def reduce(self, image, pref="gs", bias="no", gscrrej="no", crspec="no", \
qecorr="no", flat="no", fixpix="yes", vardq="no", biasPath="", \
flatPath="", refimPath="", imPath="", bpmPath="gmos$data/chipgaps.dat", \
logPath="", verbose="yes", frameNo=1):
print "RUNNING GSREDUCE"
# stash task settings
self.stashSettings('gsreduce', outpref=pref, fl_bias=bias, \
bias=biasPath, fl_flat=flat, flatim=flatPath, fl_qecorr=qecorr, \
qe_refim=refimPath, fl_gscrrej=gscrrej, fl_crspec=crspec, \
fl_fixpix=fixpix, fl_vardq=vardq, bpm=bpmPath, logfile=logPath)
# next delete previous copies of the output (should they exist)
iraf.imdel("g" + image)
iraf.imdel(pref + image)
# run gsreduce and view output
iraf.gsreduce(image, outpref=pref, fl_bias=bias, fl_gscrrej=gscrrej, \
fl_crspec=crspec, fl_qecorr=qecorr, fl_flat=flat, fl_fixpix=fixpix, \
fl_vardq=vardq, bias=biasPath, flatim=flatPath, qe_refim=refimPath, \
rawpath=imPath, bpm=bpmPath, logfile=logPath, verbose=verbose)
image = pref + image
self.viewIm(image, frameNo=frameNo)
return
def wavesol(arcfiles, rawpath):
for f in arcfiles:
binning = get_binning(f, rawpath)
iraf.unlearn(iraf.gsreduce)
if dobias:
bias_filename = "bias{binning}".format(binning=binning)
else:
bias_filename = ''
iraf.gsreduce('@' + f, outimages=f[:-4], rawpath=rawpath,
fl_flat=False, bias=bias_filename, fl_bias=dobias,
fl_fixpix=False, fl_over=dooverscan, fl_cut=False, fl_gmosaic=True,
fl_gsappwave=True, fl_oversize=False, fl_vardq=dodq)
# determine wavelength calibration -- 1d and 2d
iraf.unlearn(iraf.gswavelength)
iraf.gswavelength(f[:-4], fl_inter='yes', fl_addfeat=False, fwidth=15.0, low_reject=2.0,
high_reject=2.0, step=10, nsum=10, gsigma=2.0,
cradius=16.0, match=-6, order=7, fitcxord=7,
fitcyord=7)
if do_qecorr:
# Make an extra random copy so that gqecorr works. Stupid Gemini.
iraf.cp(f[:-4]+'.fits', f[:-4]+'.arc.fits')
# transform the CuAr spectrum, for checking that the transformation is OK
# output spectrum has prefix t
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=f[:-4])
def scireduce(scifiles, rawpath):
for f in scifiles:
setupname = getsetupname(f)
# gsreduce subtracts bias and mosaics detectors
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages=f[:-4]+'.mef', rawpath=rawpath, bias="bias",
fl_over=dooverscan, fl_fixpix='no', fl_flat=False,
fl_gmosaic=False, fl_cut=False, fl_gsappwave=False, fl_oversize=False)
if is_GS:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=setupname + '.arc.arc.fits',
corrimages=setupname +'.qe.fits', verbose=True)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4] +'.fits')
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4] +'.fits')
# Flat field the image
hdu = pyfits.open(f[:-4]+'.fits', mode='update')
hdu['SCI'].data /= pyfits.getdata(setupname+'.flat.fits', extname='SCI')
hdu.flush()
hdu.close()
# Transform the data based on the arc wavelength solution
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=setupname + '.arc')
def makemasterflat(flatfiles, rawpath, plot=True):
# normalize the flat fields
for f in flatfiles:
binning = get_binning(f, rawpath)
# Use IRAF to get put the data in the right format and subtract the
# bias
# This will currently break if multiple flats are used for a single setting
iraf.unlearn(iraf.gsreduce)
if dobias:
biasfile = "bias{binning}".format(binning=binning)
else:
biasfile = ''
iraf.gsreduce('@' + f, outimages = f[:-4]+'.mef.fits',rawpath=rawpath, fl_bias=dobias,
