def load_s2n_values(objtype, nights, channel, sub_exposures=None):
fdict = dict(waves=[], s2n=[], fluxes=[], exptime=[], OII=[])
for night in nights:
if sub_exposures is not None:
exposures = sub_exposures
else:
exposures = get_exposures(night) #, raw=True)
for exposure in exposures:
fibermap_path = findfile(filetype='fibermap',
night=night,
expid=exposure)
fibermap_data = read_fibermap(fibermap_path)
flavor = fibermap_data.meta['FLAVOR']
if flavor.lower() in ('arc', 'flat', 'bias'):
log.debug('Skipping calibration {} exposure {:08d}'.format(
flavor, exposure))
continue
# Load simspec
simspec_file = fibermap_path.replace('fibermap', 'simspec')
sps_hdu = fits.open(simspec_file)
sps_tab = Table(sps_hdu['TRUTH'].data, masked=True)
sps_hdu.close()
objs = sps_tab['TEMPLATETYPE'] == objtype
if np.sum(objs) == 0:
continue
# Load spectra (flux or not fluxed; should not matter)
for ii in range(10):
camera = channel + str(ii)
cframe_path = findfile(filetype='cframe',
night=night,
expid=exposure,
camera=camera)
try:
cframe = read_frame(cframe_path)
except:
log.warn("Cannot find file: {:s}".format(cframe_path))
continue
# Calculate S/N per Ang
dwave = cframe.wave - np.roll(cframe.wave, 1)
dwave[0] = dwave[1]
#
iobjs = objs[cframe.fibers]
if np.sum(iobjs) == 0:
continue
s2n = cframe.flux[iobjs, :] * np.sqrt(
cframe.ivar[iobjs, :]) / np.sqrt(dwave)
# Save
fdict['waves'].append(cframe.wave)
fdict['s2n'].append(s2n)
fdict['fluxes'].append(sps_tab['MAG'][cframe.fibers[iobjs]])
if objtype == 'ELG':
fdict['OII'].append(
sps_tab['OIIFLUX'][cframe.fibers[iobjs]])
fdict['exptime'].append(cframe.meta['EXPTIME'])
# Return
return fdict
def main(args):
log = get_logger()
if (args.fiberflat is None) and (args.sky is None) and (args.calib is None):
log.critical('no --fiberflat, --sky, or --calib; nothing to do ?!?')
sys.exit(12)
frame = read_frame(args.infile)
if args.cosmics_nsig>0 : # Reject cosmics
reject_cosmic_rays_1d(frame,args.cosmics_nsig)
if args.fiberflat!=None :
log.info("apply fiberflat")
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat to sky fibers
apply_fiberflat(frame, fiberflat)
if args.sky!=None :
log.info("subtract sky")
# read sky
skymodel=read_sky(args.sky)
# subtract sky
subtract_sky(frame, skymodel)
if args.calib!=None :
log.info("calibrate")
# read calibration
fluxcalib=read_flux_calibration(args.calib)
# apply calibration
apply_flux_calibration(frame, fluxcalib)
if args.cosmics_nsig>0 : # Reject cosmics one more time after sky subtraction to catch cosmics close to sky lines
reject_cosmic_rays_1d(frame,args.cosmics_nsig)
# save output
write_frame(args.outfile, frame, units='1e-17 erg/(s cm2 A)')
log.info("successfully wrote %s"%args.outfile)
def main(args) :
log=get_logger()
log.info("starting")
# read exposure to load data and get range of spectra
frame = read_frame(args.infile)
specmin, specmax = np.min(frame.fibers), np.max(frame.fibers)
if args.cosmics_nsig>0 : # Reject cosmics
reject_cosmic_rays_1d(frame,args.cosmics_nsig)
# read fiberflat
fiberflat = read_fiberflat(args.fiberflat)
# apply fiberflat to sky fibers
apply_fiberflat(frame, fiberflat)
# compute sky model
skymodel = compute_sky(frame,add_variance=(not args.no_extra_variance))
# QA
if (args.qafile is not None) or (args.qafig is not None):
log.info("performing skysub QA")
# Load
qaframe = load_qa_frame(args.qafile, frame, flavor=frame.meta['FLAVOR'])
# Run
qaframe.run_qa('SKYSUB', (frame, skymodel))
# 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_skyres(args.qafig, frame, skymodel, qaframe)
# write result
write_sky(args.outfile, skymodel, frame.meta)
