb.ms.fits') ) # List of input spectra to correct
iraf.telluric.setParam('output',os.path.join(telluricdir,'tdimcomb.ms.fits') ) # List of output corrected spectra
iraf.telluric.setParam('cal',os.path.join(telluricdir,'blackbody.fits') ) # List of telluric calibration spectra
iraf.telluric.setParam('answer','yes') # Search interactively?
iraf.telluric.setParam('xcorr', 'no') # Cross correlate for shift?
iraf.telluric.setParam('tweakrms', 'no') # Twak to minise rms?
iraf.telluric.setParam('interactive', 'yes') # Interactive?
iraf.telluric.setParam('threshold',0.0) # Threshold for calibration
iraf.telluric.setParam('offset',1) # Displayed offset between spectra
iraf.telluric.setParam('sample','15000:18000,19700:23800')
iraf.telluric.setParam('dshift',5.0)
iraf.telluric.setParam('smooth',3.0)
iraf.telluric()
"""
When your calibration spectrum has zero or negative intensity values,
you have to set the "threshold" parameter accordingly. As explained in
the help page for the TELLURIC task, you can think of the "threshold"
value as the minimum intensity value TELLURIC will accept from your
calibration spectra. Any intensity value lower than the threshold value
will be replaced by the threshold.
"""
"""
I've turned cross-correlation off, since I don't really understand it.
Tweak is on, but doesn't seem to do that much. Still not sure if it's understanding
the airmass
"""
print 'Now correcting target spectrum...'
if os.path.exists( os.path.join(targetdir,'tdimcomb+bkgd.ms.fits') ):
os.remove( os.path.join(targetdir,'tdimcomb+bkgd.ms.fits') )
iraf.telluric.setParam('input',os.path.join(targetdir,'dimcomb+bkgd.ms.fits') ) # List of input spectra to correct
iraf.telluric.setParam('output',os.path.join(targetdir,'tdimcomb+bkgd.ms.fits') ) # List of output corrected spectra
iraf.telluric.setParam('cal',os.path.join(telluricdir,'tdimcomb.ms.fits') ) # List of telluric calibration spectra
iraf.telluric.setParam('answer','yes') # Search interactively?
iraf.telluric.setParam('threshold',0.0)
iraf.telluric.setParam('xcorr', 'no') # Cross correlate for shift?
iraf.telluric.setParam('tweakrms', 'yes') # Tweak to minise rms?
iraf.telluric.setParam('interactive', 'yes') # Interactive?
iraf.telluric.setParam('offset',6) # Displayed offset between spectra
hdulist = fits.open(os.path.join(targetdir,'imcomb.ms.fits'))
hdr = hdulist[0].header
hdulist.close()
iraf.telluric.setParam('airmass',hdr['AIRMASS'])
iraf.telluric()
return None
def reduce(imglist, files_arc, files_flat, _cosmic, _interactive_extraction,
_arc):
import string
import os
import re
import sys
import pdb
os.environ["PYRAF_BETA_STATUS"] = "1"
try:
from astropy.io import fits as pyfits
except:
import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = [
'ccdproc', 'imcopy', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'longslit.fitcoords',
'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
br, inst = instruments.blue_or_red(arcs)
if br == 'blue':
list_arc_b.append(arcs)
elif br == 'red':
list_arc_r.append(arcs)
else:
errStr = '{} '.format(str(util.readkey3(hdr, 'VERSION')))
errStr += 'not in database'
print(errStr)
sys.exit()
asci_files = []
newlist = [[], []]
print('\n### images to reduce :', imglist)
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
elif len(newlist[0]) < 1:
newlist = newlist[1:]
else:
sides = raw_input("Reduce which side? ([both]/b/r): ")
if sides == 'b':
newlist = newlist[:-1]
elif sides == 'r':
newlist = newlist[1:]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
br, inst = instruments.blue_or_red(imgs[0])
if br == 'blue':
flat_file = '../RESP_blue'
elif br == 'red':
flat_file = '../RESP_red'
else:
errStr = 'Not in intrument list'
print(errStr)
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print('\n### now processing :', timg, ' for -> ', inst.get('name'))
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
# should just do this by hand
iraf.ccdproc(timg,
output='',
overscan='no',
trim='no',
zerocor="no",
flatcor="yes",
readaxi='line',
flat=flat_file,
Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
if _cosmic:
print('\n### starting cosmic removal')
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array,
gain=inst.get('gain'),
readnoise=inst.get('read_noise'),
sigclip=5,
sigfrac=0.5,
objlim=2.0)
c.run(maxiter=5)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
img = 'cosmic_' + img
print('\n### cosmic removal finished')
else:
print(
'\n### No cosmic removal, saving normalized image for inspection???'