bias=biasfile, fl_over=dooverscan, fl_flat=False, fl_gmosaic=False,
fl_fixpix=False, fl_gsappwave=False, fl_cut=False, fl_title=False,
fl_oversize=False, fl_vardq=dodq)
if do_qecorr:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=f[:-4].replace('flat', 'arc.arc.fits'),
corrimages=f[:-9] +'.qe.fits', verbose=True, fl_vardq=dodq)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
flat_hdu = fits.open(f[:-4] + '.mos.fits')
data = np.median(flat_hdu['SCI'].data, axis=0)
chip_edges = get_chipedges(data)
x = np.arange(len(data), dtype=np.float)
x /= x.max()
y = data / np.median(data)
fitme_x = x[chip_edges[0][0]:chip_edges[0][1]]
fitme_x = np.append(fitme_x, x[chip_edges[1][0]:chip_edges[1][1]])
fitme_x = np.append(fitme_x, x[chip_edges[2][0]:chip_edges[2][1]])
fitme_y = y[chip_edges[0][0]:chip_edges[0][1]]
fitme_y = np.append(fitme_y, y[chip_edges[1][0]:chip_edges[1][1]])
fitme_y = np.append(fitme_y, y[chip_edges[2][0]:chip_edges[2][1]])
fit = pfm.pffit(fitme_x, fitme_y, 21, 7, robust=True,
M=sm.robust.norms.AndrewWave())
if plot:
pyplot.ion()
pyplot.clf()
pyplot.plot(x, y)
pyplot.plot(x, pfm.pfcalc(fit, x))
_junk = raw_input('Press enter to continue')
flat_hdu['SCI'].data /= pfm.pfcalc(fit, x) * np.median(data)
flat_hdu.writeto(f[:-4] + '.fits')
def makemasterflat(flatfiles, rawpath, plot=True):
# normalize the flat fields
for f in flatfiles:
# Use IRAF to get put the data in the right format and subtract the
# bias
# This will currently break if multiple flats are used for a single setting
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages = f[:-4]+'.mef.fits',rawpath=rawpath,
bias="bias", fl_over=dooverscan, fl_flat=False, fl_gmosaic=False,
fl_fixpix=False, fl_gsappwave=False, fl_cut=False, fl_title=False,
fl_oversize=False)
if is_GS:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=f[:-4].replace('flat', 'arc.arc.fits'),
corrimages=f[:-9] +'.qe.fits', verbose=True)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4]+'.mos.fits')
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4]+'.mos.fits')
flat_hdu = pyfits.open(f[:-4] + '.mos.fits')
data = np.median(flat_hdu['SCI'].data, axis=0)
chip_edges = get_chipedges(data)
x = np.arange(len(data), dtype=np.float)
x /= x.max()
y = data / np.median(data)
fitme_x = x[chip_edges[0][0]:chip_edges[0][1]]
fitme_x = np.append(fitme_x, x[chip_edges[1][0]:chip_edges[1][1]])
fitme_x = np.append(fitme_x, x[chip_edges[2][0]:chip_edges[2][1]])
fitme_y = y[chip_edges[0][0]:chip_edges[0][1]]
fitme_y = np.append(fitme_y, y[chip_edges[1][0]:chip_edges[1][1]])
fitme_y = np.append(fitme_y, y[chip_edges[2][0]:chip_edges[2][1]])
fit = pfm.pffit(fitme_x, fitme_y, 15, 7, robust=True,
M=sm.robust.norms.AndrewWave())
if plot:
from matplotlib import pyplot
pyplot.ion()
pyplot.clf()
pyplot.plot(x, y)
pyplot.plot(x, pfm.pfcalc(fit, x))
_junk = raw_input('Press enter to continue')
flat_hdu['SCI'].data /= pfm.pfcalc(fit, x) * np.median(data)
flat_hdu.writeto(f[:-4] + '.fits')
def wavesol(arcfiles, rawpath):
for f in arcfiles:
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages=f[:-4], rawpath=rawpath,
fl_flat=False, bias="bias",
fl_fixpix=False, fl_over=dooverscan, fl_cut=False, fl_gmosaic=True,
fl_gsappwave=True, fl_oversize=False)
# determine wavelength calibration -- 1d and 2d
iraf.unlearn(iraf.gswavelength)
iraf.gswavelength(f[:-4], fl_inter='yes', fwidth=15.0, low_reject=2.0,
high_reject=2.0, step=10, nsum=10, gsigma=2.0,
cradius=16.0, match=-6, order=7, fitcxord=7,
fitcyord=7)
if is_GS:
# Make an extra random copy so that gqecorr works. Stupid Gemini.
shutil.copy(f[:-4]+'.fits', f[:-4]+'.arc.fits')
# transform the CuAr spectrum, for checking that the transformation is OK
# output spectrum has prefix t
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=f[:-4])
def scireduce(scifiles, rawpath):
for f in scifiles:
binning = get_binning(f, rawpath)
setupname = getsetupname(f)
if dobias:
bias_filename = "bias{binning}".format(binning=binning)
else:
bias_filename = ''
# gsreduce subtracts bias and mosaics detectors
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages=f[:-4]+'.mef', rawpath=rawpath, bias=bias_filename, fl_bias=dobias,
fl_over=dooverscan, fl_fixpix='no', fl_flat=False, fl_gmosaic=False, fl_cut=False,
fl_gsappwave=False, fl_oversize=False, fl_vardq=dodq)
if do_qecorr:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True, fl_vardq=dodq,
refimages=setupname + '.arc.arc.fits', corrimages=setupname +'.qe.fits', verbose=True)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4] +'.fits', fl_vardq=dodq, fl_clean=False)
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4] +'.fits', fl_vardq=dodq, fl_clean=False)
# Flat field the image
hdu = fits.open(f[:-4]+'.fits', mode='update')
hdu['SCI'].data /= fits.getdata(setupname+'.flat.fits', extname='SCI')
hdu.flush()
hdu.close()
# Transform the data based on the arc wavelength solution
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=setupname + '.arc', fl_vardq=dodq)
iraf.gsflat(inflats='@flatb600_525_3.txt',
specflat='b600_525_norm_flat_3.fits',
order=20,
fl_over=yes,
fl_bias=no,
fl_inter=no,
fl_detec=yes,
fl_seprows=no)
#log?
l.write('flats=yes')
#Reduce Science Arc and Standard Star Images
iraf.gsreduce(inimages='@object_b600_520_1.txt',
flatim='b600_520_norm_flat_1.fits',
fl_bias=no,
fl_crspec=yes)
iraf.gsreduce(inimages='@object_b600_520_2.txt',
flatim='b600_520_norm_flat_2.fits',
fl_bias=no,
fl_crspec=yes)
iraf.gsreduce(inimages='@object_b600_520_3.txt',
flatim='b600_520_norm_flat_3.fits',
fl_bias=no,
fl_crspec=no)
iraf.gsreduce(inimages='@object_b600_525_1.txt',
flatim='b600_525_norm_flat_1.fits',
fl_bias=no,
fl_crspec=yes)
iraf.gsreduce(inimages='@object_b600_525_2.txt',
bias=rawdir + bias,
fl_over='no',
fl_vardq=vardq,
fl_fulldq=vardq)
iraf.gsflat(rawdir + flat_star,
specflat='flat_star.fits',
rawpath=rawdir,
bias=rawdir + bias_star,
fl_over='no',
fl_vardq=vardq,
fl_fulldq=vardq)
# Reduce the arc image
iraf.gsreduce(rawdir + arc,
rawpath=rawdir,
bias=rawdir + bias,
fl_fixpix='yes',
flat='flat.fits',
fl_over='no')
iraf.gsreduce(rawdir + arc_star,
rawpath=rawdir,
bias=rawdir + bias_star,
fl_fixpix='yes',
flat='flat_star.fits',
fl_over='no')
# Fit wavelength solution
iraf.gswavelength('gs' + arc, fl_inter='no')
iraf.gswavelength('gs' + arc_star, fl_inter='no')
# Reduce standard star
iraf.gsreduce(rawdir + star,
fl_detec=True, ovs_flinter=False, fl_vardq=False, fl_bias=False, Stdout=1)
log_iraf_result(flat_result)
# Create a mosaic of the master flat
logger.info("Creating master flat mosaic..")
