def reduce_twighlight(args):
D = Distortion(op.join(args.configdir, 'DeformerDefaults',
'mastertrace_twi_027_L.dist'))
for spec in args.specid:
spec_ind_twi = np.where(args.twi_df['Specid'] == spec)[0]
for amp in config.Amps:
amp_ind_twi = np.where(args.twi_df['Amp'] == amp)[0]
twi_sel = np.intersect1d(spec_ind_twi, amp_ind_twi)
for ind in twi_sel:
if args.debug:
print("Working on Cal for %s, %s" %(spec, amp))
twi1 = Amplifier(args.twi_df['Files'][ind],
args.twi_df['Output'][ind],
calpath=args.twi_df['Output'][ind],
debug=True, dark_mult=0.0,
darkpath=args.darkdir, biaspath=args.biasdir,
virusconfig=args.configdir,
specname=args.specname[amp],
use_pixelflat=(args.pixelflats<1),
init_lims=args.wvl_dict[amp],
check_fibermodel=True, check_wave=True,
fsize=args.fsize,
fibmodel_nbins=args.fibmodel_bins,
sigma=args.fibmodel_sig,
power=args.fibmodel_pow,
use_trace_ref=args.use_trace_ref,
default_fib = args.default_fib,
wave_nbins = args.wave_nbins)
#twi1.load_fibers()
twi1.get_fiber_to_fiber()
twi1.sky_subtraction()
twi2 = Amplifier(args.twi_df['Files'][ind].replace(amp,
config.Amp_dict[amp][0]),
args.twi_df['Output'][ind],
calpath=args.twi_df['Output'][ind],
debug=True, dark_mult=0.0,
darkpath=args.darkdir, biaspath=args.biasdir,
virusconfig=args.configdir,
specname=args.specname[amp],
use_pixelflat=(args.pixelflats<1),
init_lims=args.wvl_dict[amp],
check_fibermodel=True, check_wave=True,
fsize=args.fsize,
fibmodel_nbins=args.fibmodel_bins,
sigma=args.fibmodel_sig,
power=args.fibmodel_pow,
use_trace_ref=args.use_trace_ref,
default_fib = args.default_fib,
wave_nbins = args.wave_nbins)
#twi2.load_fibers()
twi2.get_fiber_to_fiber()
twi2.sky_subtraction()
image1 = get_model_image(twi1.image, twi1.fibers,
'fiber_to_fiber', debug=twi1.debug)
image2 = get_model_image(twi2.image, twi2.fibers,
'fiber_to_fiber', debug=twi2.debug)
outname = op.join(args.twi_df['Output'][ind],
'mastertrace_%s_%s.fits'
%(args.twi_df['Specid'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(image1, image2, twi1.header, outname)
outname = op.join(args.twi_df['Output'][ind],
'mastertwi_%s_%s.fits'
%(args.twi_df['Specid'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(twi1.image, twi2.image,
twi1.header, outname)
outname = op.join(args.twi_df['Output'][ind],
'normtwi_%s_%s.fits'
%(args.twi_df['Specid'][ind],
amp))
make_amplifier_image(np.where(
np.isfinite(twi1.skyframe)*(twi1.skyframe!=0),
twi1.image/twi1.skyframe,
0.0),
twi1.header, outname)
outname = op.join(args.twi_df['Output'][ind],
'normtwi_%s_%s.fits'
%(args.twi_df['Specid'][ind],
config.Amp_dict[amp][0]))
make_amplifier_image(np.where(
np.isfinite(twi2.skyframe)*(twi2.skyframe!=0),
twi2.image/twi2.skyframe,
0.0),
twi2.header, outname)
D = recalculate_dist_coeff(D, twi1, twi2)
outname2 = op.join(args.twi_df['Output'][ind],
'mastertrace_%s_%s.dist'
%(args.twi_df['Specid'][ind],
config.Amp_dict[amp][1]))
D.writeto(outname2)
twi1.save_fibers()
twi2.save_fibers()
if args.debug:
print("Finished working on Cal for %s, %s" %(spec, amp))
def reduce_science(args):
for spec in args.specid:
spec_ind_sci = np.where(args.sci_df['Specid'] == spec)[0]
for amp in config.Amps:
amp_ind_sci = np.where(args.sci_df['Amp'] == amp)[0]
sci_sel = np.intersect1d(spec_ind_sci, amp_ind_sci)
for ind in sci_sel:
if args.instr == "virus":
if not args.use_trace_ref:
ifucen = np.loadtxt(op.join(args.configdir,
'IFUcen_files',
args.ifucen_fn[amp][0]
+ args.sci_df['Ifuid'][ind]
+ '.txt'),
usecols=[0,1,2,4],
skiprows=args.ifucen_fn[amp][1])
else:
if args.sci_df['Ifuid'][ind] == '004':
ifucen = np.loadtxt(op.join(args.configdir,
'IFUcen_files',
'IFUcen_HETDEX_reverse_R.txt'),
usecols=[0,1,2,4],
skiprows=args.ifucen_fn[amp][1])
ifucen[224:,:] = ifucen[-1:223:-1,:]
else:
ifucen = np.loadtxt(op.join(args.configdir,
'IFUcen_files',
'IFUcen_HETDEX.txt'),
usecols=[0,1,2,4],
skiprows=args.ifucen_fn[amp][1])
else:
ifucen = np.loadtxt(op.join(args.configdir, 'IFUcen_files',
args.ifucen_fn[amp][0]),
usecols=[0,1,2], skiprows=args.ifucen_fn[amp][1])
if args.debug:
print("Working on Sci for %s, %s" %(spec, amp))
if args.check_if_twi_exists:
fn = op.join(args.twi_dir,'fiber_*_%s_%s_%s_%s.pkl' %(spec,
args.sci_df['Ifuslot'][ind],
args.sci_df['Ifuid'][ind],
amp))
calfiles = glob.glob(fn)
if not calfiles:
print("No cals found for %s,%s: %s"
%(spec, amp, args.sci_df['Files'][ind]))
print("If you want to produce cals include "
