def del_license(fitsfile, keys):
"""Delete license information from FITS"""
try:
for key in keys:
pyfits.delval(fitsfile, key)
except KeyError:
print("License information not found.", file=sys.stderr)
def headerRenameKey2(self, key, newkey):
for filename in self._matchingFiles:
self._logger.notice('rename %s: %s= %s' % (filename, key, newkey))
try:
value = pyfits.getval(filename, key)
pyfits.setval(filename, newkey, value)
pyfits.delval(filename, key)
except:
pass
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('trm/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
#diagnostic()
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
# print ('---hdu.data---')
# print (hdu[0].data)
w=WCS(f)
# print ('-----w-----')
# print(w)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
# print('-----npix-----')
# print(npix)
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# print('-----lam-----')
# print(lam)
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
#print ('-----specs-----')
#print (specs)
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=-2e-16)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd, comment='average of multiple exposures')
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet,comment='sum of multiple exposures')
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam,comment='average of multiple exposures')
print "avg AIRMASS = " + str(avgam)
# update this to used avg jd midpoint of all exposures?
print "barycentric velocity correction (km/s) = ",
iraf.bcvcorr(spectra=combfile,keytime='UTC-OBS',keywhen='mid',
obslong="339:11:16.8",obslat="-32:22:46.2",obsalt='1798',obsname='saao',
savebcv='yes',savejd='yes',printmode=2)
pyfits.setval(combfile,'UTMID',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'GJDN',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'HJDN',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'BCV',comment='added by RVSAO task BCVCORR (km/s)')
pyfits.setval(combfile,'HCV',comment='added by RVSAO task BCVCORR (km/s)')
iraf.dopcor(input=combfile,output='',redshift=-iraf.bcvcorr.bcv,isvelocity='yes',
add='no',dispersion='yes',flux='no',verbose='yes')
pyfits.setval(combfile,'DOPCOR01',comment='barycentric velocity correction applied')
iraf.cd('..')
def headerDeleteKey(self, key):
for filename in self._matchingFiles:
self._logger.notice('delete %s: %s' % (filename, key))
pyfits.delval(filename, key)
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('flx/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
w = WCS(f)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=1e-19)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GRATING','GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd)
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet)
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam)
print "avg AIRMASS = " + str(avgam)
iraf.cd('..')
return specs
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('flx/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
w = WCS(f)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=-1e-17)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd)
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet)
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam)
print "avg AIRMASS = " + str(avgam)
iraf.cd('..')
return specs
