def test_wavelengths(self):
if self.data_unavailable:
self.skipTest("Failed to download test data.")
# Read flat
flat_hdu = fits.open(self.testflat)
header = flat_hdu[0].header
flat = flat_hdu[0].data
ny = flat.shape[0]
# Find fibers (necessary)
xpk, ypos, cut = desiboot.find_fiber_peaks(flat)
# Trace
xset, xerr = desiboot.trace_crude_init(flat, xpk, ypos)
xfit, fdicts = desiboot.fit_traces(xset, xerr)
# Test fiber_gauss_old for coverage
gauss = desiboot.fiber_gauss_old(flat, xfit, xerr)
# Gaussian
gauss = desiboot.fiber_gauss(flat, xfit, xerr)
# Read arc
arc_hdu = fits.open(self.testarc)
arc = arc_hdu[0].data
arc_ivar = np.ones(arc.shape)
# Extract arc spectra (one per fiber)
all_spec = desiboot.extract_sngfibers_gaussianpsf(arc, arc_ivar, xfit, gauss)
# Line list
camera = header['CAMERA']
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera,llist)
#
all_wv_soln = []
for ii in range(1):
spec = all_spec[:,ii]
# Find Lines
pixpk, flux = desiboot.find_arc_lines(spec)
id_dict = {"pixpk":pixpk,"flux":flux}
# Match a set of 5 gd_lines to detected lines
desiboot.id_arc_lines_using_triplets(id_dict,gd_lines,dlamb)
# Now the rest
desiboot.id_remainder(id_dict, llist, deg=3)
# Final fit wave vs. pix too
final_fit, mask = dufits.iter_fit(np.array(id_dict['id_wave']),
np.array(id_dict['id_pix']),
'polynomial', 3, xmin=0., xmax=1.)
rms = np.sqrt(np.mean((dufits.func_val(
np.array(id_dict['id_wave'])[mask==0], final_fit)-
np.array(id_dict['id_pix'])[mask==0])**2))
final_fit_pix,mask2 = dufits.iter_fit(np.array(id_dict['id_pix']),
np.array(id_dict['id_wave']),
'legendre',4, niter=5)
# Save
id_dict['final_fit'] = final_fit
id_dict['rms'] = rms
id_dict['final_fit_pix'] = final_fit_pix
id_dict['wave_min'] = dufits.func_val(0, final_fit_pix)
id_dict['wave_max'] = dufits.func_val(ny-1, final_fit_pix)
id_dict['mask'] = mask
all_wv_soln.append(id_dict)
self.assertLess(all_wv_soln[0]['rms'], 0.25)
def process_arc(frame, linelist=None, npoly=2, nbins=2, domain=None):
"""
frame: desispec.frame.Frame object, preumably resolution not evaluated.
linelist: line list to fit
npoly: polynomial order for sigma expansion
nbins: no of bins for the half of the fitting window
return: coefficients of the polynomial expansion
"""
nspec = frame.flux.shape[0]
if linelist is None:
camera = frame.meta["CAMERA"]
#- load arc lines
from desispec.bootcalib import load_arcline_list, load_gdarc_lines, find_arc_lines
llist = load_arcline_list(camera)
dlamb, gd_lines = load_gdarc_lines(camera, llist)
linelist = gd_lines
#linelist=[5854.1101,6404.018,7034.352,7440.9469] #- not final
log.info(
"No line list configured. Fitting for lines {}".format(linelist))
coeffs = np.zeros((nspec, npoly + 1)) #- coeffs array
#- amend line list to only include lines in given wavelength range
wave = frame.wave
if wave[0] >= linelist[0]:
noline_ind_lo = np.where(np.array(linelist) <= wave[0])
linelist = linelist[np.max(noline_ind_lo[0]) + 1:len(linelist) - 1]
log.info(
"First {} line(s) outside wavelength range, skipping these".format(
len(noline_ind_lo[0])))
if wave[len(wave) - 1] <= linelist[len(linelist) - 1]:
noline_ind_hi = np.where(np.array(linelist) >= wave[len(wave) - 1])
linelist = linelist[0:np.min(noline_ind_hi[0]) - 1]
log.info(
"Last {} line(s) outside wavelength range, skipping these".format(
len(noline_ind_hi[0])))
for spec in range(nspec):
flux = frame.flux[spec]
ivar = frame.ivar[spec]
meanwaves, emeanwaves, sigmas, esigmas = sigmas_from_arc(wave,
flux,
ivar,
linelist,
n=nbins)
if domain is None:
domain = (np.min(wave), np.max(wave))
thislegfit = fit_wsigmas(meanwaves,
sigmas,
esigmas,
domain=domain,
npoly=npoly)
coeffs[spec] = thislegfit.coef
return coeffs
def process_arc(frame, linelist=None, npoly=2, nbins=2, domain=None):
"""
frame: desispec.frame.Frame object, preumably resolution not evaluated.
