def _get_spectra(self):
#- Setup data for a Resolution matrix
sigma = 4.0
ndiag = 21
xx = np.linspace(-(ndiag - 1) / 2.0, +(ndiag - 1) / 2.0, ndiag)
Rdata = np.zeros((self.nspec, ndiag, self.nwave))
for i in range(self.nspec):
for j in range(self.nwave):
kernel = np.exp(-xx**2 / (2 * sigma))
kernel /= sum(kernel)
Rdata[i, :, j] = kernel
flux = np.zeros((self.nspec, self.nwave))
ivar = np.ones((self.nspec, self.nwave))
mask = np.zeros((self.nspec, self.nwave), dtype=int)
for i in range(self.nspec):
R = Resolution(Rdata[i])
flux[i] = R.dot(self.flux)
fibermap = lvmspec.io.empty_fibermap(self.nspec, 1500)
fibermap['OBJTYPE'][0::2] = 'SKY'
return Frame(self.wave,
flux,
ivar,
mask,
Rdata,
spectrograph=2,
fibermap=fibermap)
def get_frame_data(nspec=10, objtype=None):
"""
Return basic test data for lvmspec.frame object:
"""
nwave = 100
wavemin, wavemax = 4000, 4100
wave, model_flux = get_models(nspec,
nwave,
wavemin=wavemin,
wavemax=wavemax)
resol_data = set_resolmatrix(nspec, nwave)
calib = np.sin((wave - wavemin) * np.pi / np.max(wave))
flux = np.zeros((nspec, nwave))
for i in range(nspec):
flux[i] = Resolution(resol_data[i]).dot(model_flux[i] * calib)
sigma = 0.01
# flux += np.random.normal(scale=sigma, size=flux.shape)
ivar = np.ones(flux.shape) / sigma**2
mask = np.zeros(flux.shape, dtype=int)
fibermap = empty_fibermap(nspec, 1500)
if objtype is None:
fibermap['OBJTYPE'] = 'QSO'
fibermap['OBJTYPE'][0:3] = 'STD' # For flux tests
else:
fibermap['OBJTYPE'] = objtype
frame = Frame(wave, flux, ivar, mask, resol_data, fibermap=fibermap)
frame.meta = {}
frame.meta['EXPTIME'] = 1. # For flux tests
return frame
def test_resolution(self):
"""
Test that identical spectra convolved with different resolutions
results in identical fiberflats
"""
wave, flux, ivar, mask = _get_data()
nspec, nwave = flux.shape
#- Setup a Resolution matrix that varies with fiber and wavelength
#- Note: this is actually the transpose of the resolution matrix
#- I wish I was creating, but as long as we self-consistently
#- use it for convolving and solving, that shouldn't matter.
sigma = np.linspace(2, 10, nwave * nspec)
ndiag = 21
xx = np.linspace(-ndiag / 2.0, +ndiag / 2.0, ndiag)
Rdata = np.zeros((nspec, len(xx), nwave))
for i in range(nspec):
for j in range(nwave):
kernel = np.exp(-xx**2 / (2 * sigma[i * nwave + j]**2))
kernel /= sum(kernel)
Rdata[i, :, j] = kernel
#- Convolve the data with the resolution matrix
convflux = np.empty_like(flux)
for i in range(nspec):
convflux[i] = Resolution(Rdata[i]).dot(flux[i])
#- Run the code
frame = Frame(wave, convflux, ivar, mask, Rdata, spectrograph=0)
ff = compute_fiberflat(frame)
#- These fiber flats should all be ~1
self.assertTrue(np.all(np.abs(ff.fiberflat - 1) < 0.001))
def _getdata(self, n=10):
wave = np.linspace(5000, 5100, n)
flux = np.random.uniform(0, 1, size=n)
ivar = np.random.uniform(0, 1, size=n)
### mask = np.random.randint(0, 256, size=n)
rdat = np.ones((3, n))
rdat[0] *= 0.25
rdat[1] *= 0.5
rdat[2] *= 0.25
R = Resolution(rdat)
### return wave, flux, ivar, mask, R
return wave, flux, ivar, None, R
def test_throughput_resolution(self):
"""
Test that spectra with different throughputs and different resolutions
result in fiberflat variations that are only due to throughput.
"""
wave, flux, ivar, mask = _get_data()
nspec, nwave = flux.shape
#- Setup a Resolution matrix that varies with fiber and wavelength
#- Note: this is actually the transpose of the resolution matrix
#- I wish I was creating, but as long as we self-consistently
#- use it for convolving and solving, that shouldn't matter.
sigma = np.linspace(2, 10, nwave * nspec)
ndiag = 21
xx = np.linspace(-ndiag / 2.0, +ndiag / 2.0, ndiag)
Rdata = np.zeros((nspec, len(xx), nwave))
for i in range(nspec):
for j in range(nwave):
kernel = np.exp(-xx**2 / (2 * sigma[i * nwave + j]**2))
kernel /= sum(kernel)
Rdata[i, :, j] = kernel
#- Vary the input flux prior to calculating the fiber flat
flux[1] *= 1.1
flux[2] *= 1.2
flux[3] /= 1.1
flux[4] /= 1.2
#- Convolve the data with the varying resolution matrix
convflux = np.empty_like(flux)
for i in range(nspec):
convflux[i] = Resolution(Rdata[i]).dot(flux[i])
#- Run the code
frame = Frame(wave, convflux, ivar, mask, Rdata, spectrograph=0)
#- Set an accuracy for this
accuracy = 1.e-9
ff = compute_fiberflat(frame, accuracy=accuracy)
#- Compare variation with middle fiber
mid = ff.fiberflat.shape[0] // 2
diff = (ff.fiberflat[1] / 1.1 - ff.fiberflat[mid])
self.assertLess(np.max(np.abs(diff)), accuracy)
diff = (ff.fiberflat[2] / 1.2 - ff.fiberflat[mid])
self.assertLess(np.max(np.abs(diff)), accuracy)
diff = (ff.fiberflat[3] * 1.1 - ff.fiberflat[mid])
self.assertLess(np.max(np.abs(diff)), accuracy)
diff = (ff.fiberflat[4] * 1.2 - ff.fiberflat[mid])
self.assertLess(np.max(np.abs(diff)), accuracy)
def test_errors(self):
#- Bad shaped input
data = np.random.uniform(size=(10,5))
with self.assertRaises(ValueError):
R = Resolution(data)
#- Meaningless type for input
with self.assertRaises(ValueError):
R = Resolution('blat')
#- Non-uniform x spacing
with self.assertRaises(ValueError):
R = lvmspec.resolution._gauss_pix([-1,0,2])
#- missing offsets
with self.assertRaises(ValueError):
lvmspec.resolution._sort_and_symmeterize(data, [-2,-1,0,1,3])
#- length of offsets too large or small
with self.assertRaises(ValueError):
Resolution(data, offsets=[1,2])
with self.assertRaises(ValueError):
Resolution(data, offsets=np.arange(10*lvmspec.resolution.default_ndiag))
def test_main(self):
"""
Test the main program.