mosaic_result = iraf.gmosaic("master_flat.fits", outpref="mosaic_", Stdout=1)
log_iraf_result(mosaic_result)
# Reduce the science frame
logger.info("Reducing science frame(s)..")
for object_filename in object_filenames:
logger.info("Reducing {}".format(object_filename))
reduce_science_result = iraf.gsreduce("cg{0}".format(object_filename),
fl_inter=False, fl_over=False, fl_trim=True, fl_dark=False,
fl_flat=True, flatim="mosaic_master_flat.fits", fl_gmosaic=True,
fl_fixpix=True, fl_bias=False, fl_cut=True, fl_gsappwave=True,
ovs_flinter=False, fl_vardq=False, yoffset=5.0, Stdout=1)
log_iraf_result(reduce_science_result)
# Reduce the arc frames
logger.info("Reducing arc frames..")
for filename in arc_filenames:
reduce_arc_result = iraf.gsreduce("g{0}".format(filename),
fl_over=False, fl_trim=True, fl_bias=False, fl_dark=False,
fl_flat=False, fl_cut=True, fl_gsappwave=True, yoffset=5.0, Stdout=1)
log_iraf_result(reduce_arc_result)
logger.info("Running gswavelength..")
#gswavelength_result = iraf.gswavelength("gsg{0}".format(filename),
fl_seprows=no)
iraf.gsflat(inflats='@flatr400_2.txt',
specflat='r400_norm_flat_2.fits',
fl_bias=no,
fl_inter=no,
fl_detec=yes,
fl_seprows=no)
#log?
l.write('flats=yes')
#Reduce Science Arc and Standard Star Images
iraf.gsreduce(inimages='@object_r400_1.txt',
flatim='r400_norm_flat_1.fits',
fl_bias=no)
iraf.gsreduce(inimages='@CuAr_r400_1.txt',
flatim='r400_norm_flat_1.fits',
fl_bias=no)
iraf.gsreduce(inimages='@object_b600_1.txt',
flatim='b600_norm_flat_1.fits',
fl_bias=no)
iraf.gsreduce(inimages='@CuAr_b600_1.txt',
flatim='b600_norm_flat_1.fits',
fl_bias=no)
iraf.gsreduce(inimages='@object_r400_2.txt',
flatim='r400_norm_flat_2.fits',
fl_bias=no)
iraf.gsreduce(inimages='@object_b600_2.txt',
flatim='b600_norm_flat_2.fits',
**flat_kwds)
# Reduce arcs.
arcs = association[association["obstype"] == "ARC"]
for arc in arcs:
path = os.path.basename(arc["path"])
iraf.unlearn("gireduce")
iraf.unlearn("gsflat")
iraf.unlearn("gemextn")
iraf.gsreduce(path,
bias="master_bias.fits",
fl_fixpix=False,
fl_flat=False,
fl_oversize=False)
# Do wavelength calibration on arc frames.
iraf.unlearn("gswavelength")
iraf.gswavelength("gs{}".format(path), **gswavelength_kwds)
# Reduce all science frames.
science_frames = association[(association["obsclass"] == "science") \
+ ((association["obstype"] == "OBJECT") * (association["obsclass"] == "partnerCal"))]
# Load template spectrum.
template_disp, template_flux, _ = np.loadtxt("Atlas.Arcturus.372_926nm.txt",
skiprows=1).T
template_disp *= 10.0