log.info("successfully wrote %s"%args.outfile)
def main(args):
log = get_logger()
log.info("starting at {}".format(time.asctime()))
# Process
frame = read_frame(args.infile)
if args.cosmics_nsig > 0: # Reject cosmics
reject_cosmic_rays_1d(frame, args.cosmics_nsig)
fiberflat = compute_fiberflat(frame,
nsig_clipping=args.nsig,
accuracy=args.acc,
smoothing_res=args.smoothing_resolution)
# QA
if (args.qafile is not None):
log.info("performing fiberflat QA")
# Load
qaframe = load_qa_frame(args.qafile,
frame,
flavor=frame.meta['FLAVOR'])
# Run
qaframe.run_qa('FIBERFLAT', (frame, fiberflat))
# 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_fiberflat(args.qafig, qaframe, frame, fiberflat)
# Write
write_fiberflat(args.outfile, fiberflat, frame.meta)
log.info("successfully wrote %s" % args.outfile)
log.info("done at {}".format(time.asctime()))
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):
""" finds the best models of all standard stars in the frame
and normlize the model flux. Output is written to a file and will be called for calibration.
"""
log = get_logger()
log.info("mag delta %s = %f (for the pre-selection of stellar models)" %
(args.color, args.delta_color))
frames = {}
flats = {}
skies = {}
spectrograph = None
starfibers = None
starindices = None
fibermap = None
# READ DATA
############################################
for filename in args.frames:
log.info("reading %s" % filename)
frame = io.read_frame(filename)
header = fits.getheader(filename, 0)
frame_fibermap = frame.fibermap
frame_starindices = np.where(frame_fibermap["OBJTYPE"] == "STD")[0]
# check magnitude are well defined or discard stars
tmp = []
for i in frame_starindices:
mags = frame_fibermap["MAG"][i]
ok = np.sum((mags > 0) & (mags < 30))
if np.sum((mags > 0) & (mags < 30)) == mags.size:
tmp.append(i)
frame_starindices = np.array(tmp).astype(int)
camera = safe_read_key(header, "CAMERA").strip().lower()
if spectrograph is None:
spectrograph = frame.spectrograph
fibermap = frame_fibermap
starindices = frame_starindices
starfibers = fibermap["FIBER"][starindices]
elif spectrograph != frame.spectrograph:
log.error("incompatible spectrographs %d != %d" %
(spectrograph, frame.spectrograph))
raise ValueError("incompatible spectrographs %d != %d" %
(spectrograph, frame.spectrograph))
elif starindices.size != frame_starindices.size or np.sum(
starindices != frame_starindices) > 0:
log.error("incompatible fibermap")
raise ValueError("incompatible fibermap")
if not camera in frames:
frames[camera] = []
frames[camera].append(frame)
for filename in args.skymodels:
log.info("reading %s" % filename)
sky = io.read_sky(filename)
header = fits.getheader(filename, 0)
camera = safe_read_key(header, "CAMERA").strip().lower()
if not camera in skies:
skies[camera] = []
skies[camera].append(sky)
for filename in args.fiberflats:
log.info("reading %s" % filename)
header = fits.getheader(filename, 0)
flat = io.read_fiberflat(filename)
camera = safe_read_key(header, "CAMERA").strip().lower()
# NEED TO ADD MORE CHECKS
if camera in flats:
log.warning(
"cannot handle several flats of same camera (%s), will use only the first one"
% camera)
#raise ValueError("cannot handle several flats of same camera (%s)"%camera)
else:
flats[camera] = flat
if starindices.size == 0:
log.error("no STD star found in fibermap")
raise ValueError("no STD star found in fibermap")
log.info("found %d STD stars" % starindices.size)
imaging_filters = fibermap["FILTER"][starindices]
imaging_mags = fibermap["MAG"][starindices]
log.warning(
"NO MAG ERRORS IN FIBERMAP, I AM IGNORING MEASUREMENT ERRORS !!")