)
if inst.get('arm') == 'blue' and len(list_arc_b) > 0:
arcfile = list_arc_b[0]
elif inst.get('arm') == 'red' and len(list_arc_r) > 0:
arcfile = list_arc_r[0]
else:
arcfile = None
if arcfile is not None and not arcfile.endswith(".fits"):
arcfile = arcfile + '.fits'
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
os.system('cp ' + arcfile + ' .')
arcfile = string.split(arcfile, '/')[-1]
arc_ex = re.sub('.fits', '.ms.fits', arcfile)
arcref = inst.get('archive_arc_extracted')
arcref_img = string.split(arcref, '/')[-1]
arcref_img = arcref_img.replace('.ms.fits', '')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
aperture = inst.get('archive_arc_aperture')
os.system('cp ' + aperture + ' ./database')
print('\n### arcfile : ', arcfile)
print('\n### arcfile extraction : ', arc_ex)
print('\n### arc reference : ', arcref)
# read for some meta data to get the row right
tmpHDU = pyfits.open(arcfile)
header = tmpHDU[0].header
try:
spatialBin = int(header['binning'].split(',')[0])
except KeyError:
spatialBin = 1
apLine = 700 // spatialBin
iraf.specred.apall(arcfile,
output=arc_ex,
ref=arcref_img,
line=apLine,
nsum=10,
interactive='no',
extract='yes',
find='yes',
nfind=1,
format='multispec',
trace='no',
back='no',
recen='no')
iraf.longslit.reidentify(referenc=arcref,
images=arc_ex,
interac='NO',
section=inst.get('section'),
coordli=inst.get('line_list'),
shift='INDEF',
search='INDEF',
mode='h',
verbose='YES',
step=0,
nsum=5,
nlost=2,
cradius=10,
refit='yes',
overrid='yes',
newaps='no')
print('\n### extraction using apall')
result = []
hdr_image = util.readhdr(img)
_type = util.readkey3(hdr_image, 'object')
if (_type.startswith("arc") or _type.startswith("dflat")
or _type.startswith("Dflat") or _type.startswith("Dbias")
or _type.startswith("Bias")):
print('\n### warning problem \n exit ')
sys.exit()
else:
imgex = util.extractspectrum(img, dv, inst, _interactive, 'obj')
print('\n### applying wavelength solution')
print(arc_ex)
iraf.disp(inlist=imgex, reference=arc_ex)
result = result + [imgex] + [timg]
# asci_files.append(imgasci)
if not os.path.isdir(_object0 + '_ex/'):
os.mkdir(_object0 + '_ex/')
if not _arc_identify:
util.delete(arcref)
else:
util.delete(arcfile)
util.delete(arc_ex)
util.delete(img)
util.delete(imgex)
util.delete(arcref)
util.delete('logfile')
#if _cosmic:
#util.delete(img[7:])
#util.delete("cosmic_*")
os.system('mv ' + 'd' + imgex + ' ' + _object0 + '_ex/')
use_sens = raw_input('Use archival flux calibration? [y]/n ')
if use_sens != 'no':
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' ' + _object0 + '_ex/')
bstarfile = inst.get('archive_bstar')
os.system('cp ' + bstarfile + ' ' + _object0 + '_ex/')
return result
def efoscspec1Dredu(files,
_interactive,
_ext_trace,
_dispersionline,
liststandard,
listatmo0,
_automaticex,
_verbose=False):
# print "LOGX:: Entering `efoscspec1Dredu` method/function in
# %(__file__)s" % globals()
import ntt
try:
import pyfits
except:
from astropy.io import fits as pyfits
import re
import string
import sys
import os
import numpy as np
os.environ["PYRAF_BETA_STATUS"] = "1"
_extinctdir = 'direc$standard/extinction/'
_extinction = 'lasilla2.txt'
_observatory = 'lasilla'
import datetime
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
dv = ntt.dvex()
scal = np.pi / 180.