"""
# generate the frame data
wave, flux, ivar, mask = _get_data()
nspec, nwave = flux.shape
#- Setup data for a Resolution matrix
sigma = 4.0
ndiag = 11
xx = np.linspace(-(ndiag - 1) / 2.0, +(ndiag - 1) / 2.0, ndiag)
Rdata = np.zeros((nspec, ndiag, nwave))
kernel = np.exp(-xx**2 / (2 * sigma))
kernel /= sum(kernel)
for i in range(nspec):
for j in range(nwave):
Rdata[i, :, j] = kernel
#- Convolve the data with the resolution matrix
convflux = np.empty_like(flux)
for i in range(nspec):
convflux[i] = Resolution(Rdata[i]).dot(flux[i])
# create a fake fibermap
fibermap = io.empty_fibermap(nspec, nwave)
for i in range(0, nspec):
fibermap['OBJTYPE'][i] = 'FAKE'
io.write_fibermap(self.testfibermap, fibermap)
#- write out the frame
frame = Frame(wave,
convflux,
ivar,
mask,
Rdata,
spectrograph=0,
fibermap=fibermap,
meta=dict(FLAVOR='flat'))
write_frame(self.testframe, frame, fibermap=fibermap)
# set program arguments
argstr = ['--infile', self.testframe, '--outfile', self.testflat]
# run it
args = ffscript.parse(options=argstr)
ffscript.main(args)
def test_throughput(self):
"""
Test that spectra with different throughputs but the same resolution
produce a fiberflat mirroring the variations in throughput
"""
wave, flux, ivar, mask = _get_data()
nspec, nwave = flux.shape
#- Setup data for a Resolution matrix
sigma = 4.0
ndiag = 21
xx = np.linspace(-(ndiag - 1) / 2.0, +(ndiag - 1) / 2.0, ndiag)
Rdata = np.zeros((nspec, ndiag, nwave))
kernel = np.exp(-xx**2 / (2 * sigma))
kernel /= sum(kernel)
for i in range(nspec):
for j in range(nwave):
Rdata[i, :, j] = kernel
#- Vary the input flux prior to calculating the fiber flat
flux[1] *= 1.1
flux[2] *= 1.2
flux[3] *= 0.8
#- Convolve with the (common) resolution matrix
convflux = np.empty_like(flux)
for i in range(nspec):
convflux[i] = Resolution(Rdata[i]).dot(flux[i])
frame = Frame(wave, convflux, ivar, mask, Rdata, spectrograph=0)
ff = compute_fiberflat(frame)
#- flux[1] is brighter, so should fiberflat[1]. etc.
self.assertTrue(np.allclose(ff.fiberflat[0], ff.fiberflat[1] / 1.1))
self.assertTrue(np.allclose(ff.fiberflat[0], ff.fiberflat[2] / 1.2))
self.assertTrue(np.allclose(ff.fiberflat[0], ff.fiberflat[3] / 0.8))
def sim_spectra(wave,
flux,
program,
spectra_filename,
obsconditions=None,
sourcetype=None,
expid=0,
seed=0):
"""
Simulate spectra from an input set of wavelength and flux and writes a FITS file in the Spectra format that can
be used as input to the redshift fitter.
Args:
wave : 1D np.array of wavelength in Angstrom (in vacuum) in observer frame (i.e. redshifted)
flux : 1D or 2D np.array. 1D array must have same size as wave, 2D array must have shape[1]=wave.size
flux has to be in units of 10^-17 ergs/s/cm2/A
spectra_filename : path to output FITS file in the Spectra format
Optional:
obsconditions : dictionnary of observation conditions with SEEING EXPTIME AIRMASS MOONFRAC MOONALT MOONSEP
sourcetype : list of string, allowed values are (sky,elg,lrg,qso,bgs,star), type of sources, used for fiber aperture loss , default is star
expid : this expid number will be saved in the Spectra fibermap
seed : random seed
"""
log = get_logger()
if len(flux.shape) == 1:
flux = flux.reshape((1, flux.size))
nspec = flux.shape[0]
log.info("Starting simulation of {} spectra".format(nspec))
if sourcetype is None:
sourcetype = np.array(["star" for i in range(nspec)])
log.debug("sourcetype = {}".format(sourcetype))
tileid = 0
telera = 0
teledec = 0
dateobs = time.gmtime()
night = lvmsim.obs.get_night(utc=dateobs)
program = program.lower()
frame_fibermap = lvmspec.io.fibermap.empty_fibermap(nspec)
frame_fibermap.meta["FLAVOR"] = "custom"
frame_fibermap.meta["NIGHT"] = night
frame_fibermap.meta["EXPID"] = expid
# add LVM_TARGET
tm = lvmtarget.desi_mask
frame_fibermap['LVM_TARGET'][sourcetype == "star"] = tm.STD_FSTAR
frame_fibermap['LVM_TARGET'][sourcetype == "lrg"] = tm.LRG
frame_fibermap['LVM_TARGET'][sourcetype == "elg"] = tm.ELG
frame_fibermap['LVM_TARGET'][sourcetype == "qso"] = tm.QSO
frame_fibermap['LVM_TARGET'][sourcetype == "sky"] = tm.SKY
frame_fibermap['LVM_TARGET'][sourcetype == "bgs"] = tm.BGS_ANY
