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 reduce(self):
"""
Prepare, reduce, mosaic FITS File - currently using IRAF, to be replaced by our own constructs
"""
reduce_dir = os.path.join(self.fits.work_dir, 'reduced')
if not os.path.exists(reduce_dir):
os.mkdir(reduce_dir)
reduce_fname = os.path.join(reduce_dir,
'red-{0}'.format(self.fits.fname))
if os.path.exists(reduce_fname):
logger.warn('{0} exists - deleting')
os.system('rm {0}'.format(reduce_fname))
from pyraf import iraf
prepare_temp_fname = tempfile.NamedTemporaryFile().name
reduce_temp_fname = tempfile.NamedTemporaryFile().name
iraf.gemini()
iraf.gmos()
iraf.gprepare(self.fits.full_path,
rawpath='',
outimag=prepare_temp_fname)
iraf.gireduce(inimages=prepare_temp_fname,
outimag=reduce_temp_fname,
fl_over=True,
fl_trim=True,
fl_bias=False,
fl_dark=False,
fl_qeco=False,
fl_flat=False)
iraf.gmosaic(inimages=reduce_temp_fname, outimages=reduce_fname)
return reduce_fname
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)
"cg{0}".format(filename), Stdout=1)
log_iraf_result(cosmic_ray_result)
# Create a master flat
logger.info("Creating master flat..")
flat_result = iraf.gsflat(inflats="g{0}".format(flat_filename),
specflat="master_flat.fits", fl_over=False, fl_trim=True, fl_dark=False,
fl_fixpix=False, fl_inter=False, function="chebyshev", order=15,
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