ebv = np.zeros(starindices.size)
if "SFD_EBV" in fibermap.columns.names:
log.info("Using 'SFD_EBV' from fibermap")
ebv = fibermap["SFD_EBV"][starindices]
else:
log.warning("NO EXTINCTION VALUES IN FIBERMAP!!")
# DIVIDE FLAT AND SUBTRACT SKY , TRIM DATA
############################################
for cam in frames:
if not cam in skies:
log.warning("Missing sky for %s" % cam)
frames.pop(cam)
continue
if not cam in flats:
log.warning("Missing flat for %s" % cam)
frames.pop(cam)
continue
flat = flats[cam]
for frame, sky in zip(frames[cam], skies[cam]):
frame.flux = frame.flux[starindices]
frame.ivar = frame.ivar[starindices]
frame.ivar *= (frame.mask[starindices] == 0)
frame.ivar *= (sky.ivar[starindices] != 0)
frame.ivar *= (sky.mask[starindices] == 0)
frame.ivar *= (flat.ivar[starindices] != 0)
frame.ivar *= (flat.mask[starindices] == 0)
frame.flux *= (frame.ivar > 0) # just for clean plots
for star in range(frame.flux.shape[0]):
ok = np.where((frame.ivar[star] > 0)
& (flat.fiberflat[star] != 0))[0]
if ok.size > 0:
frame.flux[star] = frame.flux[star] / flat.fiberflat[
star] - sky.flux[star]
frame.resolution_data = frame.resolution_data[starindices]
nstars = starindices.size
starindices = None # we don't need this anymore
# READ MODELS
############################################
log.info("reading star models in %s" % args.starmodels)
stdwave, stdflux, templateid, teff, logg, feh = io.read_stdstar_templates(
args.starmodels)
# COMPUTE MAGS OF MODELS FOR EACH STD STAR MAG
############################################
model_filters = []
for tmp in np.unique(imaging_filters):
if len(tmp) > 0: # can be one empty entry
model_filters.append(tmp)
log.info("computing model mags %s" % model_filters)
model_mags = np.zeros((stdflux.shape[0], len(model_filters)))
fluxunits = 1e-17 * units.erg / units.s / units.cm**2 / units.Angstrom
for index in range(len(model_filters)):
if model_filters[index].startswith('WISE'):
log.warning('not computing stdstar {} mags'.format(
model_filters[index]))
continue
filter_response = load_filter(model_filters[index])
for m in range(stdflux.shape[0]):
model_mags[m, index] = filter_response.get_ab_magnitude(
stdflux[m] * fluxunits, stdwave)
log.info("done computing model mags")
mean_extinction_delta_mags = None
mean_ebv = np.mean(ebv)
if mean_ebv > 0:
log.info(
"Compute a mean delta_color from average E(B-V) = %3.2f based on canonial model star"
% mean_ebv)
# compute a mean delta_color from mean_ebv based on canonial model star
#######################################################################
# will then use this color offset in the model pre-selection
# find canonical f-type model: Teff=6000, logg=4, Fe/H=-1.5
canonical_model = np.argmin((teff - 6000.0)**2 + (logg - 4.0)**2 +
(feh + 1.5)**2)
canonical_model_mags_without_extinction = model_mags[canonical_model]
canonical_model_mags_with_extinction = np.zeros(
canonical_model_mags_without_extinction.shape)
canonical_model_reddened_flux = stdflux[
canonical_model] * dust_transmission(stdwave, mean_ebv)
for index in range(len(model_filters)):
if model_filters[index].startswith('WISE'):
log.warning('not computing stdstar {} mags'.format(
model_filters[index]))
continue
filter_response = load_filter(model_filters[index])
canonical_model_mags_with_extinction[
index] = filter_response.get_ab_magnitude(
