def main(args) :
log=get_logger()
log.info("read frame")
# read frame
frame = read_frame(args.infile)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel=read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux,model_wave,model_fibers=read_stdstar_models(args.models)
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
model_fibers = model_fibers%500
if np.any(fibermap['OBJTYPE'][model_fibers] != 'STD'):
for i in model_fibers:
log.error("inconsistency with spectrum %d, OBJTYPE='%s' in fibermap"%(i,fibermap["OBJTYPE"][i]))
sys.exit(12)
fluxcalib = compute_flux_calibration(frame, model_wave, model_flux)
# QA
if (args.qafile is not None):
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile, frame, flavor=frame.meta['FLAVOR'])
# Run
#import pdb; pdb.set_trace()
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s"%args.outfile)
def make_frameqa(self, make_plots=False, clobber=True):
""" Work through the Production and make QA for all frames
Parameters:
make_plots: bool, optional
Remake the plots too?
clobber: bool, optional
Returns:
"""
# imports
from desispec.io import meta
from desispec.io.qa import load_qa_frame, write_qa_frame
from desispec.io.fiberflat import read_fiberflat
from desispec.io.sky import read_sky
from desispec.io.fluxcalibration import read_flux_calibration
from desispec.qa import qa_plots
from desispec.io.fluxcalibration import read_stdstar_models
# Loop on nights
path_nights = glob.glob(self.specprod_dir + '/exposures/*')
nights = [ipathn[ipathn.rfind('/') + 1:] for ipathn in path_nights]
for night in nights:
for exposure in get_exposures(night,
specprod_dir=self.specprod_dir):
# Object only??
frames_dict = get_files(filetype=str('frame'),
night=night,
expid=exposure,
specprod_dir=self.specprod_dir)
for camera, frame_fil in frames_dict.items():
# Load frame
frame = read_frame(frame_fil)
spectro = int(frame.meta['CAMERA'][-1])
if frame.meta['FLAVOR'] in ['flat', 'arc']:
qatype = 'qa_calib'
else:
qatype = 'qa_data'
qafile = meta.findfile(qatype,
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
if (not clobber) & os.path.isfile(qafile):
log.info(
"qafile={:s} exists. Not over-writing. Consider clobber=True"
.format(qafile))
continue
# Load
qaframe = load_qa_frame(qafile,
frame,
flavor=frame.meta['FLAVOR'])
# Flat QA
if frame.meta['FLAVOR'] in ['flat']:
fiberflat_fil = meta.findfile(
'fiberflat',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
fiberflat = read_fiberflat(fiberflat_fil)
qaframe.run_qa('FIBERFLAT', (frame, fiberflat),
clobber=clobber)
if make_plots:
# Do it
qafig = meta.findfile(
'qa_flat_fig',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
qa_plots.frame_fiberflat(qafig, qaframe, frame,
fiberflat)
# SkySub QA
if qatype == 'qa_data':
sky_fil = meta.findfile('sky',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
skymodel = read_sky(sky_fil)
qaframe.run_qa('SKYSUB', (frame, skymodel))
if make_plots:
qafig = meta.findfile(
'qa_sky_fig',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
qa_plots.frame_skyres(qafig, frame, skymodel,
qaframe)
# FluxCalib QA
if qatype == 'qa_data':
# Standard stars
stdstar_fil = meta.findfile(
'stdstars',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir,
spectrograph=spectro)
model_tuple = read_stdstar_models(stdstar_fil)
flux_fil = meta.findfile(
'calib',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
fluxcalib = read_flux_calibration(flux_fil)
qaframe.run_qa(
'FLUXCALIB',
(frame, fluxcalib, model_tuple)) #, indiv_stars))
if make_plots:
qafig = meta.findfile(
'qa_flux_fig',
night=night,
camera=camera,
expid=exposure,