# add dummy TARGETID
frame_fibermap['TARGETID'] = np.arange(nspec).astype(int)
# spectra fibermap has two extra fields : night and expid
# This would be cleaner if lvmspec would provide the spectra equivalent
# of lvmspec.io.empty_fibermap()
spectra_fibermap = lvmspec.io.empty_fibermap(nspec)
spectra_fibermap = lvmspec.io.util.add_columns(
spectra_fibermap,
['NIGHT', 'EXPID', 'TILEID'],
[np.int32(night), np.int32(expid),
np.int32(tileid)],
)
for s in range(nspec):
for tp in frame_fibermap.dtype.fields:
spectra_fibermap[s][tp] = frame_fibermap[s][tp]
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = lvmsim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = lvmsim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = lvmsim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = lvmsim.simexp.reference_conditions[
obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(lvmsim.simexp.reference_conditions.keys())))
try:
params = lvmmodel.io.load_desiparams()
wavemin = params['ccd']['b']['wavemin']
wavemax = params['ccd']['z']['wavemax']
except KeyError:
wavemin = lvmmodel.io.load_throughput('b').wavemin
wavemax = lvmmodel.io.load_throughput('z').wavemax
if wave[0] > wavemin:
log.warning(
'Minimum input wavelength {}>{}; padding with zeros'.format(
wave[0], wavemin))
dwave = wave[1] - wave[0]
npad = int((wave[0] - wavemin) / dwave + 1)
wavepad = np.arange(npad) * dwave
wavepad += wave[0] - dwave - wavepad[-1]
fluxpad = np.zeros((flux.shape[0], len(wavepad)), dtype=flux.dtype)
wave = np.concatenate([wavepad, wave])
flux = np.hstack([fluxpad, flux])
assert flux.shape[1] == len(wave)
assert np.allclose(dwave, np.diff(wave))
assert wave[0] <= wavemin
if wave[-1] < wavemax:
log.warning(
'Maximum input wavelength {}<{}; padding with zeros'.format(
wave[-1], wavemax))
dwave = wave[-1] - wave[-2]
npad = int((wavemax - wave[-1]) / dwave + 1)
wavepad = wave[-1] + dwave + np.arange(npad) * dwave
fluxpad = np.zeros((flux.shape[0], len(wavepad)), dtype=flux.dtype)
wave = np.concatenate([wave, wavepad])
flux = np.hstack([flux, fluxpad])
assert flux.shape[1] == len(wave)
assert np.allclose(dwave, np.diff(wave))
assert wavemax <= wave[-1]
ii = (wavemin <= wave) & (wave <= wavemax)
flux_unit = 1e-17 * u.erg / (u.Angstrom * u.s * u.cm**2)
wave = wave[ii] * u.Angstrom
flux = flux[:, ii] * flux_unit
sim = lvmsim.simexp.simulate_spectra(wave,
flux,
fibermap=frame_fibermap,
obsconditions=obsconditions,
seed=seed)
random_state = np.random.RandomState(seed)
sim.generate_random_noise(random_state)
scale = 1e17
specdata = None
resolution = {}
for camera in sim.instrument.cameras:
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(), [nspec, 1, 1])
for table in sim.camera_output:
wave = table['wavelength'].astype(float)
flux = (table['observed_flux'] + table['random_noise_electrons'] *
table['flux_calibration']).T.astype(float)
ivar = table['flux_inverse_variance'].T.astype(float)
band = table.meta['name'].strip()[0]
flux = flux * scale
ivar = ivar / scale**2
mask = np.zeros(flux.shape).astype(int)
spec = Spectra([band], {band: wave}, {band: flux}, {band: ivar},
resolution_data={band: resolution[band]},
mask={band: mask},
fibermap=spectra_fibermap,
meta=None,
single=True)
if specdata is None:
specdata = spec
else:
specdata.update(spec)
lvmspec.io.write_spectra(spectra_filename, specdata)
log.info('Wrote ' + spectra_filename)
def compute_sky(frame, nsig_clipping=4.,max_iterations=100,model_ivar=False,add_variance=True) :
"""Compute a sky model.
Input has to correspond to sky fibers only.
Input flux are expected to be flatfielded!
We don't check this in this routine.
Args:
frame : Frame object, which includes attributes
- wave : 1D wavelength grid in Angstroms
- flux : 2D flux[nspec, nwave] density
- ivar : 2D inverse variance of flux
- mask : 2D inverse mask flux (0=good)
- resolution_data : 3D[nspec, ndiag, nwave] (only sky fibers)
nsig_clipping : [optional] sigma clipping value for outlier rejection
Optional:
max_iterations : int , number of iterations
model_ivar : replace ivar by a model to avoid bias due to correlated flux and ivar. this has a negligible effect on sims.