_gain = ntt.util.readkey3(ntt.util.readhdr(re.sub('\n', '', files[0])),
'gain')
_rdnoise = ntt.util.readkey3(ntt.util.readhdr(re.sub('\n', '', files[0])),
'ron')
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
objectlist = {}
for img in files:
hdr = ntt.util.readhdr(img)
img = re.sub('\n', '', img)
ntt.util.correctcard(img)
_ra = ntt.util.readkey3(hdr, 'RA')
_dec = ntt.util.readkey3(hdr, 'DEC')
_object = ntt.util.readkey3(hdr, 'object')
_grism = ntt.util.readkey3(hdr, 'grism')
_filter = ntt.util.readkey3(hdr, 'filter')
_slit = ntt.util.readkey3(hdr, 'slit')
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'
else:
_type = 'obj'
if min(dd) < 100:
ntt.util.updateheader(img, 0,
{'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
if _type not in objectlist:
objectlist[_type] = {}
if (_grism, _filter, _slit) not in objectlist[_type]:
objectlist[_type][_grism, _filter, _slit] = [img]
else:
objectlist[_type][_grism, _filter, _slit].append(img)
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.verbose = 'no'
iraf.specred.dispaxi = 2
iraf.set(direc=ntt.__path__[0] + '/')
sens = {}
print objectlist
outputfile = []
if 'obj' in objectlist.keys():
tpe = 'obj'
elif 'stdsens' in objectlist.keys():
tpe = 'stdsens'
else:
sys.exit('error: no objects and no standards in the list')
for setup in objectlist[tpe]:
extracted = []
listatmo = []
if setup not in sens:
sens[setup] = []
if tpe == 'obj':
print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][
setup], '\n'
for img in objectlist['obj'][setup]:
# hdr=readhdr(img)
print '\n\n### next object= ', img, ' ', ntt.util.readkey3(
ntt.util.readhdr(img), 'object'), '\n'
if os.path.isfile(re.sub('.fits', '_ex.fits', img)):
if ntt.util.readkey3(
ntt.util.readhdr(re.sub('.fits', '_ex.fits', img)),
'quality') == 'Rapid':
ntt.util.delete(re.sub('.fits', '_ex.fits', img))
imgex = ntt.util.extractspectrum(img,
dv,
_ext_trace,
_dispersionline,
_interactive,
'obj',
automaticex=_automaticex)
if not os.path.isfile(imgex):
sys.exit('### error, extraction not computed')
if not ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') != 0.0:
# if not readkey3(readhdr(imgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(imgex, _interactive)
extracted.append(imgex)
if imgex not in outputfile:
outputfile.append(imgex)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(
imgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(imgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(imgex, 0,
{'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' +
re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '',
imgex) not in outputfile:
outputfile.append('database/ap' +
re.sub('_ex.fits', '', imgex))
print '\n### all object with this setup extracted\n'
if liststandard:
standardlist = liststandard
_type = 'stdfromdreducer'
else:
try:
standardlist = objectlist['stdsens'][setup]
_type = 'stdsens'
except:
standardlist = ''
_type = ''
if _type == 'stdfromdreducer' and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], standardlist)[0]
print '\n### using standard from reducer ' + str(_outputsens2)
elif _type not in ['stdsens', 'stdfromdreducer'
] and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], '')[0]
os.system('cp ' + _outputsens2 + ' .')
