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 make_qecorrection(arcfiles):
for f in arcfiles:
if is_GS:
#read in the arcfile name
with open(f) as txtfile:
arcimage = txtfile.readline()
# Strip off the newline character
arcimage = 'g' + arcimage.split('\n')[0]
if not os.path.exists(f[:-8] +'.qe.fits'):
iraf.gqecorr(arcimage, refimages=f[:-4]+'.arc.fits', fl_correct=False, fl_keep=True,
corrimages=f[:-8] +'.qe.fits', verbose=True)
def correctQE(self, inIm, **kwargs):
print "\nCORRECTING QE IN " + inIm
# delete previous correction data and QE-corrected exposure
iraf.imdelete("q" + inIm)
iraf.imdelete("qecorr" + kwargs["refimages"])
iraf.gqecorr(inIm, **kwargs)
return
def make_qecorrection(arcfiles):
for f in arcfiles:
if do_qecorr:
#read in the arcfile name
with open(f) as txtfile:
arcimage = txtfile.readline()
# Strip off the newline character
arcimage = 'g' + arcimage.split('\n')[0]
if not os.path.exists(f[:-8] +'.qe.fits'):
iraf.gqecorr(arcimage, refimages=f[:-4]+'.arc.fits', fl_correct=False, fl_keep=True,
corrimages=f[:-8] +'.qe.fits', verbose=True, fl_vardq=dodq)
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.unlearn('gfextract')
iraf.unlearn('gfdisplay')
# ---------- QE correction and extraction of the science ---------- #
flat0 = iraf.type(ic.lst_flat, Stdout=1)[0].strip()
response = flat0+'_resp'
ref_flat0 = 'eqbrg'+flat0
arc0 = iraf.type(ic.lst_arc, Stdout=1)[0].strip()
iraf.imdelete('qxbrg@'+ic.lst_sci)
iraf.imdelete('eqxbrg@'+ic.lst_sci)
for sci in iraf.type(ic.lst_sci, Stdout=1):
sci = sci.strip()
iraf.gqecorr('xbrg'+sci, refimage='erg'+arc0, fl_correct='yes',
fl_vardq='yes', verbose='yes')
iraf.gfextract('qxbrg'+sci, response=response, recenter='no',
trace='no', reference=ref_flat0, weights='none',
fl_vardq='yes', line=1400)
os.system('ds9 &')
iraf.sleep(5.0)
for sci in iraf.type(ic.lst_sci, Stdout=1):
sci = sci.strip()
iraf.gfdisplay('eqxbrg'+sci, 1)
# Printing the running time
print('--- %s seconds ---' %(time.time()-start_time))
