def invert(self, domain=None, coeff=None, deg=None):
"""
Utility to return a traceset modeling x vs. y instead of y vs. x
"""
if domain is None:
domain = [self.wmin, self.wmax]
ispec = np.arange(self.nspec) # Doing for all spectra
if coeff is None:
coeff = self.ycoeff # doing y-wavelength map
ytmp = list()
for ii in ispec:
fit_dict = dufits.mk_fit_dict(coeff[ii, :], coeff.shape[1],
'legendre', domain[0], domain[1])
xtmp = np.array((domain[0], domain[1]))
yfit = dufits.func_val(xtmp, fit_dict)
ytmp.append(yfit)
ymin = np.min(ytmp)
ymax = np.max(ytmp)
x = np.linspace(domain[0], domain[1], 1000)
if deg is None:
deg = self.ncoeff + 2
#- Now get the coefficients for inverse mapping
c = np.zeros((coeff.shape[0], deg + 1))
for ii in ispec:
fit_dict = dufits.mk_fit_dict(coeff[ii, :], coeff.shape,
'legendre', domain[0], domain[1])
y = dufits.func_val(x, fit_dict)
yy = 2.0 * (y - ymin) / (ymax - ymin) - 1.0
c[ii] = legfit(yy, x, deg)
return c, ymin, ymax
def invert(self, domain=None, coeff=None, deg=None):
"""
Utility to return a traceset modeling x vs. y instead of y vs. x
"""
if domain is None:
domain=[self.wmin,self.wmax]
ispec=np.arange(self.nspec) # Doing for all spectra
if coeff is None:
coeff=self.ycoeff # doing y-wavelength map
ytmp=list()
for ii in ispec:
fit_dict=dufits.mk_fit_dict(coeff[ii,:],coeff.shape[1],'legendre',domain[0],domain[1])
xtmp=np.array((domain[0],domain[1]))
yfit = dufits.func_val(xtmp, fit_dict)
ytmp.append(yfit)
ymin = np.min(ytmp)
ymax = np.max(ytmp)
x = np.linspace(domain[0], domain[1], 1000)
if deg is None:
deg = self.ncoeff+2
#- Now get the coefficients for inverse mapping
c = np.zeros((coeff.shape[0], deg+1))
for ii in ispec:
fit_dict=dufits.mk_fit_dict(coeff[ii,:],coeff.shape,'legendre',domain[0],domain[1])
y = dufits.func_val(x,fit_dict)
yy = 2.0 * (y-ymin) / (ymax-ymin) - 1.0
c[ii] = legfit(yy, x, deg)
return c,ymin,ymax
def y(self,ispec=None,wavelength=None):
"""
returns CCD y centroids for the spectra
ispec can be None, scalar or a vector
wavelength can be a vector but not allowing None #- similar as in specter.psf.PSF.y
"""
if wavelength is None:
raise ValueError, "PSF.y requires wavelength 1D vector"
wavelength = np.asarray(wavelength)
if ispec is None:
ispec=np.arange(self.nspec)
y=list()
for ii in ispec:
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
yfit=dufits.func_val(wavelength,fit_dicty)
y.append(yfit)
return np.array(y)
if isinstance(ispec,(np.ndarray,list,tuple)):
y=list()
for ii in ispec:
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
yfit=dufits.func_val(wavelength,fit_dicty)
y.append(yfit)
return np.array(y)
if isinstance(ispec,int): # int ispec
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ispec],self.ncoeff,'legendre',self.wmin,self.wmax)
y=dufits.func_val(wavelength,fit_dicty)
return np.array(y)
def wavelength(self, ispec=None, y=None):
"""
returns wavelength evaluated at y
"""
if y is None:
y = np.arange(0, self.npix_y)
if ispec is None:
ispec = np.arange(self.nspec)
c = self.icoeff
ymin = self.ymin
ymax = self.ymax
if isinstance(ispec, numbers.Integral):
new_dict = dufits.mk_fit_dict(c[ispec, :], c[ispec, :].shape,
'legendre', ymin, ymax)
wfit = dufits.func_val(y, new_dict)
return wfit
else:
ww = list()
for ii in ispec:
new_dict = dufits.mk_fit_dict(c[ii, :], c[ii, :].shape,
'legendre', ymin, ymax)
wfit = dufits.func_val(y, new_dict)
ww.append(wfit)
return np.array(ww)
def y(self,ispec=None,wavelength=None):
"""
returns CCD y centroids for the spectra
ispec can be None, scalar or a vector
wavelength can be a vector but not allowing None #- similar as in specter.psf.PSF.y
"""
if wavelength is None:
raise ValueError("PSF.y requires wavelength 1D vector")
wavelength = np.asarray(wavelength)
if ispec is None:
ispec=np.arange(self.nspec)
y=list()
for ii in ispec:
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
yfit=dufits.func_val(wavelength,fit_dicty)
y.append(yfit)
return np.array(y)
if isinstance(ispec,(np.ndarray,list,tuple)):
y=list()
for ii in ispec:
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
yfit=dufits.func_val(wavelength,fit_dicty)
y.append(yfit)
return np.array(y)
if isinstance(ispec, numbers.Integral): # int ispec
fit_dicty=dufits.mk_fit_dict(self.ycoeff[ispec],self.ncoeff,'legendre',self.wmin,self.wmax)
y=dufits.func_val(wavelength,fit_dicty)
return np.array(y)
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 x(self, ispec=None, wavelength=None):
"""
returns CCD x centroids for the spectra
ispec can be None, scalar or a vector
wavelength can be None or a vector
"""
if wavelength is None:
#- ispec = None -> all the spectra
if ispec is None:
ispec = np.arange(self.nspec)
x = list()
#x=np.array((len(ispec),len(wavelength)))
for ii in ispec:
wave = self.wavelength(ii)
fit_dictx = dufits.mk_fit_dict(self.xcoeff[ii],
self.ncoeff, 'legendre',
self.wmin, self.wmax)
xfit = dufits.func_val(wave, fit_dictx)
x.append(xfit)
return np.array(x)
if isinstance(ispec, (np.ndarray, list, tuple)):
x = list()
for ii in ispec:
wave = self.wavelength(ii)
fit_dictx = dufits.mk_fit_dict(self.xcoeff[ii],
self.ncoeff, 'legendre',
self.wmin, self.wmax)
xfit = dufits.func_val(wave, fit_dictx)
x.append(xfit)
return np.array(x)
else: # int ispec
wave = self.wavelength(ispec)
fit_dictx = dufits.mk_fit_dict(self.xcoeff[ispec], self.ncoeff,
'legendre', self.wmin,
self.wmax)
x = dufits.func_val(wave, fit_dictx)
return np.array(x)
#- wavelength not None but a scalar or 1D-vector here and below
wavelength = np.asarray(wavelength)
if isinstance(ispec, numbers.Integral):
fit_dictx = dufits.mk_fit_dict(self.xcoeff[ispec], self.ncoeff,
'legendre', self.wmin, self.wmax)
x = dufits.func_val(wavelength, fit_dictx)
return np.array(x)
if ispec is None:
ispec = np.arange(self.nspec)
x = list()
for ii in ispec: #- for a None or a np.ndarray or anything that can be iterated case
fit_dictx = dufits.mk_fit_dict(self.xcoeff[ii], self.ncoeff,
'legendre', self.wmin, self.wmax)
xfit = dufits.func_val(wavelength, fit_dictx)
x.append(xfit)
return np.array(x)
def x(self,ispec=None,wavelength=None):
"""
returns CCD x centroids for the spectra
ispec can be None, scalar or a vector
wavelength can be None or a vector
"""
if wavelength is None:
#- ispec = None -> all the spectra
if ispec is None:
ispec=np.arange(self.nspec)
wavelength=self.wavelength
x=list()
for ii in ispec:
wave=self.wavelength(ii)
fit_dictx=dufits.mk_fit_dict(self.xcoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
xfit=dufits.func_val(wave,fit_dictx)
x.append(xfit)