linelist: line list to fit
npoly: polynomial order for sigma expansion
nbins: no of bins for the half of the fitting window
return: coefficients of the polynomial expansion
"""
nspec = frame.flux.shape[0]
if linelist is None:
camera = frame.meta["CAMERA"]
#- load arc lines
from desispec.bootcalib import load_arcline_list, load_gdarc_lines, find_arc_lines
llist = load_arcline_list(camera)
dlamb, gd_lines = load_gdarc_lines(camera, llist)
linelist = gd_lines
#linelist=[5854.1101,6404.018,7034.352,7440.9469] #- not final
log.info(
"No line list configured. Fitting for lines {}".format(linelist))
coeffs = np.zeros((nspec, npoly + 1)) #- coeffs array
for spec in range(nspec):
wave = frame.wave
flux = frame.flux[spec]
ivar = frame.ivar[spec]
meanwaves, emeanwaves, sigmas, esigmas = sigmas_from_arc(wave,
flux,
ivar,
linelist,
n=nbins)
if domain is None:
domain = (np.min(wave), np.max(wave))
thislegfit = fit_wsigmas(meanwaves,
sigmas,
esigmas,
domain=domain,
npoly=npoly)
coeffs[spec] = thislegfit.coef
return coeffs
def test_load_gdarc_lines(self):
for camera in ['b', 'r', 'z']:
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera,llist)
def test_wavelengths(self):
if self.data_unavailable:
self.skipTest("Failed to download test data.")
# Read flat
flat_hdu = fits.open(self.testflat)
header = flat_hdu[0].header
flat = flat_hdu[0].data
ny = flat.shape[0]
# Find fibers (necessary)
xpk, ypos, cut = desiboot.find_fiber_peaks(flat)
# Trace
xset, xerr = desiboot.trace_crude_init(flat, xpk, ypos)
xfit, fdicts = desiboot.fit_traces(xset, xerr)
# Test fiber_gauss_old for coverage
gauss = desiboot.fiber_gauss_old(flat, xfit, xerr)
# Gaussian
gauss = desiboot.fiber_gauss(flat, xfit, xerr)
# Read arc
arc_hdu = fits.open(self.testarc)
arc = arc_hdu[0].data
arc_ivar = np.ones(arc.shape)
# Extract arc spectra (one per fiber)
all_spec = desiboot.extract_sngfibers_gaussianpsf(
arc, arc_ivar, xfit, gauss)
# Line list
camera = header['CAMERA']
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera, llist)
#
all_wv_soln = []
for ii in range(1):
spec = all_spec[:, ii]
# Find Lines
pixpk, flux = desiboot.find_arc_lines(spec)
id_dict = {"pixpk": pixpk, "flux": flux}
# Match a set of 5 gd_lines to detected lines
desiboot.id_arc_lines_using_triplets(id_dict, gd_lines, dlamb)
# Now the rest
desiboot.id_remainder(id_dict, llist, deg=3)
# Final fit wave vs. pix too
final_fit, mask = dufits.iter_fit(np.array(id_dict['id_wave']),
np.array(id_dict['id_pix']),
'polynomial',
3,
xmin=0.,
xmax=1.)
