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))
def custom(args):
lowfib = int(112 / 4. - 1.)
midfib = int(112 / 2. - 1.)
highfib = int(3.* 112. / 4. - 1.)
trace_list = {"LL":[],"LU":[],"RU":[],"RL":[]}
amps = {"LL":"LL","LU":"LL","RU":"RU","RL":"RU"}
for spec in args.specid:
spec_ind_twi = np.where(args.twi_df['Specid'] == spec)[0]
spec_ind_sci = np.where(args.sci_df['Specid'] == spec)[0]
for ind in spec_ind_twi:
amp = args.twi_df['Amp'][ind]
AMP = amps[amp]
twi = 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)
twi.load_fibers()
if len(twi.fibers)==0:
twi.get_trace()
else:
if not hasattr(twi.fibers[0],'trace'):
twi.get_trace()
blue = int(twi.D /4.)
green = int(twi.D /2.)
red = int(3.*twi.D /4.)
trace_list[amp].append(np.array([twi.fibers[lowfib].trace[blue],
twi.fibers[lowfib].trace[green],
twi.fibers[lowfib].trace[red],
twi.fibers[midfib].trace[blue],
twi.fibers[midfib].trace[green],
twi.fibers[midfib].trace[red],
twi.fibers[highfib].trace[blue],
twi.fibers[highfib].trace[green],
twi.fibers[highfib].trace[red]]))
for ind in spec_ind_sci:
amp = args.sci_df['Amp'][ind]
AMP = amps[amp]
print(args.sci_df['Files'][ind])
sci = Amplifier(args.sci_df['Files'][ind],
args.sci_df['Output'][ind],
calpath=args.twi_dir, skypath=args.sky_dir,
debug=False, refit=True,
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=False)
sci.load_fibers()
if len(sci.fibers)==0:
sci.get_trace()
else:
if not hasattr(sci.fibers[0],'trace'):
sci.get_trace()
blue = int(sci.D /4.)
green = int(sci.D /2.)
red = int(3.*sci.D /4.)
trace_list[amp].append(np.array([sci.fibers[lowfib].trace[blue],
sci.fibers[lowfib].trace[green],
sci.fibers[lowfib].trace[red],
sci.fibers[midfib].trace[blue],
sci.fibers[midfib].trace[green],
sci.fibers[midfib].trace[red],
sci.fibers[highfib].trace[blue],
sci.fibers[highfib].trace[green],
sci.fibers[highfib].trace[red]]))
import matplotlib.pyplot as plt
plt.figure(figsize=(12,12))
ax1 = plt.axes([0.1,0.1,0.35,0.35])
ax2 = plt.axes([0.1,0.55,0.35,0.35])
ax3 = plt.axes([0.55,0.1,0.35,0.35])
ax4 = plt.axes([0.55,0.55,0.35,0.35])
amps = ["LL","LU","RU","RL"]
ax = [ax1,ax2,ax3,ax4]
for i,amp in enumerate(amps):
TR = np.array(trace_list[amp])
avg = biweight_location(TR,axis=(0,))
print(TR-avg)
ax[i].plot(TR-avg)
fn = op.join(args.output, args.scidir_date[0], args.instr,
'trace_%s.png' %args.specid[0])
plt.savefig(fn,dpi=150)
def main():
args = parse_args()
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,
refit=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)
twi1.get_trace()
fname = op.join(
args.configdir, 'Fiber_Locations', args.twidir_date[0],
'fiber_loc_%s_%s_%s_%s.txt' %
(args.twi_df['Specid'][ind], args.twi_df['Ifuslot'][ind],
args.twi_df['Ifuid'][ind], amp))
if len(twi1.fibers) == args.nfibers[amp]:
print("The correct number of fibers, %i, were found." %
args.nfibers[amp])
Y = np.zeros((len(twi1.fibers), 2))
col = int(twi1.D * config.frac)
for i, fiber in enumerate(twi1.fibers):
Y[i, 0] = fiber.trace[col]
print("Printing file for fibers.")
create_fiber_file(fname, config.frac, Y)
else:
print("Only found %i fibers instead of the expected %i" %
(len(twi1.fibers), args.nfibers[amp]))
Y = find_missing_fibers(config.frac, twi1)
create_fiber_file(fname, config.frac, Y)
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,
refit=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)
twi2.get_trace()
folder = op.join(args.configdir, 'Fiber_Locations',
args.twidir_date[0])
mkpath(folder)
fname = op.join(
args.configdir, 'Fiber_Locations', args.twidir_date[0],
'fiber_loc_%s_%s_%s_%s.txt' %
(args.twi_df['Specid'][ind], args.twi_df['Ifuslot'][ind],
args.twi_df['Ifuid'][ind], config.Amp_dict[amp][0]))
if len(twi2.fibers) == args.nfibers[config.Amp_dict[amp][0]]:
print("The correct number of fibers, %i, were found." %
args.nfibers[config.Amp_dict[amp][0]])
Y = np.zeros((len(twi2.fibers), 2))
col = int(twi2.D * config.frac)
for i, fiber in enumerate(twi2.fibers):
Y[i, 0] = fiber.trace[col]
print("Printing file for fibers.")
create_fiber_file(fname, config.frac, Y)
else:
print("Only found %i fibers instead of the expected %i" %
(len(twi2.fibers),
args.nfibers[config.Amp_dict[amp][0]]))
Y = find_missing_fibers(config.frac, twi2)
create_fiber_file(fname, config.frac, Y)