"--reduce_twi")
sci1 = Amplifier(args.sci_df['Files'][ind],
args.sci_df['Output'][ind],
calpath=args.twi_dir, skypath=args.sky_dir,
debug=False, refit=False,
dark_mult=args.dark_mult[amp],
darkpath=args.darkdir, biaspath=args.biasdir,
virusconfig=args.configdir,
specname=args.specname[amp],
use_pixelflat=(args.pixelflats<1),
use_trace_ref=args.use_trace_ref,
calculate_shift=args.adjust_trace,
fiber_date=args.fiber_date,
cont_smooth=args.cont_smooth)
#sci1.load_fibers()
#if sci1.fibers and not args.start_from_scratch:
# if sci1.fibers[0].spectrum is not None:
# sci1.prepare_image()
# sci1.sky_subtraction()
# sci1.clean_cosmics()
#else:
sci1.load_all_cal()
if args.adjust_trace:
sci1.refit=True
sci1.get_trace()
sci1.refit=False
sci1.fiberextract()
if args.refit_fiber_to_fiber:
sci1.refit=True
sci1.get_fiber_to_fiber()
sci1.refit=False
sci1.sky_subtraction()
sci1.clean_cosmics()
sci1.fiberextract()
sci1.sky_subtraction()
sci2 = Amplifier(args.sci_df['Files'][ind].replace(amp,
config.Amp_dict[amp][0]),
args.sci_df['Output'][ind],
calpath=args.twi_dir, skypath=args.sky_dir,
debug=False, refit=False,
dark_mult=args.dark_mult[config.Amp_dict[amp][0]],
darkpath=args.darkdir, biaspath=args.biasdir,
virusconfig=args.configdir,
specname=args.specname[amp],
use_pixelflat=(args.pixelflats<1),
use_trace_ref=args.use_trace_ref,
calculate_shift=args.adjust_trace,
fiber_date=args.fiber_date,
cont_smooth=args.cont_smooth)
#sci2.load_fibers()
#if sci2.fibers and not args.start_from_scratch:
# if sci2.fibers[0].spectrum is not None:
# sci2.prepare_image()
# sci2.sky_subtraction()
# sci2.clean_cosmics()
#else:
sci2.load_all_cal()
if args.adjust_trace:
sci2.refit=True
sci2.get_trace()
sci2.refit=False
sci2.fiberextract()
if args.refit_fiber_to_fiber:
sci2.refit=True
sci2.get_fiber_to_fiber()
sci2.refit=False
sci2.sky_subtraction()
sci2.clean_cosmics()
sci2.fiberextract()
sci2.sky_subtraction()
outname = op.join(args.sci_df['Output'][ind],
'S%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(sci1.clean_image, sci2.clean_image,
sci1.header, outname)
make_spectrograph_image(sci1.error, sci2.error,
sci1.header, op.join(op.dirname(outname), 'ee.'+op.basename(outname)))
make_error_frame(sci1.clean_image, sci2.clean_image, sci1.mask,
sci2.mask, sci1.header, outname)
outname = op.join(args.sci_df['Output'][ind],
'cS%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(np.where(sci1.mask==0,
sci1.clean_image, 0.0),
np.where(sci2.mask==0,
sci2.clean_image, 0.0),
sci1.header, outname)
make_error_frame(sci1.clean_image, sci2.clean_image, sci1.mask,
sci2.mask, sci1.header, outname)
outname = op.join(args.sci_df['Output'][ind],
'CsS%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(sci1.continuum_sub, sci2.continuum_sub,
sci1.header, outname)
make_error_frame(sci1.continuum_sub, sci2.continuum_sub,
sci1.mask, sci2.mask, sci1.header, outname)
outname = op.join(args.sci_df['Output'][ind],
'cCsS%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_spectrograph_image(np.where(sci1.mask==0,
sci1.continuum_sub, 0.0),
np.where(sci2.mask==0,
sci2.continuum_sub, 0.0),
sci1.header, outname)
make_error_frame(sci1.continuum_sub, sci2.continuum_sub,
sci1.mask, sci2.mask, sci1.header, outname)
outname = op.join(args.sci_df['Output'][ind],
'cCsS%s_%s_sci_%s_imstat.png' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
imstat(sci1.residual, sci2.residual, sci1.fibers,
sci2.fibers, outname)
Fe, FeS = recreate_fiberextract(sci1, sci2,
wavelim=args.wvl_dict[amp],
disp=args.disp[amp])
outname = op.join(args.sci_df['Output'][ind],
'Fe%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_fiber_image(Fe, sci1.header, outname, args, amp)
make_fiber_error(Fe, sci1.header, outname, args, amp)
make_cube_file(args, outname, ifucen, args.cube_scale,
config.Amp_dict[amp][1])
outname = op.join(args.sci_df['Output'][ind],
'FeS%s_%s_sci_%s.fits' %(
op.basename(args.sci_df['Files'][ind]).split('_')[0],
args.sci_df['Ifuslot'][ind],
config.Amp_dict[amp][1]))
make_fiber_image(FeS, sci1.header, outname, args, amp)
make_fiber_error(FeS, sci1.header, outname, args, amp)
make_cube_file(args, outname, ifucen, args.cube_scale,
config.Amp_dict[amp][1])
if args.save_sci_fibers:
sci1.save_fibers()
sci2.save_fibers()
if args.save_sci_amplifier:
sci1.save()
sci2.save()
if args.debug:
print("Finished working on Sci for %s, %s" %(spec, amp))