specprod_dir=self.specprod_dir)
qa_plots.frame_fluxcalib(qafig, qaframe, frame,
fluxcalib, model_tuple)
# Write
write_qa_frame(qafile, qaframe)
def main(args) :
log=get_logger()
cmd = ['desi_compute_fluxcalibration',]
for key, value in args.__dict__.items():
if value is not None:
cmd += ['--'+key, str(value)]
cmd = ' '.join(cmd)
log.info(cmd)
log.info("read frame")
# read frame
frame = read_frame(args.infile)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel=read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux,model_wave,model_fibers,model_metadata=read_stdstar_models(args.models)
if args.chi2cut > 0 :
ok = np.where(model_metadata["CHI2DOF"]<args.chi2cut)[0]
if ok.size == 0 :
log.error("chi2cut has discarded all stars")
sys.exit(12)
nstars=model_flux.shape[0]
nbad=nstars-ok.size
if nbad>0 :
log.warning("discarding %d star(s) out of %d because of chi2cut"%(nbad,nstars))
model_flux=model_flux[ok]
model_fibers=model_fibers[ok]
model_metadata=model_metadata[:][ok]
if args.delta_color_cut > 0 :
ok = np.where(np.abs(model_metadata["MODEL_G-R"]-model_metadata["DATA_G-R"])<args.delta_color_cut)[0]
nstars=model_flux.shape[0]
nbad=nstars-ok.size
if nbad>0 :
log.warning("discarding %d star(s) out of %d because |delta_color|>%f"%(nbad,nstars,args.delta_color_cut))
model_flux=model_flux[ok]
model_fibers=model_fibers[ok]
model_metadata=model_metadata[:][ok]
# automatically reject stars that ar chi2 outliers
if args.chi2cut_nsig > 0 :
mchi2=np.median(model_metadata["CHI2DOF"])
rmschi2=np.std(model_metadata["CHI2DOF"])
maxchi2=mchi2+args.chi2cut_nsig*rmschi2
ok=np.where(model_metadata["CHI2DOF"]<=maxchi2)[0]
nstars=model_flux.shape[0]
nbad=nstars-ok.size
if nbad>0 :
log.warning("discarding %d star(s) out of %d because reduced chi2 outliers (at %d sigma, giving rchi2<%f )"%(nbad,nstars,args.chi2cut_nsig,maxchi2))
model_flux=model_flux[ok]
model_fibers=model_fibers[ok]
model_metadata=model_metadata[:][ok]
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
## check whether star fibers from args.models are consistent with fibers from fibermap
## if not print the OBJTYPE from fibermap for the fibers numbers in args.models and exit
fibermap_std_indices = np.where(isStdStar(fibermap['DESI_TARGET']))[0]
if np.any(~np.in1d(model_fibers%500, fibermap_std_indices)):
for i in model_fibers%500:
log.error("inconsistency with spectrum {}, OBJTYPE='{}', DESI_TARGET={} in fibermap".format(
(i, fibermap["OBJTYPE"][i], fibermap["DESI_TARGET"][i])))
sys.exit(12)
fluxcalib = compute_flux_calibration(frame, model_wave, model_flux, model_fibers%500)
# QA
if (args.qafile is not None):
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile, frame, flavor=frame.meta['FLAVOR'])
# Run
#import pdb; pdb.set_trace()
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s"%args.outfile)
def make_frameqa(self, make_plots=False, clobber=True):
""" Work through the Production and make QA for all frames
Parameters:
make_plots: bool, optional
Remake the plots too?
clobber: bool, optional
Returns:
"""
# imports
from desispec.io import meta
from desispec.io.qa import load_qa_frame, write_qa_frame
from desispec.io.fiberflat import read_fiberflat
from desispec.io.sky import read_sky
from desispec.io.fluxcalibration import read_flux_calibration
from desispec.qa import qa_plots
from desispec.io.fluxcalibration import read_stdstar_models
# Loop on nights
path_nights = glob.glob(self.specprod_dir+'/exposures/*')
nights = [ipathn[ipathn.rfind('/')+1:] for ipathn in path_nights]
for night in nights:
for exposure in get_exposures(night, specprod_dir = self.specprod_dir):