canonical_model_reddened_flux * fluxunits, stdwave)
mean_extinction_delta_mags = canonical_model_mags_with_extinction - canonical_model_mags_without_extinction
# LOOP ON STARS TO FIND BEST MODEL
############################################
linear_coefficients = np.zeros((nstars, stdflux.shape[0]))
chi2dof = np.zeros((nstars))
redshift = np.zeros((nstars))
normflux = []
star_colors_array = np.zeros((nstars))
model_colors_array = np.zeros((nstars))
for star in range(nstars):
log.info("finding best model for observed star #%d" % star)
# np.array of wave,flux,ivar,resol
wave = {}
flux = {}
ivar = {}
resolution_data = {}
for camera in frames:
for i, frame in enumerate(frames[camera]):
identifier = "%s-%d" % (camera, i)
wave[identifier] = frame.wave
flux[identifier] = frame.flux[star]
ivar[identifier] = frame.ivar[star]
resolution_data[identifier] = frame.resolution_data[star]
# preselec models based on magnitudes
# compute star color
index1, index2 = get_color_filter_indices(imaging_filters[star],
args.color)
if index1 < 0 or index2 < 0:
log.error("cannot compute '%s' color from %s" %
(color_name, filters))
filter1 = imaging_filters[star][index1]
filter2 = imaging_filters[star][index2]
star_color = imaging_mags[star][index1] - imaging_mags[star][index2]
star_colors_array[star] = star_color
# compute models color
model_index1 = -1
model_index2 = -1
for i, fname in enumerate(model_filters):
if fname == filter1:
model_index1 = i
elif fname == filter2:
model_index2 = i
if model_index1 < 0 or model_index2 < 0:
log.error("cannot compute '%s' model color from %s" %
(color_name, filters))
model_colors = model_mags[:, model_index1] - model_mags[:,
model_index2]
# apply extinction here
# use the colors derived from the cannonical model with the mean ebv of the stars
# and simply apply a scaling factor based on the ebv of this star
# this is sufficiently precise for the broad model pre-selection we are doing here
# the exact reddening of the star to each pre-selected model is
# apply afterwards
if mean_extinction_delta_mags is not None and mean_ebv != 0:
delta_color = (mean_extinction_delta_mags[model_index1] -
mean_extinction_delta_mags[model_index2]
) * ebv[star] / mean_ebv
model_colors += delta_color
log.info(
"Apply a %s-%s color offset = %4.3f to the models for star with E(B-V)=%4.3f"
% (model_filters[model_index1], model_filters[model_index2],
delta_color, ebv[star]))
# selection
selection = np.abs(model_colors - star_color) < args.delta_color
# smallest cube in parameter space including this selection (needed for interpolation)
new_selection = (teff >= np.min(teff[selection])) & (teff <= np.max(
teff[selection]))
new_selection &= (logg >= np.min(logg[selection])) & (logg <= np.max(
logg[selection]))
new_selection &= (feh >= np.min(feh[selection])) & (feh <= np.max(
feh[selection]))
selection = np.where(new_selection)[0]
log.info(
"star#%d fiber #%d, %s = %s-%s = %f, number of pre-selected models = %d/%d"
% (star, starfibers[star], args.color, filter1, filter2,
star_color, selection.size, stdflux.shape[0]))
# apply extinction to selected_models
dust_transmission_of_this_star = dust_transmission(stdwave, ebv[star])
selected_reddened_stdflux = stdflux[
selection] * dust_transmission_of_this_star
coefficients, redshift[star], chi2dof[star] = match_templates(