_outputsens2 = string.split(_outputsens2, '/')[-1]
print '\n### no standard in the list, using standard from archive'
else:
for simg in standardlist:
print '\n### standard for setup ' + \
str(setup) + ' = ', simg, ' ', ntt.util.readkey3(
ntt.util.readhdr(simg), 'object'), '\n'
simgex = ntt.util.extractspectrum(simg,
dv,
False,
False,
_interactive,
'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(
simgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(simgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(simgex, 0,
{'TRACE1': [simg, 'Originating file']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') != 0.0:
# if not readkey3(readhdr(simgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(simgex, _interactive)
atmofile = ntt.efoscspec1Ddef.telluric_atmo(
simgex) # atmo file2
ntt.util.updateheader(atmofile, 0,
{'TRACE1': [simgex, 'Originating file']})
ntt.util.updateheader(
atmofile, 0,
{'FILETYPE': [21211, 'telluric correction 1D spectrum ']})
if tpe != 'obj' and atmofile not in outputfile:
outputfile.append(atmofile)
if not listatmo0:
listatmo.append(atmofile)
sens[setup].append(simgex)
if simgex not in outputfile:
outputfile.append(simgex)
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', simg)):
print '\n### extract standard frame with blocking filter to correct for second order contamination\n'
simg2 = re.sub('Free', 'GG495', simg)
simgex2 = ntt.util.extractspectrum(
simg2,
dv,
False,
False,
_interactive,
'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex2, 0,
{'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(
simgex2, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(simgex2),
'tech').upper(),
'Data product category'
]
})
if not ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') != 0.0:
# if not readkey3(readhdr(simgex2),'shift'):
ntt.efoscspec1Ddef.checkwavestd(
simgex2, _interactive)
ntt.util.updateheader(
simgex2, 0,
{'TRACE1': [simg2, 'Originating file']})
print '\n### standard available: ', sens[setup]
if tpe == 'obj':
if len(sens[setup]) > 1:
goon = 'no'
while goon != 'yes':
stdused = raw_input(
'\n### more than one standard for this setup, which one do you want to use ['
+ sens[setup][0] + '] ?')
if not stdused:
stdused = sens[setup][0]
if os.path.isfile(stdused):
goon = 'yes'
else:
stdused = sens[setup][0]
stdvec = [stdused]
else:
stdvec = sens[setup]
for stdused in stdvec:
stdusedclean = re.sub('_ex', '_clean', stdused)
ntt.util.delete(stdusedclean)
iraf.specred.sarith(input1=stdused,
op='/',
input2=atmofile,
output=stdusedclean,
format='multispec')
_outputsens2 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean, 'spline3', 16, _interactive)
ntt.util.updateheader(
_outputsens2, 0,
{'FILETYPE': [21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens2, 0, {'TRACE1': [stdused, 'Originating file']})
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', stdused)):
print '\n### compute sensitivity function of grim 13 with blocking filter ' \
'to correct for second order contamination \n'
stdused2 = re.sub('Free', 'GG495', stdused)
if not ntt.util.readkey3(ntt.util.readhdr(stdused2),
'STDNAME'):
ntt.util.updateheader(
stdused2, 0, {
'STDNAME': [
ntt.util.readkey3(
ntt.util.readhdr(stdused),
'STDNAME'), ''
]
})
atmofile2 = ntt.efoscspec1Ddef.telluric_atmo(
stdused2) # atmo file2
stdusedclean2 = re.sub('_ex', '_clean', stdused2)
ntt.util.delete(stdusedclean2)
iraf.specred.sarith(input1=stdused2,
op='/',
input2=atmofile2,
output=stdusedclean2,
format='multispec')
_outputsens3 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean2, 'spline3', 16, _interactive)
ntt.util.updateheader(
_outputsens3, 0,
{'FILETYPE': [21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens3, 0,
{'TRACE1': [stdused2, 'Originating file']})
_outputsens2 = correctsens(_outputsens2, _outputsens3)
if _outputsens2 not in outputfile:
outputfile.append(_outputsens2)