rms = np.sqrt(
np.mean((dufits.func_val(
np.array(id_dict['id_wave'])[mask == 0], final_fit) -
np.array(id_dict['id_pix'])[mask == 0])**2))
final_fit_pix, mask2 = dufits.iter_fit(np.array(id_dict['id_pix']),
np.array(
id_dict['id_wave']),
'legendre',
4,
niter=5)
# Save
id_dict['final_fit'] = final_fit
id_dict['rms'] = rms
id_dict['final_fit_pix'] = final_fit_pix
id_dict['wave_min'] = dufits.func_val(0, final_fit_pix)
id_dict['wave_max'] = dufits.func_val(ny - 1, final_fit_pix)
id_dict['mask'] = mask
all_wv_soln.append(id_dict)
self.assertLess(all_wv_soln[0]['rms'], 0.25)
def test_load_gdarc_lines(self):
for camera in ['b', 'r', 'z']:
llist = desiboot.load_arcline_list(camera)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera, llist)
def main(args):
log=get_logger()
log.info("Starting")
if args.triplet_matching :
log.warning("triplet_matching option deprecated, this algorithm is now used for all cases")
lamps=None
if args.lamps :
lamps=np.array(args.lamps.split(","))
log.info("Using lamps = %s"%str(lamps))
else :
log.info("Using default set of lamps")
if args.fiberflat is not None and args.contfile is None :
args.contfile=args.fiberflat
log.warning("Please use --contfile instead of --fiberflat")
if (args.psffile is None) and (args.contfile is None):
raise IOError("Must provide either a PSF file or a contfile")
# Start QA
try:
pp = PdfPages(args.qafile)
except ValueError:
QA = False
else:
QA = True
contfile_header = None
if args.psffile is None:
###########
# Read continuum
cont_hdu = fits.open(args.contfile)
contfile_header = cont_hdu[0].header
header = cont_hdu[0].header
if len(cont_hdu)>=3 :
cont = cont_hdu[0].data*(cont_hdu[1].data>0)*(cont_hdu[2].data==0)
else :
cont = cont_hdu[0].data
log.warning("found only %d HDU in cont, do not use ivar"%len(cont_hdu))
ny = cont.shape[0]
###########
# Find fibers
log.info("Finding the fibers")
xpk, ypos, cut = desiboot.find_fiber_peaks(cont)
if QA:
desiboot.qa_fiber_peaks(xpk, cut, pp)
# Test?
if args.test:
log.warning("cutting down fibers for testing..")
#xpk = xpk[0:100]
xpk = xpk[0:50]
#xpk = xpk[0:5]
###########
# Trace the fiber cont spectra
log.info("Tracing the continuum spectra")
# Crude first
log.info("Crudely..")
xset, xerr = desiboot.trace_crude_init(cont,xpk,ypos)
# Polynomial fits
log.info("Fitting the traces")
xfit, fdicts = desiboot.fit_traces(xset,xerr)
# QA
if QA:
desiboot.qa_fiber_Dx(xfit, fdicts, pp)
###########
# Model the PSF with Gaussian
log.info("Modeling the PSF with a Gaussian, be patient..")
gauss = desiboot.fiber_gauss(cont,xfit,xerr)
if QA:
desiboot.qa_fiber_gauss(gauss, pp)
XCOEFF = None
else: # Load PSF file and generate trace info
log.warning("Not tracing the continuum. Using the PSF file.")
psf_hdu = fits.open(args.psffile)
psf_head = psf_hdu[0].header
# Gaussians
gauss = psf_hdu[2].data
# Traces
WAVEMIN = psf_head['WAVEMIN']
WAVEMAX = psf_head['WAVEMAX']
XCOEFF = psf_hdu[0].data
xfit = None
fdicts = None
arc_header = None
# ARCS
if not args.trace_only:
###########
# Read arc
log.info("Reading arc")
arc_hdu = fits.open(args.arcfile)
arc_header = arc_hdu[0].header
if len(arc_hdu)>=3 :
# set to zero ivar of masked pixels, force positive or null ivar
arc_ivar = arc_hdu[1].data*(arc_hdu[2].data==0)*(arc_hdu[1].data>0)
# and mask pixels below -5 sigma (cures unmasked dead columns in sims.)
arc_ivar *= (arc_hdu[0].data*np.sqrt(arc_hdu[1].data)>-5.)