wave,
flux,
ivar,
resolution_data,
stdwave,
selected_reddened_stdflux,
teff[selection],
logg[selection],
feh[selection],
ncpu=args.ncpu,
z_max=args.z_max,
z_res=args.z_res,
template_error=args.template_error)
linear_coefficients[star, selection] = coefficients
log.info(
'Star Fiber: {0}; TEFF: {1}; LOGG: {2}; FEH: {3}; Redshift: {4}; Chisq/dof: {5}'
.format(starfibers[star], np.inner(teff,
linear_coefficients[star]),
np.inner(logg, linear_coefficients[star]),
np.inner(feh, linear_coefficients[star]), redshift[star],
chi2dof[star]))
# Apply redshift to original spectrum at full resolution
model = np.zeros(stdwave.size)
for i, c in enumerate(linear_coefficients[star]):
if c != 0:
model += c * np.interp(stdwave, stdwave *
(1 + redshift[star]), stdflux[i])
# Apply dust extinction
model *= dust_transmission_of_this_star
# Compute final model color
mag1 = load_filter(model_filters[model_index1]).get_ab_magnitude(
model * fluxunits, stdwave)
mag2 = load_filter(model_filters[model_index2]).get_ab_magnitude(
model * fluxunits, stdwave)
model_colors_array[star] = mag1 - mag2
# Normalize the best model using reported magnitude
normalizedflux = normalize_templates(stdwave, model,
imaging_mags[star],
imaging_filters[star])
normflux.append(normalizedflux)
# Now write the normalized flux for all best models to a file
normflux = np.array(normflux)
data = {}
data['LOGG'] = linear_coefficients.dot(logg)
data['TEFF'] = linear_coefficients.dot(teff)
data['FEH'] = linear_coefficients.dot(feh)
data['CHI2DOF'] = chi2dof
data['REDSHIFT'] = redshift
data['COEFF'] = linear_coefficients
data['DATA_%s' % args.color] = star_colors_array
data['MODEL_%s' % args.color] = model_colors_array
norm_model_file = args.outfile
io.write_stdstar_models(args.outfile, normflux, stdwave, starfibers, data)
def get_skyres(cframes, sub_sky=False, flatten=True):
"""
Args:
cframes: str or list
Single cframe or a list of them
sub_sky: bool, optional
Subtract the sky? This should probably not be done
flatten: bool, optional
Return a flat, 1D array for each variable
combine: bool, optional
combine the individual sky fibers? Median 'smash'
Returns:
wave : ndarray
flux : ndarray
res : ndarray
ivar : ndarray
"""
from lvmspec.io import read_frame
from lvmspec.io.sky import read_sky
from lvmspec.sky import subtract_sky
if isinstance(cframes, list):
all_wave, all_flux, all_res, all_ivar = [], [], [], []
for cframe_file in cframes:
wave, flux, res, ivar = get_skyres(cframe_file, flatten=flatten)
# Save
all_wave.append(wave)
all_flux.append(flux)
all_res.append(res)
all_ivar.append(ivar)
# Concatenate -- Shape is preserved (nfibers, npix)
twave = np.concatenate(all_wave)
tflux = np.concatenate(all_flux)
tres = np.concatenate(all_res)
tivar = np.concatenate(all_ivar)
# Return
return twave, tflux, tres, tivar
cframe = read_frame(cframes)
if cframe.meta['FLAVOR'] in ['flat', 'arc']:
raise ValueError("Bad flavor for exposure: {:s}".format(cframes))
# Sky
sky_file = cframes.replace('cframe', 'sky')
skymodel = read_sky(sky_file)
if sub_sky:
subtract_sky(cframe, skymodel)
# Resid
skyfibers = np.where(cframe.fibermap['OBJTYPE'] == 'SKY')[0]
res = cframe.flux[skyfibers] # Flux calibrated
ivar = cframe.ivar[skyfibers] # Flux calibrated
flux = skymodel.flux[skyfibers] # Residuals; not flux calibrated!