if _outputsens2 and tpe == 'obj':
####################################################
for img in objectlist['obj'][setup]: # flux calibrate 2d images
imgd = fluxcalib2d(img, _outputsens2)
ntt.util.updateheader(
imgd, 0, {
'FILETYPE':
[22209, '2D wavelength and flux calibrated spectrum ']
})
ntt.util.updateheader(imgd, 0,
{'TRACE1': [img, 'Originating files']})
iraf.hedit(imgd,
'PRODCATG',
delete='yes',
update='yes',
verify='no')
if imgd not in outputfile:
outputfile.append(imgd)
####################################################
# flux calib in the standard way
if not listatmo and listatmo0:
listatmo = listatmo0[:]
for _imgex in extracted:
_airmass = ntt.util.readkey3(ntt.util.readhdr(_imgex),
'airmass')
_exptime = ntt.util.readkey3(ntt.util.readhdr(_imgex),
'exptime')
_imgf = re.sub('_ex.fits', '_f.fits', _imgex)
ntt.util.delete(_imgf)
qqq = iraf.specred.calibrate(input=_imgex,
output=_imgf,
sensiti=_outputsens2,
extinct='yes',
flux='yes',
extinction=_extinctdir +
_extinction,
observatory=_observatory,
airmass=_airmass,
ignorea='yes',
exptime=_exptime,
fnu='no')
hedvec = {
'SENSFUN': [_outputsens2, ''],
'FILETYPE':
[22208, '1D wavelength and flux calibrated spectrum', ''],
# 'SNR':[ntt.util.StoN(_imgf,50),'Average signal to noise ratio per pixel'],
'SNR': [
ntt.util.StoN2(_imgf, False),
'Average signal to noise ratio per pixel'
],
'BUNIT':
['erg/cm2/s/Angstrom', 'Physical unit of array values'],
'TRACE1': [_imgex, 'Originating file'],
'ASSON1': [
re.sub('_f.fits', '_2df.fits', _imgf),
'Name of associated file'
],
'ASSOC1':
['ANCILLARY.2DSPECTRUM', 'Category of associated file']
}
ntt.util.updateheader(_imgf, 0, hedvec)
if _imgf not in outputfile:
outputfile.append(_imgf)
if listatmo:
atmofile = ntt.util.searcharc(_imgex, listatmo)[0]
if atmofile:
_imge = re.sub('_f.fits', '_e.fits', _imgf)
ntt.util.delete(_imge)
iraf.specred.sarith(input1=_imgf,
op='/',
input2=atmofile,
output=_imge,
w1='INDEF',
w2='INDEF',
format='multispec')
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,2]',
output=_imge + '[*,1,2]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,3]',
output=_imge + '[*,1,3]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,4]',
output=_imge + '[*,1,4]',
verbose='no')
except:
pass
if _imge not in outputfile:
outputfile.append(_imge)
ntt.util.updateheader(
_imge, 0, {
'FILETYPE': [
22210,
'1D, wave, flux calib, telluric corr.'
]
})
if atmofile not in outputfile:
outputfile.append(atmofile)
ntt.util.updateheader(_imge, 0,
{'ATMOFILE': [atmofile, '']})
ntt.util.updateheader(
_imge, 0, {'TRACE1': [_imgf, 'Originating file']})
imgin = _imge
else:
imgin = _imgf
else:
imgin = _imgf
imgasci = re.sub('.fits', '.asci', imgin)
ntt.util.delete(imgasci)
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext(imgin + '[*,1,1]',
imgasci,
header='no')
if imgasci not in outputfile:
outputfile.append(imgasci)
print '\n### adding keywords for phase 3 ....... '
for img in outputfile:
if str(img)[-5:] == '.fits':
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(img, 0, {'quality': ['Final', '']})
except:
print 'Warning: ' + img + ' is not a fits file'
try:
if int(re.sub('\.', '', str(pyfits.__version__))[:2]) <= 30:
aa = 'HIERARCH '
else:
aa = ''
except:
aa = ''
imm = pyfits.open(img, mode='update')
hdr = imm[0].header
if aa + 'ESO DPR CATG' in hdr:
hdr.pop(aa + 'ESO DPR CATG')
if aa + 'ESO DPR TECH' in hdr:
hdr.pop(aa + 'ESO DPR TECH')
if aa + 'ESO DPR TYPE' in hdr:
hdr.pop(aa + 'ESO DPR TYPE')
imm.flush()
imm.close()
print outputfile
reduceddata = ntt.rangedata(outputfile)
f = open(
'logfile_spec1d_' + str(reduceddata) + '_' + str(datenow) +
'.raw.list', 'w')
for img in outputfile:
try:
f.write(ntt.util.readkey3(ntt.util.readhdr(img), 'arcfile') + '\n')
except:
pass
f.close()
return outputfile, 'logfile_spec1d_' + str(reduceddata) + '_' + str(
datenow) + '.raw.list'