# set to zero pixel values with null ivar
arc = arc_hdu[0].data*(arc_ivar>0)
else :
arc = arc_hdu[0].data
arc_ivar = np.ones(arc.shape)
log.warning("found only %d HDU in arc, do not use ivar"%len(arc_hdu))
header = arc_hdu[0].header
ny = arc.shape[0]
#####################################
# Extract arc spectra (one per fiber)
log.info("Extracting arcs")
if xfit is None:
wv_array = np.linspace(WAVEMIN, WAVEMAX, num=arc.shape[0])
nfiber = XCOEFF.shape[0]
ncoeff = XCOEFF.shape[1]
xfit = np.zeros((arc.shape[0], nfiber))
# Generate a fit_dict
fit_dict = dufits.mk_fit_dict(XCOEFF[:,0], ncoeff, 'legendre', WAVEMIN, WAVEMAX)
for ii in range(nfiber):
fit_dict['coeff'] = XCOEFF[ii,:]
xfit[:,ii] = dufits.func_val(wv_array, fit_dict)
all_spec = desiboot.extract_sngfibers_gaussianpsf(arc, arc_ivar, xfit, gauss)
############################
# Line list
camera = header['CAMERA'].lower()
log.info("Loading line list")
llist = desiboot.load_arcline_list(camera,vacuum=True,lamps=lamps)
dlamb, gd_lines = desiboot.load_gdarc_lines(camera,llist,vacuum=True,lamps=lamps,good_lines_filename=args.good_lines)
#####################################
# Loop to solve for wavelengths
all_wv_soln = []
all_dlamb = []
debug=False
id_dict_of_fibers=[]
# first loop to find arc lines and do a first matching
for ii in range(all_spec.shape[1]):
spec = all_spec[:,ii]
id_dict={}
id_dict["fiber"] = ii
id_dict["status"] = "none"
id_dict['id_pix'] = []
id_dict['id_idx'] = []
id_dict['id_wave'] = []
pixpk, flux = desiboot.find_arc_lines(spec)
id_dict["pixpk"] = pixpk
id_dict["flux"] = flux
try:
desiboot.id_arc_lines_using_triplets(id_dict, gd_lines, dlamb,ntrack=args.ntrack,nmax=args.nmax,toler=args.toler)
except :
log.warning(sys.exc_info())
log.warning("fiber {:d} ID_ARC failed".format(ii))
id_dict['status'] = "failed"
id_dict_of_fibers.append(id_dict)
continue
# Add lines
if len(id_dict['pixpk'])>len(id_dict['id_pix']) :
desiboot.id_remainder(id_dict, llist, deg=args.legendre_degree)
log.info("Fiber #{:d} n_match={:d} n_detec={:d}".format(ii,len(id_dict['id_pix']),len(id_dict['pixpk'])))
# Save
id_dict_of_fibers.append(id_dict)
# now record the list of waves identified in several fibers
matched_lines=np.array([])
for ii in range(all_spec.shape[1]):
matched_lines = np.append(matched_lines,id_dict_of_fibers[ii]['id_wave'])
matched_lines = np.unique(matched_lines)
number_of_detections = []
for line in matched_lines :
ndet=0
for ii in range(all_spec.shape[1]):
if np.sum(id_dict_of_fibers[ii]['id_wave']==line) >0 :
ndet += 1
print(line,"ndet=",ndet)
number_of_detections.append(ndet)
# choose which lines are ok and
# ok if 5 detections (coincidental error very low)
min_number_of_detections=min(5,all_spec.shape[1])
number_of_detections=np.array(number_of_detections)
good_matched_lines = matched_lines[number_of_detections>=min_number_of_detections]
bad_matched_lines = matched_lines[number_of_detections<min_number_of_detections]
log.info("good matched lines = {:s}".format(str(good_matched_lines)))
log.info("bad matched lines = {:s}".format(str(bad_matched_lines)))
# loop again on all fibers
for ii in range(all_spec.shape[1]):
spec = all_spec[:,ii]
id_dict = id_dict_of_fibers[ii]
n_matched_lines=len(id_dict['id_wave'])
n_detected_lines=len(id_dict['pixpk'])