returns SkyModel object with attributes wave, flux, ivar, mask
"""
log=get_logger()
log.info("starting")
# Grab sky fibers on this frame
skyfibers = np.where(frame.fibermap['OBJTYPE'] == 'SKY')[0]
assert np.max(skyfibers) < 500 #- indices, not fiber numbers
nwave=frame.nwave
nfibers=len(skyfibers)
current_ivar=frame.ivar[skyfibers].copy()*(frame.mask[skyfibers]==0)
flux = frame.flux[skyfibers]
Rsky = frame.R[skyfibers]
input_ivar=None
if model_ivar :
log.info("use a model of the inverse variance to remove bias due to correlated ivar and flux")
input_ivar=current_ivar.copy()
median_ivar_vs_wave = np.median(current_ivar,axis=0)
median_ivar_vs_fiber = np.median(current_ivar,axis=1)
median_median_ivar = np.median(median_ivar_vs_fiber)
for f in range(current_ivar.shape[0]) :
threshold=0.01
current_ivar[f] = median_ivar_vs_fiber[f]/median_median_ivar * median_ivar_vs_wave
# keep input ivar for very low weights
ii=(input_ivar[f]<=(threshold*median_ivar_vs_wave))
#log.info("fiber {} keep {}/{} original ivars".format(f,np.sum(ii),current_ivar.shape[1]))
current_ivar[f][ii] = input_ivar[f][ii]
sqrtw=np.sqrt(current_ivar)
sqrtwflux=sqrtw*flux
chi2=np.zeros(flux.shape)
#debug
#nfibers=min(nfibers,2)
nout_tot=0
for iteration in range(max_iterations) :
A=scipy.sparse.lil_matrix((nwave,nwave)).tocsr()
B=np.zeros((nwave))
# diagonal sparse matrix with content = sqrt(ivar)*flat of a given fiber
SD=scipy.sparse.lil_matrix((nwave,nwave))
# loop on fiber to handle resolution
for fiber in range(nfibers) :
if fiber%10==0 :
log.info("iter %d fiber %d"%(iteration,fiber))
R = Rsky[fiber]
# diagonal sparse matrix with content = sqrt(ivar)
SD.setdiag(sqrtw[fiber])
sqrtwR = SD*R # each row r of R is multiplied by sqrtw[r]
A = A+(sqrtwR.T*sqrtwR).tocsr()
B += sqrtwR.T*sqrtwflux[fiber]
log.info("iter %d solving"%iteration)
w = A.diagonal()>0
A_pos_def = A.todense()[w,:]
A_pos_def = A_pos_def[:,w]
skyflux = B*0
try:
skyflux[w]=cholesky_solve(A_pos_def,B[w])
except:
log.info("cholesky failed, trying svd in iteration {}".format(iteration))
skyflux[w]=np.linalg.lstsq(A_pos_def,B[w])[0]
log.info("iter %d compute chi2"%iteration)
for fiber in range(nfibers) :
S = Rsky[fiber].dot(skyflux)
chi2[fiber]=current_ivar[fiber]*(flux[fiber]-S)**2
log.info("rejecting")
nout_iter=0
if iteration<1 :
# only remove worst outlier per wave
# apply rejection iteratively, only one entry per wave among fibers
# find waves with outlier (fastest way)
nout_per_wave=np.sum(chi2>nsig_clipping**2,axis=0)
selection=np.where(nout_per_wave>0)[0]
for i in selection :
worst_entry=np.argmax(chi2[:,i])
current_ivar[worst_entry,i]=0
sqrtw[worst_entry,i]=0
sqrtwflux[worst_entry,i]=0
nout_iter += 1
else :
# remove all of them at once
bad=(chi2>nsig_clipping**2)
current_ivar *= (bad==0)
sqrtw *= (bad==0)
sqrtwflux *= (bad==0)
nout_iter += np.sum(bad)
nout_tot += nout_iter
sum_chi2=float(np.sum(chi2))
ndf=int(np.sum(chi2>0)-nwave)
chi2pdf=0.
if ndf>0 :
chi2pdf=sum_chi2/ndf
log.info("iter #%d chi2=%f ndf=%d chi2pdf=%f nout=%d"%(iteration,sum_chi2,ndf,chi2pdf,nout_iter))
if nout_iter == 0 :
break
log.info("nout tot=%d"%nout_tot)
# no need restore original ivar to compute model error when modeling ivar
# the sky inverse variances are very similar
# solve once again to get deconvolved sky variance
try :
unused_skyflux,skycovar=cholesky_solve_and_invert(A.todense(),B)
except np.linalg.linalg.LinAlgError :
log.warning("cholesky_solve_and_invert failed, switching to np.linalg.lstsq and np.linalg.pinv")
#skyflux = np.linalg.lstsq(A.todense(),B)[0]
skycovar = np.linalg.pinv(A.todense())
#- sky inverse variance, but incomplete and not needed anyway
# skyvar=np.diagonal(skycovar)
# skyivar=(skyvar>0)/(skyvar+(skyvar==0))
# Use diagonal of skycovar convolved with mean resolution of all fibers
# first compute average resolution
mean_res_data=np.mean(frame.resolution_data,axis=0)
R = Resolution(mean_res_data)
# compute convolved sky and ivar
cskycovar=R.dot(skycovar).dot(R.T.todense())
cskyvar=np.diagonal(cskycovar)
cskyivar=(cskyvar>0)/(cskyvar+(cskyvar==0))
# convert cskyivar to 2D; today it is the same for all spectra,
# but that may not be the case in the future
cskyivar = np.tile(cskyivar, frame.nspec).reshape(frame.nspec, nwave)
# Convolved sky
cskyflux = np.zeros(frame.flux.shape)
for i in range(frame.nspec):
cskyflux[i] = frame.R[i].dot(skyflux)
# look at chi2 per wavelength and increase sky variance to reach chi2/ndf=1
if skyfibers.size > 1 and add_variance :
log.info("Add a model error due to wavelength solution noise")
tivar = util.combine_ivar(frame.ivar[skyfibers], cskyivar[skyfibers])
# the chi2 at a given wavelength can be large because on a cosmic
# and not a psf error or sky non uniformity
# so we need to consider only waves for which
# a reasonable sky model error can be computed
# mean sky
msky = np.mean(cskyflux,axis=0)
dwave = np.mean(np.gradient(frame.wave))
dskydw = np.zeros(msky.shape)
dskydw[1:-1]=(msky[2:]-msky[:-2])/(frame.wave[2:]-frame.wave[:-2])
dskydw = np.abs(dskydw)
# now we consider a worst possible sky model error (20% error on flat, 0.5A )
max_possible_var = 1./(tivar+(tivar==0)) + (0.2*msky)**2 + (0.5*dskydw)**2
# exclude residuals inconsistent with this max possible variance (at 3 sigma)
bad = (frame.flux[skyfibers]-cskyflux[skyfibers])**2 > 3**2*max_possible_var
tivar[bad]=0
ndata = np.sum(tivar>0,axis=0)
ok=np.where(ndata>1)[0]
print("ok.size=",ok.size)
chi2 = np.zeros(frame.wave.size)
chi2[ok] = np.sum(tivar*(frame.flux[skyfibers]-cskyflux[skyfibers])**2,axis=0)[ok]/(ndata[ok]-1)
chi2[ndata<=1] = 1. # default
# now we are going to evaluate a sky model error based on this chi2,
# but only around sky flux peaks (>0.1*max)
tmp = np.zeros(frame.wave.size)
tmp = (msky[1:-1]>msky[2:])*(msky[1:-1]>msky[:-2])*(msky[1:-1]>0.1*np.max(msky))
peaks = np.where(tmp)[0]+1
dpix = int(np.ceil(3/dwave)) # +- n Angstrom around each peak
skyvar = 1./(cskyivar+(cskyivar==0))