# Object only??
frames_dict = get_files(filetype = str('frame'), night = night,
expid = exposure, specprod_dir = self.specprod_dir)
for camera,frame_fil in frames_dict.items():
# Load frame
frame = read_frame(frame_fil)
spectro = int(frame.meta['CAMERA'][-1])
if frame.meta['FLAVOR'] in ['flat','arc']:
qatype = 'qa_calib'
else:
qatype = 'qa_data'
qafile = meta.findfile(qatype, night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
if (not clobber) & os.path.isfile(qafile):
log.info("qafile={:s} exists. Not over-writing. Consider clobber=True".format(qafile))
continue
# Load
qaframe = load_qa_frame(qafile, frame, flavor=frame.meta['FLAVOR'])
# Flat QA
if frame.meta['FLAVOR'] in ['flat']:
fiberflat_fil = meta.findfile('fiberflat', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
fiberflat = read_fiberflat(fiberflat_fil)
qaframe.run_qa('FIBERFLAT', (frame, fiberflat), clobber=clobber)
if make_plots:
# Do it
qafig = meta.findfile('qa_flat_fig', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
qa_plots.frame_fiberflat(qafig, qaframe, frame, fiberflat)
# SkySub QA
if qatype == 'qa_data':
sky_fil = meta.findfile('sky', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
skymodel = read_sky(sky_fil)
qaframe.run_qa('SKYSUB', (frame, skymodel))
if make_plots:
qafig = meta.findfile('qa_sky_fig', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
qa_plots.frame_skyres(qafig, frame, skymodel, qaframe)
# FluxCalib QA
if qatype == 'qa_data':
# Standard stars
stdstar_fil = meta.findfile('stdstars', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir,
spectrograph=spectro)
model_tuple=read_stdstar_models(stdstar_fil)
flux_fil = meta.findfile('calib', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
fluxcalib = read_flux_calibration(flux_fil)
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib, model_tuple))#, indiv_stars))
if make_plots:
qafig = meta.findfile('qa_flux_fig', night=night, camera=camera, expid=exposure, specprod_dir=self.specprod_dir)
qa_plots.frame_fluxcalib(qafig, qaframe, frame, fluxcalib, model_tuple)
# Write
write_qa_frame(qafile, qaframe)
def main(args):
log = get_logger()
log.info("read frame")
# read frame
frame = read_frame(args.infile)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel = read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux, model_wave, model_fibers = read_stdstar_models(args.models)
model_tuple = model_flux, model_wave, model_fibers
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
model_fibers = model_fibers % 500
if np.any(fibermap['OBJTYPE'][model_fibers] != 'STD'):
for i in model_fibers:
log.error(
"inconsistency with spectrum %d, OBJTYPE='%s' in fibermap" %
(i, fibermap["OBJTYPE"][i]))
sys.exit(12)
#fluxcalib, indiv_stars = compute_flux_calibration(frame, model_wave, model_flux)
fluxcalib = compute_flux_calibration(frame, model_wave, model_flux)
# QA
if (args.qafile is not None):
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile,
frame,
flavor=frame.meta['FLAVOR'])
# Run
qaframe.run_qa('FLUXCALIB',
(frame, fluxcalib, model_tuple)) #, indiv_stars))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib,
model_tuple)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s" % args.outfile)
def main(args):
log = get_logger()
cmd = [
'desi_compute_fluxcalibration',
]
for key, value in args.__dict__.items():
if value is not None:
cmd += ['--' + key, str(value)]
cmd = ' '.join(cmd)
log.info(cmd)
log.info("read frame")
# read frame
frame = read_frame(args.infile)
# Set fibermask flagged spectra to have 0 flux and variance
frame = get_fiberbitmasked_frame(frame,
bitmask='flux',
ivar_framemask=True)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel = read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux, model_wave, model_fibers, model_metadata = read_stdstar_models(
args.models)
ok = np.ones(len(model_metadata), dtype=bool)
if args.chi2cut > 0:
log.info("Apply cut CHI2DOF<{}".format(args.chi2cut))
ok &= (model_metadata["CHI2DOF"] < args.chi2cut)
if args.delta_color_cut > 0:
log.info("Apply cut |delta color|<{}".format(args.delta_color_cut))
ok &= (np.abs(model_metadata["MODEL_G-R"] - model_metadata["DATA_G-R"])
< args.delta_color_cut)
if args.min_color is not None:
log.info("Apply cut DATA_G-R>{}".format(args.min_color))
ok &= (model_metadata["DATA_G-R"] > args.min_color)
if args.chi2cut_nsig > 0:
# automatically reject stars that ar chi2 outliers
mchi2 = np.median(model_metadata["CHI2DOF"])
rmschi2 = np.std(model_metadata["CHI2DOF"])
maxchi2 = mchi2 + args.chi2cut_nsig * rmschi2
log.info("Apply cut CHI2DOF<{} based on chi2cut_nsig={}".format(
maxchi2, args.chi2cut_nsig))
ok &= (model_metadata["CHI2DOF"] <= maxchi2)
ok = np.where(ok)[0]
if ok.size == 0:
log.error("cuts discarded all stars")
sys.exit(12)
nstars = model_flux.shape[0]
nbad = nstars - ok.size
if nbad > 0:
log.warning("discarding %d star(s) out of %d because of cuts" %
(nbad, nstars))
model_flux = model_flux[ok]
model_fibers = model_fibers[ok]
model_metadata = model_metadata[:][ok]
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
## check whether star fibers from args.models are consistent with fibers from fibermap
## if not print the OBJTYPE from fibermap for the fibers numbers in args.models and exit
fibermap_std_indices = np.where(isStdStar(fibermap))[0]
if np.any(~np.in1d(model_fibers % 500, fibermap_std_indices)):
target_colnames, target_masks, survey = main_cmx_or_sv(fibermap)
colname = target_colnames[0]
for i in model_fibers % 500:
log.error(
"inconsistency with spectrum {}, OBJTYPE={}, {}={} in fibermap"
.format(i, fibermap["OBJTYPE"][i], colname,
fibermap[colname][i]))
sys.exit(12)