# did we find any bad line for this fiber?
n_bad = np.intersect1d(id_dict['id_wave'],bad_matched_lines).size
# how many good lines did we find
n_good = np.intersect1d(id_dict['id_wave'],good_matched_lines).size
if id_dict['status']=="ok" and ( n_bad>0 or (n_good < good_matched_lines.size-1 and n_good<30) ) and n_good<40 :
log.info("Try to refit fiber {:d} with n_bad={:d} and n_good={:d} when n_good_all={:d} n_detec={:d}".format(ii,n_bad,n_good,good_matched_lines.size,n_detected_lines))
try:
desiboot.id_arc_lines_using_triplets(id_dict, good_matched_lines, dlamb,ntrack=args.ntrack,nmax=args.nmax)
except:
log.warning(sys.exc_info())
log.warning("ID_ARC failed on fiber {:d}".format(ii))
id_dict["status"]="failed"
if id_dict['status']=="ok" and len(id_dict['pixpk'])>len(id_dict['id_pix']) :
desiboot.id_remainder(id_dict, llist, deg=args.legendre_degree)
else :
log.info("Do not refit fiber {:d} with n_bad={:d} and n_good={:d} when n_good_all={:d} n_detec={:d}".format(ii,n_bad,n_good,good_matched_lines.size,n_detected_lines))
if id_dict['status'] != 'ok':
all_wv_soln.append(id_dict)
all_dlamb.append(0.)
log.warning("Fiber #{:d} failed, no final fit".format(ii))
continue
# Final fit wave vs. pix too
id_wave=np.array(id_dict['id_wave'])
id_pix=np.array(id_dict['id_pix'])
deg=max(1,min(args.legendre_degree,id_wave.size-2))
final_fit, mask = dufits.iter_fit(id_wave,id_pix, 'polynomial', deg, xmin=0., xmax=1., sig_rej=3.)
rms = np.sqrt(np.mean((dufits.func_val(id_wave[mask==0], final_fit)-id_pix[mask==0])**2))
final_fit_pix,mask2 = dufits.iter_fit(id_pix[mask==0],id_wave[mask==0],'legendre',deg , sig_rej=100000000.)
rms_pix = np.sqrt(np.mean((dufits.func_val(id_pix[mask==0], final_fit_pix)-id_wave[mask==0])**2))
# Append
wave = dufits.func_val(np.arange(spec.size),final_fit_pix)
idlamb = np.median(np.abs(wave-np.roll(wave,1)))
all_dlamb.append(idlamb)
# Save
id_dict['final_fit'] = final_fit
id_dict['rms'] = rms
id_dict['final_fit_pix'] = final_fit_pix
id_dict['wave_min'] = dufits.func_val(0,final_fit_pix)
id_dict['wave_max'] = dufits.func_val(ny-1,final_fit_pix)
id_dict['mask'] = mask
log.info("Fiber #{:d} final fit rms(y->wave) = {:g} A ; rms(wave->y) = {:g} pix ; nlines = {:d}".format(ii,rms,rms_pix,id_pix.size))
all_wv_soln.append(id_dict)
if QA:
desiboot.qa_arc_spec(all_spec, all_wv_soln, pp)
desiboot.qa_fiber_arcrms(all_wv_soln, pp)
desiboot.qa_fiber_dlamb(all_spec, all_wv_soln, pp)
else:
all_wv_soln = None
###########
# Write PSF file
log.info("Writing PSF file")
desiboot.write_psf(args.outfile, xfit, fdicts, gauss, all_wv_soln, legendre_deg=args.legendre_degree , without_arc=args.trace_only,
XCOEFF=XCOEFF,fiberflat_header=contfile_header,arc_header=arc_header)
log.info("Successfully wrote {:s}".format(args.outfile))
if ( not args.trace_only ) and args.out_line_list :
log.info("Writing list of lines found in {:s}".format(args.out_line_list))
desiboot.write_line_list(args.out_line_list,all_wv_soln,llist)
log.info("Successfully wrote {:s}".format(args.out_line_list))
###########
# All done
if QA:
log.info("Successfully wrote {:s}".format(args.qafile))
pp.close()
log.info("end")
return
def process_arc(qframe, xytraceset, linelist=None, npoly=2, nbins=2):
"""
qframe: desispec.qframe.QFrame object