# loop on peaks
for peak in peaks :
b=peak-dpix
e=peak+dpix+1
mchi2 = np.mean(chi2[b:e]) # mean reduced chi2 around peak
mndata = np.mean(ndata[b:e]) # mean number of fibers contributing
# sky model variance = sigma_flat * msky + sigma_wave * dmskydw
sigma_flat=0.000 # the fiber flat error is already included in the flux ivar
sigma_wave=0.005 # A, minimum value
res2=(frame.flux[skyfibers,b:e]-cskyflux[skyfibers,b:e])**2
var=1./(tivar[:,b:e]+(tivar[:,b:e]==0))
nd=np.sum(tivar[:,b:e]>0)
while(sigma_wave<2) :
pivar=1./(var+(sigma_flat*msky[b:e])**2+(sigma_wave*dskydw[b:e])**2)
pchi2=np.sum(pivar*res2)/nd
if pchi2<=1 :
log.info("peak at {}A : sigma_wave={}".format(int(frame.wave[peak]),sigma_wave))
skyvar[:,b:e] += ( (sigma_flat*msky[b:e])**2 + (sigma_wave*dskydw[b:e])**2 )
break
sigma_wave += 0.005
modified_cskyivar = (cskyivar>0)/skyvar
else :
modified_cskyivar = cskyivar.copy()
# need to do better here
mask = (cskyivar==0).astype(np.uint32)
return SkyModel(frame.wave.copy(), cskyflux, modified_cskyivar, mask,
nrej=nout_tot, stat_ivar = cskyivar) # keep a record of the statistical ivar for QA
def main(args):
# Set up the logger
if args.verbose:
log = get_logger(DEBUG)
else:
log = get_logger()
# Make sure all necessary environment variables are set
setup_envs()
# Initialize random number generator to use.
np.random.seed(args.seed)
random_state = np.random.RandomState(args.seed)
# Derive spectrograph number from nstart if needed
if args.spectrograph is None:
args.spectrograph = args.nstart / args.n_fibers
# Read fibermapfile to get object type, night and expid
fibermap, objtype, night, expid = get_fibermap(args.fibermap,
log=log,
nspec=args.nspec)
# Initialize the spectral simulator
log.info("Initializing SpecSim with config {}".format(args.config))
lvmparams = load_lvmparams(config=args.config, telescope=args.telescope)
qsim = get_simulator(args.config, num_fibers=1, params=lvmparams)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = lvmsim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
# - TODO: do we need quickgen to support arcs? For full pipeline
# - arcs are used to measure PSF but aren't extracted except for
# - debugging.
# - TODO: if we do need arcs, this needs to be redone.
# - conversion from phot to flux doesn't include throughput,
# - and arc lines are rebinned to nearest 0.2 A.
# Create full wavelength and flux arrays for arc exposure
wave_b = np.array(simspec.wave['b'])
wave_r = np.array(simspec.wave['r'])
wave_z = np.array(simspec.wave['z'])
phot_b = np.array(simspec.phot['b'][0])
phot_r = np.array(simspec.phot['r'][0])
phot_z = np.array(simspec.phot['z'][0])
sim_wave = np.concatenate((wave_b, wave_r, wave_z))
sim_phot = np.concatenate((phot_b, phot_r, phot_z))
wavelengths = np.arange(3533., 9913.1, 0.2)
phot = np.zeros(len(wavelengths))
for i in range(len(sim_wave)):
wavelength = sim_wave[i]
flux_index = np.argmin(abs(wavelength - wavelengths))
phot[flux_index] = sim_phot[i]
# Convert photons to flux: following specter conversion method
dw = np.gradient(wavelengths)
exptime = 5. # typical BOSS exposure time in s
fibarea = const.pi * (1.07e-2 /
2)**2 # cross-sectional fiber area in cm^2
hc = 1.e17 * const.h * const.c # convert to erg A
spectra = (hc * exptime * fibarea * dw * phot) / wavelengths
else:
wavelengths = simspec.wave['brz']
spectra = simspec.flux
if nspec < args.nspec:
log.info("Only {} spectra in input file".format(nspec))
args.nspec = nspec
else:
# Initialize the output truth table.
spectra = []
wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10))
truth['FLUX'] = np.zeros((args.nspec, npix))
truth['WAVE'] = wavelengths
jj = list()
for thisobj in set(true_objtype):
ii = np.where(true_objtype == thisobj)[0]
nobj = len(ii)
truth['OBJTYPE'][ii] = thisobj
log.info('Generating {} template'.format(thisobj))
# Generate the templates
if thisobj == 'ELG':
elg = lvmsim.templates.ELG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = elg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_elg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'LRG':
lrg = lvmsim.templates.LRG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = lrg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_lrg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'QSO':
qso = lvmsim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(
nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = lvmsim.templates.BGS(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = bgs.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_bgs,
rmagrange=args.rmagrange_bgs,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'STD':
fstd = lvmsim.templates.FSTD(wave=wavelengths)
flux, tmpwave, meta1 = fstd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = lvmsim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = lvmsim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'WD':
wd = lvmsim.templates.WD(wave=wavelengths)
flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'SKY':
flux = np.zeros((nobj, npix))
meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32)))
elif thisobj == 'TEST':
flux = np.zeros((args.nspec, npix))
indx = np.where(wave > 5800.0 - 1E-6)[0][0]
ref_integrated_flux = 1E-10
ref_cst_flux_density = 1E-17
single_line = (np.arange(args.nspec) % 2 == 0).astype(
np.float32)
continuum = (np.arange(args.nspec) % 2 == 1).astype(np.float32)
for spec in range(args.nspec):
flux[spec, indx] = single_line[
spec] * ref_integrated_flux / np.gradient(wavelengths)[
indx] # single line
flux[spec] += continuum[
spec] * ref_cst_flux_density # flat continuum
meta1 = Table(
dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32),
LINE=wave[indx] *
np.ones(args.nspec, dtype=np.float32),
LINEFLUX=single_line * ref_integrated_flux,
CONSTFLUXDENSITY=continuum * ref_cst_flux_density))
else:
log.fatal('Unknown object type {}'.format(thisobj))
sys.exit(1)
# Pack it in.
truth['FLUX'][ii] = flux
meta = vstack([meta, meta1])
jj.append(ii.tolist())