# Make sure the fibers of interest aren't entirely masked.
if np.sum(
np.sum(frame.ivar[model_fibers % 500, :] == 0, axis=1) ==
frame.nwave) == len(model_fibers):
log.warning('All standard-star spectra are masked!')
return
fluxcalib = compute_flux_calibration(
frame,
model_wave,
model_flux,
model_fibers % 500,
highest_throughput_nstars=args.highest_throughput)
# QA
if (args.qafile is not None):
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile,
frame_meta=frame.meta,
flavor=frame.meta['FLAVOR'])
# Run
#import pdb; pdb.set_trace()
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s" % args.outfile)
def main(args):
log = get_logger()
log.info("read frame")
# read frame
frame = read_frame(args.infile)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel = read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux, model_wave, model_fibers, model_metadata = read_stdstar_models(
args.models)
if args.chi2cut > 0:
ok = np.where(model_metadata["CHI2DOF"] < args.chi2cut)[0]
if ok.size == 0:
log.error("chi2cut has discarded all stars")
sys.exit(12)
nstars = model_flux.shape[0]
nbad = nstars - ok.size
if nbad > 0:
log.warning("discarding %d star(s) out of %d because of chi2cut" %
(nbad, nstars))
model_flux = model_flux[ok]
model_fibers = model_fibers[ok]
model_metadata = model_metadata[:][ok]
if args.delta_color_cut > 0:
ok = np.where(
np.abs(model_metadata["MODEL_G-R"] -
model_metadata["DATA_G-R"]) < args.delta_color_cut)[0]
nstars = model_flux.shape[0]
nbad = nstars - ok.size
if nbad > 0:
log.warning(
"discarding %d star(s) out of %d because |delta_color|>%f" %
(nbad, nstars, args.delta_color_cut))
model_flux = model_flux[ok]
model_fibers = model_fibers[ok]
model_metadata = model_metadata[:][ok]
# automatically reject stars that ar chi2 outliers
if args.chi2cut_nsig > 0:
mchi2 = np.median(model_metadata["CHI2DOF"])
rmschi2 = np.std(model_metadata["CHI2DOF"])
maxchi2 = mchi2 + args.chi2cut_nsig * rmschi2
ok = np.where(model_metadata["CHI2DOF"] <= maxchi2)[0]
nstars = model_flux.shape[0]
nbad = nstars - ok.size
if nbad > 0:
log.warning(
"discarding %d star(s) out of %d because reduced chi2 outliers (at %d sigma, giving rchi2<%f )"
% (nbad, nstars, args.chi2cut_nsig, maxchi2))
model_flux = model_flux[ok]
model_fibers = model_fibers[ok]
model_metadata = model_metadata[:][ok]
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
## check whether star fibers from args.models are consistent with fibers from fibermap
## if not print the OBJTYPE from fibermap for the fibers numbers in args.models and exit
w = np.where(fibermap["OBJTYPE"][model_fibers % 500] != 'STD')[0]
if len(w) > 0:
for i in model_fibers % 500:
log.error(
"inconsistency with spectrum %d, OBJTYPE='%s' in fibermap" %
(i, fibermap["OBJTYPE"][i]))
sys.exit(12)
fluxcalib = compute_flux_calibration(frame, model_wave, model_flux,
model_fibers % 500)
# QA
if (args.qafile is not None):
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile,
frame,
flavor=frame.meta['FLAVOR'])
# Run
#import pdb; pdb.set_trace()
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s" % args.outfile)
def main(args):
log = get_logger()
cmd = [
'desi_compute_fluxcalibration',
]
for key, value in args.__dict__.items():
if value is not None:
cmd += ['--' + key, str(value)]
cmd = ' '.join(cmd)
log.info(cmd)
log.info("read frame")
# read frame
frame = read_frame(args.infile)
# Set fibermask flagged spectra to have 0 flux and variance
frame = get_fiberbitmasked_frame(frame,
bitmask='flux',