return np.array(x)
if isinstance(ispec,(np.ndarray,list,tuple)):
x=list()
for ii in ispec:
wave=self.wavelength(ii)
fit_dictx=dufits.mk_fit_dict(self.xcoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
xfit=dufits.func_val(wave,fit_dictx)
x.append(xfit)
return np.array(x)
else: # int ispec
wave=self.wavelength(ispec)
fit_dictx=dufits.mk_fit_dict(self.xcoeff[ispec],self.ncoeff,'legendre',self.wmin,self.wmax)
x=dufits.func_val(wave,fit_dictx)
return np.array(x)
#- wavelength not None but a scalar or 1D-vector here and below
wavelength = np.asarray(wavelength)
if isinstance(ispec,int):
fit_dictx=dufits.mk_fit_dict(self.xcoeff[ispec],self.ncoeff,'legendre',self.wmin,self.wmax)
x=dufits.func_val(wavelength,fit_dictx)
return np.array(x)
if ispec is None:
ispec=np.arange(self.nspec)
x=list()
for ii in ispec: #- for a None or a np.ndarray or anything that can be iterated case
fit_dictx=dufits.mk_fit_dict(self.xcoeff[ii],self.ncoeff,'legendre',self.wmin,self.wmax)
xfit=dufits.func_val(wavelength,fit_dictx)
x.append(xfit)
return np.array(x)
def wavelength(self,ispec=None,y=None):
"""
returns wavelength evaluated at y
"""
if y is None:
y=np.arange(0,self.npix_y)
if ispec is None:
ispec=np.arange(self.nspec)
#- First get the inversion map y --> wavelength dictionary
c,ymin,ymax=self.invert(coeff=self.ycoeff)
if isinstance(ispec,int):
new_dict=dufits.mk_fit_dict(c[ispec,:],c[ispec,:].shape,'legendre',ymin,ymax)
wfit=dufits.func_val(y,new_dict)
return wfit
else:
ww=list()
for ii in ispec:
new_dict=dufits.mk_fit_dict(c[ii,:],c[ii,:].shape,'legendre',ymin,ymax)
wfit=dufits.func_val(y,new_dict)
ww.append(wfit)
return np.array(ww)
def __init__(self, mu=None, sigma=None, wdict=None, waves=None, ndiag=9):
self.__ndiag = ndiag
if ndiag & 0x1 == 0:
raise ValueError("Need odd numbered diagonals, got %d" % ndiag)
def _binIntegral(x, mu=None, sigma=None):
"""
x: bin boundaries vector (self.__ndiag,)
mu: means vector of shape[nwave,1]
sigma: sigmas of shape[nwave,1]
"""
nvecs = 1
if sigma is not None:
nvecs = sigma.shape[0]
if mu is None:
mu = np.zeros((nvecs, 1))
if sigma is None:
sigma = np.ones(mu.shape) * 0.5
sx = (np.tile(x, (mu.shape[0], 1)) - mu) / (sigma * np.sqrt(2))
return 0.5 * (np.abs(np.diff(scipy.special.erf(sx))))
mnone = mu is None
snone = sigma is None
dnone = wdict is None
wnone = waves is None
if snone:
if wnone or dnone:
raise ValueError(
'Cannot initialize Resolution data need sigma or wdict and waves'
)
else:
from desiutil import funcfits as dufits
sigma = dufits.func_val(waves, wdict)
nwave = len(sigma)
s = sigma.reshape((nwave, 1))
bins = np.arange(ndiag, 0, -1)
bins = bins - (bins[0] + bins[-1]) / 2.0
x = np.concatenate([bins + 0.5, bins[-1:] - 0.5])
self.offsets = bins
rdata = _binIntegral(x, mu=mu, sigma=s).T
scipy.sparse.dia_matrix.__init__(self, (rdata, self.offsets),
(nwave, nwave))
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 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 wdisp(self,ispec,wave):
#- wave: scalar or vector, ispec: scalar integer TODO: make useful for other permutations
if hasattr(self,'wcoeff'):
new_dict=dufits.mk_fit_dict(self.wcoeff[ispec],self.wcoeff.shape[1],'legendre',self.wmin,self.wmax)
wsigma=dufits.func_val(wave,new_dict)
return wsigma