import os
# Delete some directories/files from previous runs.
os.system("rm -rf login.cl pyraf database uparm")
os.system("mkiraf")
# Now load IRAF
import pyraf.iraf as iraf
# Load the packages we might need.
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.unlearn(iraf.apall)
# The name of the science file.
filename = 'vega_9.3narrow.fit'
extracted_filename = 'vega_9.3narrow.ms.fits'
calibrated_filename = 'vega_9.3narrow.calib.fits'
# Delete previous results.
os.system("rm "+extracted_filename+" "+calibrated_filename)
# Make sure that the dispersion axis is in the header.
def extract_spectrum(targetdir,
trace,
arcspec,
refspec,
t_nsum,
t_step,
line,
ylevel,
interactive):
"""
Extract spectrum
Must be in target directory.
"""
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
basedir = '/data/lc585/WHT_20150331/OBS/'
targetdir = os.path.join(basedir,targetdir,'Reduced')
print 'Target directory is ' + targetdir
print 'Extracting spectrum...'
if os.path.exists( os.path.join(targetdir, 'imcomb.ms.fits') ):
os.remove( os.path.join( targetdir, 'imcomb.ms.fits') )
print 'Removing file ' + os.path.join( targetdir, 'imcomb.ms.fits')
# If can't fit trace use trace from nearby object
if trace == 'no':
dest = os.path.join(targetdir,'database')
if not os.path.exists(dest):
os.makedirs(dest)
db = os.path.join(basedir,refspec,'Reduced','database','ap_data_lc585_WHT_20150331_OBS_'+refspec+'_Reduced_imcomb')
shutil.copy(db,dest)
iraf.apall.setParam('references',os.path.join(basedir,refspec,'Reduced','imcomb.fit')) # List of aperture reference images
print 'Using trace from reference spectra ' + refspec
# Since frame is averaged I think we need to scale down read nosie but gain will stay the same.
names = []
for n in os.listdir(targetdir):
if (n.endswith('.fit')) & (n.startswith('r')):
names.append(n)
nframes = float(len(names))
# Doesn't seem to work if I give it absolute path to input!
iraf.apall.setParam('input','imcomb.fit') # List of input images
iraf.apall.setParam('output','') # List of output spectra
iraf.apall.setParam('apertur','') # Apertures
iraf.apall.setParam('format','multispec') # Extracted spectra format
iraf.apall.setParam('referen','') # List of aperture reference images
iraf.apall.setParam('profile','') # List of aperture profile images
iraf.apall.setParam('interac',interactive) # Run task interactively?
iraf.apall.setParam('find','no') # Find apertures?
iraf.apall.setParam('recente','no') # Recenter apertures?
iraf.apall.setParam('resize','no') # Resize apertures?
iraf.apall.setParam('edit','yes') # Edit apertures?
iraf.apall.setParam('trace',trace) # Trace apertures?
iraf.apall.setParam('fittrac',interactive) # Fit the traced points interactively?
iraf.apall.setParam('extract','yes') # Extract spectra?
iraf.apall.setParam('extras','yes') # Extract sky, sigma, etc.?
iraf.apall.setParam('review',interactive) # Review extractions?