xytraceset : desispec.xytraceset.XYTraceSet object
linelist: line list to fit
npoly: polynomial order for sigma expansion
nbins: no of bins for the half of the fitting window
return: xytraceset (with ysig vs wave)
"""
log = get_logger()
if linelist is None:
if qframe.meta is None or "CAMERA" not in qframe.meta:
log.error(
"no information about camera in qframe so I don't know which lines to use"
)
raise RuntimeError(
"no information about camera in qframe so I don't know which lines to use"
)
camera = qframe.meta["CAMERA"]
#- load arc lines
from desispec.bootcalib import load_arcline_list, load_gdarc_lines, find_arc_lines
llist = load_arcline_list(camera)
dlamb, gd_lines = load_gdarc_lines(camera, llist)
linelist = gd_lines
log.info(
"No line list configured. Fitting for lines {}".format(linelist))
tset = xytraceset
assert (qframe.nspec == tset.nspec)
tset.ysig_vs_wave_traceset = TraceSet(np.zeros((tset.nspec, npoly + 1)),
[tset.wavemin, tset.wavemax])
for spec in range(tset.nspec):
spec_wave = qframe.wave[spec]
spec_linelist = linelist[(linelist > spec_wave[0])
& (linelist < spec_wave[-1])]
meanwaves, emeanwaves, sigmas, esigmas = sigmas_from_arc(
spec_wave,
qframe.flux[spec],
qframe.ivar[spec],
spec_linelist,
n=nbins)
# convert from wavelength A unit to CCD pixel for consistency with specex PSF
y = tset.y_vs_wave(spec, spec_wave)
dydw = np.interp(meanwaves, spec_wave,
np.gradient(y) / np.gradient(spec_wave))
sigmas *= dydw # A -> pixels
esigmas *= dydw # A -> pixels
ok = (sigmas > 0) & (esigmas > 0)
try:
thislegfit = Legendre.fit(meanwaves[ok],
sigmas[ok],
npoly,
domain=[tset.wavemin, tset.wavemax],
w=1. / esigmas[ok]**2)
tset.ysig_vs_wave_traceset._coeff[spec] = thislegfit.coef
except:
log.error("legfit of psf width failed for spec {}".format(spec))
wave = np.linspace(tset.wavemin, tset.wavemax, 20)
#plt.plot(wave,tset.ysig_vs_wave(spec,wave))
#plt.show()
return xytraceset
def process_arc(frame, linelist=None, npoly=2, nbins=2, domain=None):
"""
frame: desispec.frame.Frame object, preumably resolution not evaluated.
linelist: line list to fit
npoly: polynomial order for sigma expansion
nbins: no of bins for the half of the fitting window
return: coefficients of the polynomial expansion
"""
if domain is None:
raise ValueError("domain must be given in process_arc")
nspec = frame.flux.shape[0]
if linelist is None:
camera = frame.meta["CAMERA"]
#- load arc lines
from desispec.bootcalib import load_arcline_list, load_gdarc_lines, find_arc_lines
llist = load_arcline_list(camera)
dlamb, gd_lines = load_gdarc_lines(camera, llist)
linelist = gd_lines
#linelist=[5854.1101,6404.018,7034.352,7440.9469] #- not final
log.info(
"No line list configured. Fitting for lines {}".format(linelist))
coeffs = np.zeros((nspec, npoly + 1)) #- coeffs array
for spec in range(nspec):
#- Allow arc processing to use either QL or QP extraction
if isinstance(frame.wave[0], float):
wave = frame.wave
else:
wave = frame.wave[spec]
flux = frame.flux[spec]
ivar = frame.ivar[spec]
#- amend line list to only include lines in given wavelength range
if wave[0] >= linelist[0]:
noline_ind_lo = np.where(np.array(linelist) <= wave[0])
linelist = linelist[np.max(noline_ind_lo[0]) + 1:len(linelist) - 1]
log.info(
"First {} line(s) outside wavelength range, skipping these".