ivar_framemask=True)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel = read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux, model_wave, model_fibers, model_metadata = read_stdstar_models(
args.models)
ok = np.ones(len(model_metadata), dtype=bool)
if args.chi2cut > 0:
log.info("apply cut CHI2DOF<{}".format(args.chi2cut))
good = (model_metadata["CHI2DOF"] < args.chi2cut)
bad = ~good
ok &= good
if np.any(bad):
log.info(" discard {} stars with CHI2DOF= {}".format(
np.sum(bad), list(model_metadata["CHI2DOF"][bad])))
legacy_filters = ('G-R', 'R-Z')
gaia_filters = ('GAIA-BP-RP', 'GAIA-G-RP')
model_column_list = model_metadata.columns.names
if args.color is None:
if 'MODEL_G-R' in model_column_list:
color = 'G-R'
elif 'MODEL_GAIA-BP-RP' in model_column_list:
log.info('Using Gaia filters')
color = 'GAIA-BP-RP'
else:
log.error(
"Can't find either G-R or BP-RP color in the model file.")
sys.exit(15)
else:
if args.color not in legacy_filters and args.color not in gaia_filters:
log.error(
'Color name {} is not allowed, must be one of {} {}'.format(
args.color, legacy_filters, gaia_filters))
sys.exit(14)
color = args.color
if color not in model_column_list:
# This should't happen
log.error(
'The color {} was not computed in the models'.format(color))
sys.exit(16)
if args.delta_color_cut > 0:
log.info("apply cut |delta color|<{}".format(args.delta_color_cut))
good = (np.abs(model_metadata["MODEL_" + color] -
model_metadata["DATA_" + color]) < args.delta_color_cut)
bad = ok & (~good)
ok &= good
if np.any(bad):
vals = model_metadata["MODEL_" +
color][bad] - model_metadata["DATA_" +
color][bad]
log.info(" discard {} stars with dcolor= {}".format(
np.sum(bad), list(vals)))
if args.min_color is not None:
log.info("apply cut DATA_{}>{}".format(color, args.min_color))
good = (model_metadata["DATA_{}".format(color)] > args.min_color)
bad = ok & (~good)
ok &= good
if np.any(bad):
vals = model_metadata["DATA_{}".format(color)][bad]
log.info(" discard {} stars with {}= {}".format(
np.sum(bad), color, list(vals)))
if args.chi2cut_nsig > 0:
# automatically reject stars that ar chi2 outliers
mchi2 = np.median(model_metadata["CHI2DOF"])
rmschi2 = np.std(model_metadata["CHI2DOF"])
maxchi2 = mchi2 + args.chi2cut_nsig * rmschi2
log.info("apply cut CHI2DOF<{} based on chi2cut_nsig={}".format(
maxchi2, args.chi2cut_nsig))
good = (model_metadata["CHI2DOF"] <= maxchi2)
bad = ok & (~good)
ok &= good
if np.any(bad):
log.info(" discard {} stars with CHI2DOF={}".format(
np.sum(bad), list(model_metadata["CHI2DOF"][bad])))
ok = np.where(ok)[0]
if ok.size == 0:
log.error("selection cuts discarded all stars")
sys.exit(12)
nstars = model_flux.shape[0]
nbad = nstars - ok.size
if nbad > 0:
log.warning("discarding %d star(s) out of %d because of cuts" %
(nbad, nstars))
model_flux = model_flux[ok]
model_fibers = model_fibers[ok]
model_metadata = model_metadata[:][ok]
# check that the model_fibers are actually standard stars
fibermap = frame.fibermap
## check whether star fibers from args.models are consistent with fibers from fibermap
## if not print the OBJTYPE from fibermap for the fibers numbers in args.models and exit
fibermap_std_indices = np.where(isStdStar(fibermap))[0]
if np.any(~np.in1d(model_fibers % 500, fibermap_std_indices)):
target_colnames, target_masks, survey = main_cmx_or_sv(fibermap)
colname = target_colnames[0]
for i in model_fibers % 500:
log.error(
"inconsistency with spectrum {}, OBJTYPE={}, {}={} in fibermap"
.format(i, fibermap["OBJTYPE"][i], colname,
fibermap[colname][i]))
sys.exit(12)