iraf.apall.setParam('line',line) # Dispersion line
iraf.apall.setParam('nsum',20) # Number of dispersion lines to sum or median
# DEFAULT APERTURE PARAMETERS
iraf.apall.setParam('lower',-5.) # Lower aperture limit relative to center
iraf.apall.setParam('upper',5.) # Upper aperture limit relative to center
iraf.apall.setParam('apidtab','') # Aperture ID table (optional)
# DEFAULT BACKGROUND PARAMETERS
# Background is now a constant at each wavelength
iraf.apall.setParam('b_funct','chebyshev') # Background function
iraf.apall.setParam('b_order',1) # Background function order
iraf.apall.setParam('b_sampl','-10:-6,6:10') # Background sample regions
iraf.apall.setParam('b_naver',-3) # Background average or median
iraf.apall.setParam('b_niter',2) # Background rejection iterations
iraf.apall.setParam('b_low_r',3.) # Background lower rejection sigma
iraf.apall.setParam('b_high_',3.) # Background upper rejection sigma
iraf.apall.setParam('b_grow',0.) # Background rejection growing radius
# APERTURE CENTERING PARAMETERS
iraf.apall.setParam('width',5.) # Profile centering width
iraf.apall.setParam('radius',10.) # Profile centering radius
iraf.apall.setParam('thresho',0.) # Detection threshold for profile centering
# AUTOMATIC FINDING AND ORDERING PARAMETERS
iraf.apall.setParam('nfind','') # Number of apertures to be found automatically
iraf.apall.setParam('minsep',5.) # Minimum separation between spectra
iraf.apall.setParam('maxsep',100000.) # Maximum separation between spectra
iraf.apall.setParam('order','increasing') # Order of apertures
# RECENTERING PARAMETERS
iraf.apall.setParam('aprecen','') # Apertures for recentering calculation
iraf.apall.setParam('npeaks','INDEF') # Select brightest peaks
iraf.apall.setParam('shift','yes') # Use average shift instead of recentering?
# RESIZING PARAMETERS
iraf.apall.setParam('llimit','INDEF') # Lower aperture limit relative to center
iraf.apall.setParam('ulimit','INDEF') # Upper aperture limit relative to center
iraf.apall.setParam('ylevel',0.2) # Fraction of peak or intensity for automatic widt
iraf.apall.setParam('peak','yes') # Is ylevel a fraction of the peak?
iraf.apall.setParam('bkg','yes') # Subtract background in automatic width?
iraf.apall.setParam('r_grow',0.) # Grow limits by this factor
iraf.apall.setParam('avglimi','no') # Average limits over all apertures?
# TRACING PARAMETERS
iraf.apall.setParam('t_nsum',20) # Number of dispersion lines to sum
iraf.apall.setParam('t_step', 20) # Tracing step
iraf.apall.setParam('t_nlost',3) # Number of consecutive times profile is lost befo
iraf.apall.setParam('t_funct','spline3') # Trace fitting function
iraf.apall.setParam('t_order',2) # Trace fitting function order
iraf.apall.setParam('t_sampl','*') # Trace sample regions
iraf.apall.setParam('t_naver',1) # Trace average or median
iraf.apall.setParam('t_niter',2) # Trace rejection iterations
iraf.apall.setParam('t_low_r',3.) # Trace lower rejection sigma
iraf.apall.setParam('t_high_',3.) # Trace upper rejection sigma
iraf.apall.setParam('t_grow',0.) # Trace rejection growing radius
# EXTRACTION PARAMETERS
iraf.apall.setParam('backgro','none') # Background to subtract
iraf.apall.setParam('skybox',1) # Box car smoothing length for sky
iraf.apall.setParam('weights','variance') # Extraction weights (none|variance)
iraf.apall.setParam('pfit','fit1d') # Profile fitting type (fit1d|fit2d)
iraf.apall.setParam('clean','yes') # Detect and replace bad pixels?
iraf.apall.setParam('saturat',300000.) # Saturation level
# iraf.apall.setParam('readnoi',17.0)
iraf.apall.setParam('readnoi',17./np.sqrt(nframes)) # Read out noise sigma (photons)
iraf.apall.setParam('gain',4.) # Photon gain (photons/data number)
iraf.apall.setParam('lsigma',4.) # Lower rejection threshold
iraf.apall.setParam('usigma',4.) # Upper rejection threshold
iraf.apall.setParam('nsubaps',1) # Number of subapertures per aperture
iraf.apall.setParam('mode','q') # h = hidden, q = query, l = learn
iraf.apall()
# Now extract arc through same aperture for wavelength calibration
print '\n' '\n' '\n'
print 'Extracting Arc through same aperture...'