# Sanity check on units; templates currently return ergs, not 1e-17 ergs...
# assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6)
# Sort the metadata table.
jj = sum(jj, [])
meta_new = Table()
for k in range(args.nspec):
index = int(np.where(np.array(jj) == k)[0])
meta_new = vstack([meta_new, meta[index]])
meta = meta_new
# Add TARGETID and the true OBJTYPE to the metadata table.
meta.add_column(
Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE'))
meta.add_column(Column(targetids, name='TARGETID'))
# Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z
meta.rename_column('REDSHIFT', 'TRUEZ')
# ---------- end simspec
# explicitly set location on focal plane if needed to support airmass
# variations when using specsim v0.5
if qsim.source.focal_xy is None:
qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm'))
# Set simulation parameters from the simspec header or lvmparams
bright_objects = ['bgs', 'mws', 'bright', 'BGS', 'MWS', 'BRIGHT_MIX']
gray_objects = ['gray', 'grey']
if args.simspec is None:
object_type = objtype
flavor = None
elif simspec.flavor == 'science':
object_type = None
flavor = simspec.header['PROGRAM']
else:
object_type = None
flavor = simspec.flavor
log.warning(
'Maybe using an outdated simspec file with flavor={}'.format(
flavor))
# Set airmass
if args.airmass is not None:
qsim.atmosphere.airmass = args.airmass
elif args.simspec and 'AIRMASS' in simspec.header:
qsim.atmosphere.airmass = simspec.header['AIRMASS']
else:
qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2
# Set site location
if args.location is not None:
qsim.observation.observatory = args.location
else:
qsim.observation.observatory = 'APO'
# Set exptime
if args.exptime is not None:
qsim.observation.exposure_time = args.exptime * u.s
elif args.simspec and 'EXPTIME' in simspec.header:
qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s
elif objtype in bright_objects:
qsim.observation.exposure_time = lvmparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = lvmparams['exptime_dark'] * u.s
# Set Moon Phase
if args.moon_phase is not None:
qsim.atmosphere.moon.moon_phase = args.moon_phase
elif args.simspec and 'MOONFRAC' in simspec.header:
qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC']
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_phase = 0.7
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_phase = 0.1
else:
qsim.atmosphere.moon.moon_phase = 0.5
# Set Moon Zenith
if args.moon_zenith is not None:
qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg
elif args.simspec and 'MOONALT' in simspec.header:
qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_zenith = 30 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_zenith = 80 * u.deg
else:
qsim.atmosphere.moon.moon_zenith = 100 * u.deg
# Set Moon - Object Angle
if args.moon_angle is not None:
qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg
elif args.simspec and 'MOONSEP' in simspec.header:
qsim.atmosphere.moon.separation_angle = simspec.header[
'MOONSEP'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.separation_angle = 50 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
else:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
# Initialize per-camera output arrays that will be saved
waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {}
maxbin = 0
nmax = args.nspec
for camera in qsim.instrument.cameras:
# Lookup this camera's resolution matrix and convert to the sparse format used in lvmspec.
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(),
[args.nspec, 1, 1])
waves[camera.name] = (camera.output_wavelength.to(
u.Angstrom).value.astype(np.float32))
nwave = len(waves[camera.name])
maxbin = max(maxbin, len(waves[camera.name]))
nobj = np.zeros((nmax, 3, maxbin)) # object photons
nsky = np.zeros((nmax, 3, maxbin)) # sky photons
nivar = np.zeros((nmax, 3, maxbin)) # inverse variance (object+sky)
cframe_observedflux = np.zeros(
(nmax, 3, maxbin)) # calibrated object flux
cframe_ivar = np.zeros(
(nmax, 3, maxbin)) # inverse variance of calibrated object flux
cframe_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to calibrated flux
sky_ivar = np.zeros((nmax, 3, maxbin)) # inverse variance of sky
sky_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to sky only
frame_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to nobj+nsky
trueflux[camera.name] = np.empty(
(args.nspec, nwave)) # calibrated flux
noisyflux[camera.name] = np.empty(
(args.nspec, nwave)) # observed flux with noise
obsivar[camera.name] = np.empty(
(args.nspec, nwave)) # inverse variance of flux
if args.simspec:
dw = np.gradient(simspec.wave[camera.name])
else:
sflux = np.empty((args.nspec, npix))
# - Check if input simspec is for a continuum flat lamp instead of science
# - This does not convolve to per-fiber resolution
if args.simspec:
if simspec.flavor == 'flat':
log.info("Simulating flat lamp exposure")
for i, camera in enumerate(qsim.instrument.cameras):
channel = camera.name
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
dw = np.gradient(simspec.wave[channel])
meanspec = resample_flux(
waves[channel], simspec.wave[channel],
np.average(simspec.phot[channel] / dw, axis=0))
fiberflat = random_state.normal(loc=1.0,
scale=1.0 / np.sqrt(meanspec),
size=(nspec, num_pixels))
ivar = np.tile(meanspec, [nspec, 1])
mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32)
for kk in range((args.nspec + args.nstart - 1) //
args.n_fibers + 1):
camera = channel + str(kk)
outfile = lvmspec.io.findfile('fiberflat', night, expid,
camera)
start = max(args.n_fibers * kk, args.nstart)
end = min(args.n_fibers * (kk + 1), nmax)
if (args.spectrograph <= kk):
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, kk, start, end))
ff = FiberFlat(waves[channel],
fiberflat[start:end, :],
ivar[start:end, :],
mask[start:end, :],
meanspec,
header=dict(CAMERA=camera))
write_fiberflat(outfile, ff)
filePath = lvmspec.io.findfile("fiberflat", night, expid,
camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
scale = 1e-17
fluxunits = scale * u.erg / (u.s * u.cm**2 * u.Angstrom)
for j in range(args.nspec):
thisobjtype = objtype[j]
sys.stdout.flush()
if flavor == 'arc':
qsim.source.update_in('Quickgen source {0}'.format, 'perfect',
wavelengths * u.Angstrom,
spectra * fluxunits)
else:
qsim.source.update_in('Quickgen source {0}'.format(j),
thisobjtype.lower(),
wavelengths * u.Angstrom,
spectra[j, :] * fluxunits)
qsim.source.update_out()
qsim.simulate()
qsim.generate_random_noise(random_state)
for i, output in enumerate(qsim.camera_output):