format(len(noline_ind_lo[0])))
if wave[len(wave) - 1] <= linelist[len(linelist) - 1]:
noline_ind_hi = np.where(np.array(linelist) >= wave[len(wave) - 1])
linelist = linelist[0:np.min(noline_ind_hi[0]) - 1]
log.info(
"Last {} line(s) outside wavelength range, skipping these".
format(len(noline_ind_hi[0])))
meanwaves, emeanwaves, sigmas, esigmas = sigmas_from_arc(wave,
flux,
ivar,
linelist,
n=nbins)
if domain is None:
domain = (np.min(wave), np.max(wave))
# RS: if Gaussian couldn't be fit to a line, don't do legendre fit for fiber
if 0. in sigmas or 0. in esigmas:
pass
else:
try:
thislegfit = fit_wsigmas(meanwaves,
sigmas,
esigmas,
domain=domain,
npoly=npoly)
coeffs[spec] = thislegfit.coef
except:
pass
# need to return the wavemin and wavemax of the fit
return coeffs, domain[0], domain[1]
def process_arc(frame,linelist=None,npoly=2,nbins=2,domain=None):
"""
frame: desispec.frame.Frame object, preumably resolution not evaluated.
linelist: line list to fit
npoly: polynomial order for sigma expansion
nbins: no of bins for the half of the fitting window
return: coefficients of the polynomial expansion
"""
if domain is None :
raise ValueError("domain must be given in process_arc")
nspec=frame.flux.shape[0]
if linelist is None:
camera=frame.meta["CAMERA"]
#- load arc lines
from desispec.bootcalib import load_arcline_list, load_gdarc_lines,find_arc_lines
llist=load_arcline_list(camera)
dlamb,gd_lines=load_gdarc_lines(camera,llist)
linelist=gd_lines
#linelist=[5854.1101,6404.018,7034.352,7440.9469] #- not final
log.info("No line list configured. Fitting for lines {}".format(linelist))
coeffs=np.zeros((nspec,npoly+1)) #- coeffs array
for spec in range(nspec):
#- Allow arc processing to use either QL or QP extraction
if isinstance(frame.wave[0],float):
wave=frame.wave
else:
wave=frame.wave[spec]
flux=frame.flux[spec]
ivar=frame.ivar[spec]
#- amend line list to only include lines in given wavelength range
if wave[0] >= linelist[0]:
noline_ind_lo=np.where(np.array(linelist)<=wave[0])
linelist=linelist[np.max(noline_ind_lo[0])+1:len(linelist)-1]
log.info("First {} line(s) outside wavelength range, skipping these".format(len(noline_ind_lo[0])))
if wave[len(wave)-1] <= linelist[len(linelist)-1]:
noline_ind_hi=np.where(np.array(linelist)>=wave[len(wave)-1])
linelist=linelist[0:np.min(noline_ind_hi[0])-1]
log.info("Last {} line(s) outside wavelength range, skipping these".format(len(noline_ind_hi[0])))
meanwaves,emeanwaves,sigmas,esigmas=sigmas_from_arc(wave,flux,ivar,linelist,n=nbins)
if domain is None:
domain=(np.min(wave),np.max(wave))
# RS: if Gaussian couldn't be fit to a line, don't do legendre fit for fiber
if 0. in sigmas or 0. in esigmas:
pass
else:
try:
thislegfit=fit_wsigmas(meanwaves,sigmas,esigmas,domain=domain,npoly=npoly)
coeffs[spec]=thislegfit.coef
except:
pass
# need to return the wavemin and wavemax of the fit
return coeffs,domain[0],domain[1]