if os.path.exists( os.path.join(targetdir,'aimcomb.fits')):
os.remove( os.path.join(targetdir, 'aimcomb.fits') )
print 'Removing file ' + os.path.join(targetdir, 'aimcomb.fits')
arcspec = os.path.join(basedir,arcspec)
iraf.apall.setParam('input', arcspec)
iraf.apall.setParam('output', 'aimcomb')
iraf.apall.setParam('references', 'imcomb.fit' )
iraf.apall.setParam('recenter','no')
iraf.apall.setParam('trace','no')
iraf.apall.setParam('background','no')
iraf.apall.setParam('interactive','no')
iraf.apall()
if os.path.exists( os.path.join(targetdir, 'imcomb+bkgd.ms.fits') ):
os.remove( os.path.join( targetdir, 'imcomb+bkgd.ms.fits') )
print 'Removing file ' + os.path.join( targetdir, 'imcomb+bkgd.ms.fits')
iraf.apall.setParam('input','imcomb+bkgd.fit') # List of input images
iraf.apall.setParam('output','') # List of output spectra
iraf.apall.setParam('referen','imcomb.fit') # List of aperture reference images
iraf.apall.setParam('interac','yes') # Run task interactively?
iraf.apall.setParam('find','yes') # Find apertures?
iraf.apall.setParam('recenter','no') # Recenter apertures?
iraf.apall.setParam('resize','no') # Resize apertures?
iraf.apall.setParam('edit','yes') # Edit apertures?
iraf.apall.setParam('trace','no') # Trace apertures?
iraf.apall.setParam('fittrac',interactive) # Fit the traced points interactively?
iraf.apall.setParam('extract','yes') # Extract spectra?
iraf.apall.setParam('extras','yes') # Extract sky, sigma, etc.?
iraf.apall.setParam('review','yes') # Review extractions?
# DEFAULT BACKGROUND PARAMETERS
# Background is now a constant at each wavelength
iraf.apall.setParam('b_funct','chebyshev') # Background function
iraf.apall.setParam('b_order',1) # Background function order
iraf.apall.setParam('b_sampl','-10:-6,6:10') # Background sample regions
iraf.apall.setParam('b_naver',-3) # Background average or median
iraf.apall.setParam('b_niter',2) # Background rejection iterations
iraf.apall.setParam('b_low_r',3.) # Background lower rejection sigma
iraf.apall.setParam('b_high_',3.) # Background upper rejection sigma
iraf.apall.setParam('b_grow',0.) # Background rejection growing radius
# EXTRACTION PARAMETERS
# before i wasn't dividing by the square root of the frames, but surely this must be true if I'm taking the average
iraf.apall.setParam('backgro','median') # Background to subtract
iraf.apall.setParam('skybox',1) # Box car smoothing length for sky
iraf.apall.setParam('weights','variance') # Extraction weights (none|variance)
iraf.apall.setParam('pfit','fit1d') # Profile fitting type (fit1d|fit2d)
iraf.apall.setParam('clean','yes') # Detect and replace bad pixels?
iraf.apall.setParam('saturat',300000.) # Saturation level
# iraf.apall.setParam('readnoi',17.0)
iraf.apall.setParam('readnoi',17.0/np.sqrt(nframes)) # Read out noise sigma (photons)
iraf.apall.setParam('gain',4.) # Photon gain (photons/data number)
iraf.apall.setParam('lsigma',4.) # Lower rejection threshold
iraf.apall.setParam('usigma',4.) # Upper rejection threshold
iraf.apall.setParam('nsubaps',1) # Number of subapertures per aperture
iraf.apall()
hdulist = fits.open(os.path.join(targetdir, 'imcomb+bkgd.ms.fits'))
sigma = hdulist[0].data[3,0,:]
hdulist.close()
hdulist = fits.open(os.path.join(targetdir, 'imcomb.ms.fits'), mode='update')
hdulist[0].data[2,0,:] = sigma
hdulist.flush()
hdulist.close()
return None