assert output['observed_flux'].unit == 1e17 * fluxunits
# Extract the simulation results needed to create our uncalibrated
# frame output file.
num_pixels = len(output)
nobj[j, i, :num_pixels] = output['num_source_electrons'][:, 0]
nsky[j, i, :num_pixels] = output['num_sky_electrons'][:, 0]
nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:, 0]
# Get results for our flux-calibrated output file.
cframe_observedflux[
j, i, :num_pixels] = 1e17 * output['observed_flux'][:, 0]
cframe_ivar[
j,
i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:, 0]
# Fill brick arrays from the results.
camera = output.meta['name']
trueflux[camera][j][:] = 1e17 * output['observed_flux'][:, 0]
noisyflux[camera][j][:] = 1e17 * (
output['observed_flux'][:, 0] +
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:,
0]
# Use the same noise realization in the cframe and frame, without any
# additional noise from sky subtraction for now.
frame_rand_noise[
j, i, :num_pixels] = output['random_noise_electrons'][:, 0]
cframe_rand_noise[j, i, :num_pixels] = 1e17 * (
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
# The sky output file represents a model fit to ~40 sky fibers.
# We reduce the variance by a factor of 25 to account for this and
# give the sky an independent (Gaussian) noise realization.
sky_ivar[
j,
i, :num_pixels] = 25.0 / (output['variance_electrons'][:, 0] -
output['num_source_electrons'][:, 0])
sky_rand_noise[j, i, :num_pixels] = random_state.normal(
scale=1.0 / np.sqrt(sky_ivar[j, i, :num_pixels]),
size=num_pixels)
armName = {"b": 0, "r": 1, "z": 2}
for channel in 'brz':
# Before writing, convert from counts/bin to counts/A (as in Pixsim output)
# Quicksim Default:
# FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang]
# COUNTS_OBJ - object counts in 0.5 Ang bin
# COUNTS_SKY - sky counts in 0.5 Ang bin
num_pixels = len(waves[channel])
dwave = np.gradient(waves[channel])
nobj[:, armName[channel], :num_pixels] /= dwave
frame_rand_noise[:, armName[channel], :num_pixels] /= dwave
nivar[:, armName[channel], :num_pixels] *= dwave**2
nsky[:, armName[channel], :num_pixels] /= dwave
sky_rand_noise[:, armName[channel], :num_pixels] /= dwave
sky_ivar[:, armName[channel], :num_pixels] /= dwave**2
# Now write the outputs in DESI standard file system. None of the output file can have more than args.n_fibers spectra
# Looping over spectrograph
for ii in range((args.nspec + args.nstart - 1) // args.n_fibers + 1):
start = max(args.n_fibers * ii,
args.nstart) # first spectrum for a given spectrograph
end = min(args.n_fibers * (ii + 1),
nmax) # last spectrum for the spectrograph
if (args.spectrograph <= ii):
camera = "{}{}".format(channel, ii)
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, ii, start, end))
num_pixels = len(waves[channel])
# Write frame file
framefileName = lvmspec.io.findfile("frame", night, expid,
camera)
frame_flux = nobj[start:end, armName[channel], :num_pixels] + \
nsky[start:end, armName[channel], :num_pixels] + \
frame_rand_noise[start:end, armName[channel], :num_pixels]
frame_ivar = nivar[start:end, armName[channel], :num_pixels]
# required for slicing the resolution metric, resolusion matrix has (nspec, ndiag, wave)
# for example if nstart =400, nspec=150: two spectrographs:
# 400-499=> 0 spectrograph, 500-549 => 1
sh1 = frame_flux.shape[0]
if (args.nstart == start):
resol = resolution[channel][:sh1, :, :]
else:
resol = resolution[channel][-sh1:, :, :]
# must create lvmspec.Frame object
frame = Frame(waves[channel],
frame_flux,
frame_ivar,
resolution_data=resol,
spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor))
lvmspec.io.write_frame(framefileName, frame)
framefilePath = lvmspec.io.findfile("frame", night, expid,
camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName = lvmspec.io.findfile("cframe", night, expid,
camera)
cframeFlux = cframe_observedflux[start:end, armName[channel], :num_pixels] + \
cframe_rand_noise[start:end, armName[channel], :num_pixels]
cframeIvar = cframe_ivar[start:end,
armName[channel], :num_pixels]
# must create lvmspec.Frame object
cframe = Frame(waves[channel],
cframeFlux,
cframeIvar,
resolution_data=resol,
spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor))
lvmspec.io.frame.write_frame(cframeFileName, cframe)
cframefilePath = lvmspec.io.findfile("cframe", night, expid,
camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName = lvmspec.io.findfile("sky", night, expid, camera)
skyflux = nsky[start:end, armName[channel], :num_pixels] + \
sky_rand_noise[start:end, armName[channel], :num_pixels]
skyivar = sky_ivar[start:end, armName[channel], :num_pixels]
skymask = np.zeros(skyflux.shape, dtype=np.uint32)
# must create lvmspec.Sky object
skymodel = SkyModel(waves[channel],
skyflux,
skyivar,
skymask,
header=dict(CAMERA=camera))
lvmspec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath = lvmspec.io.findfile("sky", night, expid, camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile = lvmspec.io.findfile("calib", night, expid,
camera)
flux = cframe_observedflux[start:end,
armName[channel], :num_pixels]
phot = nobj[start:end, armName[channel], :num_pixels]
calibration = np.zeros_like(phot)
jj = (flux > 0)
calibration[jj] = phot[jj] / flux[jj]