wave = np.outer(np.ones(flux.shape[0]), cframe.wave)
# Combine?
'''
if combine:
res = np.median(res, axis=0)
ivar = np.median(ivar, axis=0)
flux = np.median(flux, axis=0)
wave = np.median(wave, axis=0)
'''
# Return
if flatten:
return wave.flatten(), flux.flatten(), res.flatten(), ivar.flatten()
else:
return wave, flux, res, ivar
def qaframe_from_frame(frame_file,
specprod_dir=None,
make_plots=False,
output_dir=None):
""" Generate a qaframe object from an input frame_file name (and night)
Write QA to disk
Will also make plots if directed
Args:
frame_file: str
specprod_dir: str, optional
make_plots: bool, optional
output_dir: str, optional
Returns:
"""
import glob
import os
from lvmspec.io import read_frame
from lvmspec.io import meta
from lvmspec.io.qa import load_qa_frame, write_qa_frame
from lvmspec.io.frame import search_for_framefile
from lvmspec.io.fiberflat import read_fiberflat
from lvmspec.fiberflat import apply_fiberflat
from lvmspec.qa import qa_plots
from lvmspec.io.sky import read_sky
from lvmspec.io.fluxcalibration import read_flux_calibration
if '/' in frame_file: # If present, assume full path is used here
pass
else: # Find the frame file in the lvmspec hierarchy?
frame_file = search_for_framefile(frame_file)
# Load frame
frame = read_frame(frame_file)
frame_meta = frame.meta
night = frame_meta['NIGHT'].strip()
camera = frame_meta['CAMERA'].strip()
expid = frame_meta['EXPID']
spectro = int(frame_meta['CAMERA'][-1])
if frame_meta['FLAVOR'] in ['flat', 'arc']:
qatype = 'qa_calib'
else:
qatype = 'qa_data'
# Load
qafile = meta.findfile(qatype,
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
outdir=output_dir)
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=expid,
specprod_dir=specprod_dir)
fiberflat = read_fiberflat(fiberflat_fil)
qaframe.run_qa('FIBERFLAT', (frame, fiberflat), clobber=True)
if make_plots:
# Do it
qafig = meta.findfile('qa_flat_fig',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
outdir=output_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=expid,
specprod_dir=specprod_dir)
# Flat field first
dummy_fiberflat_fil = meta.findfile(
'fiberflat',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir) # This is dummy
path, _ = os.path.split(dummy_fiberflat_fil)
fiberflat_files = glob.glob(
os.path.join(path, 'fiberflat-' + camera + '*.fits'))
# Sort and take the first (same as current pipeline)
fiberflat_files.sort()
fiberflat = read_fiberflat(fiberflat_files[0])
apply_fiberflat(frame, fiberflat)
# Load sky model and run
try:
skymodel = read_sky(sky_fil)
except FileNotFoundError:
warnings.warn(
"Sky file {:s} not found. Skipping..".format(sky_fil))
else:
qaframe.run_qa('SKYSUB', (frame, skymodel))
if make_plots:
qafig = meta.findfile('qa_sky_fig',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
outdir=output_dir)
qafig2 = meta.findfile('qa_skychi_fig',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
outdir=output_dir)
qa_plots.frame_skyres(qafig, frame, skymodel, qaframe)
#qa_plots.frame_skychi(qafig2, frame, skymodel, qaframe)
# FluxCalib QA
if qatype == 'qa_data':
# Standard stars
stdstar_fil = meta.findfile('stdstars',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
spectrograph=spectro)
# try:
# model_tuple=read_stdstar_models(stdstar_fil)
# except FileNotFoundError:
# warnings.warn("Standard star file {:s} not found. Skipping..".format(stdstar_fil))
# else:
flux_fil = meta.findfile('calib',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir)
try:
fluxcalib = read_flux_calibration(flux_fil)
except FileNotFoundError:
warnings.warn(
"Flux file {:s} not found. Skipping..".format(flux_fil))
else:
qaframe.run_qa(
'FLUXCALIB',
(frame, fluxcalib)) # , model_tuple))#, indiv_stars))
if make_plots:
qafig = meta.findfile('qa_flux_fig',
night=night,
camera=camera,
expid=expid,
specprod_dir=specprod_dir,
outdir=output_dir)
qa_plots.frame_fluxcalib(qafig, qaframe, frame,
fluxcalib) # , model_tuple)
# Write
write_qa_frame(qafile, qaframe, verbose=True)
return qaframe