# Make sure the fibers of interest aren't entirely masked.
if np.sum(
np.sum(frame.ivar[model_fibers % 500, :] == 0, axis=1) ==
frame.nwave) == len(model_fibers):
log.warning('All standard-star spectra are masked!')
return
fluxcalib = compute_flux_calibration(
frame,
model_wave,
model_flux,
model_fibers % 500,
highest_throughput_nstars=args.highest_throughput,
exposure_seeing_fwhm=args.seeing_fwhm)
# QA
if (args.qafile is not None):
from desispec.io import write_qa_frame
from desispec.io.qa import load_qa_frame
from desispec.qa import qa_plots
log.info("performing fluxcalib QA")
# Load
qaframe = load_qa_frame(args.qafile,
frame_meta=frame.meta,
flavor=frame.meta['FLAVOR'])
# Run
#import pdb; pdb.set_trace()
qaframe.run_qa('FLUXCALIB', (frame, fluxcalib))
# Write
if args.qafile is not None:
write_qa_frame(args.qafile, qaframe)
log.info("successfully wrote {:s}".format(args.qafile))
# Figure(s)
if args.qafig is not None:
qa_plots.frame_fluxcalib(args.qafig, qaframe, frame, fluxcalib)
# record inputs
frame.meta['IN_FRAME'] = shorten_filename(args.infile)
frame.meta['IN_SKY'] = shorten_filename(args.sky)
frame.meta['FIBERFLT'] = shorten_filename(args.fiberflat)
frame.meta['STDMODEL'] = shorten_filename(args.models)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.meta)
log.info("successfully wrote %s" % args.outfile)
def main() :
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--infile', type = str, default = None, required=True,
help = 'path of DESI exposure frame fits file')
parser.add_argument('--fibermap', type = str, default = None, required=True,
help = 'path of DESI exposure frame fits file')
parser.add_argument('--fiberflat', type = str, default = None, required=True,
help = 'path of DESI fiberflat fits file')
parser.add_argument('--sky', type = str, default = None, required=True,
help = 'path of DESI sky fits file')
parser.add_argument('--models', type = str, default = None, required=True,
help = 'path of spetro-photometric stellar spectra fits file')
parser.add_argument('--outfile', type = str, default = None, required=True,
help = 'path of DESI flux calbration fits file')
args = parser.parse_args()
log=get_logger()
log.info("read frame")
# read frame
frame = read_frame(args.infile)
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat
apply_fiberflat(frame, fiberflat)
log.info("subtract sky")
# read sky
skymodel=read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
log.info("compute flux calibration")
# read models
model_flux,model_wave,model_fibers=read_stdstar_models(args.models)
# select fibers
SPECMIN=frame.header["SPECMIN"]
SPECMAX=frame.header["SPECMAX"]
selec=np.where((model_fibers>=SPECMIN)&(model_fibers<=SPECMAX))[0]
if selec.size == 0 :
log.error("no stellar models for this spectro")
sys.exit(12)
fibers=model_fibers[selec]-frame.header["SPECMIN"]
log.info("star fibers= %s"%str(fibers))
table = read_fibermap(args.fibermap)
bad=np.where(table["OBJTYPE"][fibers]!="STD")[0]
if bad.size > 0 :
for fiber in fibers[bad] :
log.error("inconsistency with fiber %d, OBJTYPE='%s' in fibermap"%(fiber,table["OBJTYPE"][fiber]))
sys.exit(12)
fluxcalib = compute_flux_calibration(frame, fibers, model_wave, model_flux)
# write result
write_flux_calibration(args.outfile, fluxcalib, header=frame.header)
log.info("successfully wrote %s"%args.outfile)