# - TODO: what should calibivar be?
# - For now, model it as the noise of combining ~10 spectra
calibivar = 10 / cframe_ivar[start:end,
armName[channel], :num_pixels]
# mask=(1/calibivar>0).astype(int)??
mask = np.zeros(calibration.shape, dtype=np.uint32)
# write flux calibration
fluxcalib = FluxCalib(waves[channel], calibration, calibivar,
mask)
write_flux_calibration(calibVectorFile, fluxcalib)
calibfilePath = lvmspec.io.findfile("calib", night, expid,
camera)
log.info("Wrote file {}".format(calibfilePath))
def main(args=None):
'''
Converts simspec -> frame files; see fastframe --help for usage options
'''
#- TODO: use lvmutil.log
if isinstance(args, (list, tuple, type(None))):
args = parse(args)
print('Reading files')
simspec = lvmsim.io.read_simspec(args.simspec)
if simspec.flavor == 'arc':
print('arc exposure; no frames to output')
return
fibermap = simspec.fibermap
obs = simspec.obs
night = simspec.header['NIGHT']
expid = simspec.header['EXPID']
firstspec = args.firstspec
nspec = min(args.nspec, len(fibermap) - firstspec)
print('Simulating spectra {}-{}'.format(firstspec, firstspec + nspec))
wave = simspec.wave['brz']
flux = simspec.flux
ii = slice(firstspec, firstspec + nspec)
if simspec.flavor == 'science':
sim = lvmsim.simexp.simulate_spectra(wave,
flux[ii],
fibermap=fibermap[ii],
obsconditions=obs,
dwave_out=1.0)
elif simspec.flavor in ['arc', 'flat', 'calib']:
x = fibermap['X_TARGET']
y = fibermap['Y_TARGET']
fiber_area = lvmsim.simexp.fiber_area_arcsec2(fibermap['X_TARGET'],
fibermap['Y_TARGET'])
surface_brightness = (flux.T / fiber_area).T
config = lvmsim.simexp._specsim_config_for_wave(wave, dwave_out=1.0)
# sim = specsim.simulator.Simulator(config, num_fibers=nspec)
sim = lvmsim.specsim.get_simulator(config, num_fibers=nspec)
sim.observation.exposure_time = simspec.header['EXPTIME'] * u.s
sbunit = 1e-17 * u.erg / (u.Angstrom * u.s * u.cm**2 * u.arcsec**2)
xy = np.vstack([x, y]).T * u.mm
sim.simulate(calibration_surface_brightness=surface_brightness[ii] *
sbunit,
focal_positions=xy[ii])
else:
raise ValueError('Unknown simspec flavor {}'.format(simspec.flavor))
sim.generate_random_noise()
for i, results in enumerate(sim.camera_output):
results = sim.camera_output[i]
wave = results['wavelength']
scale = 1e17
if args.cframe:
phot = scale * (results['observed_flux'] +
results['random_noise_electrons'] *
results['flux_calibration']).T
ivar = 1. / scale**2 * results['flux_inverse_variance'].T
else:
phot = (results['num_source_electrons'] + \
results['num_sky_electrons'] + \
results['num_dark_electrons'] + \
results['random_noise_electrons']).T
ivar = 1.0 / results['variance_electrons'].T
R = Resolution(
sim.instrument.cameras[i].get_output_resolution_matrix())
Rdata = np.tile(R.data.T, nspec).T.reshape(nspec, R.data.shape[0],
R.data.shape[1])
assert np.all(Rdata[0] == R.data)
assert phot.shape == (nspec, len(wave))
for spectro in range(10):
imin = max(firstspec, spectro * 500) - firstspec
imax = min(firstspec + nspec, (spectro + 1) * 500) - firstspec
if imax <= imin:
continue
xphot = phot[imin:imax]
xivar = ivar[imin:imax]
xfibermap = fibermap[ii][imin:imax]
camera = '{}{}'.format(sim.camera_names[i], spectro)
meta = simspec.header.copy()
meta['CAMERA'] = camera
if args.cframe:
units = '1e-17 erg/(s cm2 A)'
else:
units = 'photon/bin'
if 'BUNIT' in meta:
meta['BUNIT'] = units
frame = Frame(wave,
xphot,
xivar,
resolution_data=Rdata[0:imax - imin],
spectrograph=spectro,
fibermap=xfibermap,
meta=meta)
if args.cframe:
outfile = lvmspec.io.findfile('cframe',
night,
expid,
camera,
outdir=args.outdir)
else:
outfile = lvmspec.io.findfile('frame',
night,
expid,
camera,
outdir=args.outdir)
print('writing {}'.format(outfile))
lvmspec.io.write_frame(outfile, frame, units=units)
def __init__(self,
wave,
flux,
ivar,
mask=None,
resolution_data=None,
fibers=None,
spectrograph=None,
meta=None,
fibermap=None,
chi2pix=None,
wsigma=None,
ndiag=21):
"""
Lightweight wrapper for multiple spectra on a common wavelength grid
x.wave, x.flux, x.ivar, x.mask, x.resolution_data, x.header, sp.R
Args:
wave: 1D[nwave] wavelength in Angstroms
flux: 2D[nspec, nwave] flux
ivar: 2D[nspec, nwave] inverse variance of flux
Optional:
mask: 2D[nspec, nwave] integer bitmask of flux. 0=good.
resolution_data: 3D[nspec, ndiag, nwave]
diagonals of resolution matrix data
fibers: ndarray of which fibers these spectra are
spectrograph: integer, which spectrograph [0-9]
meta: dict-like object (e.g. FITS header)
fibermap: fibermap table
chi2pix: 2D[nspec, nwave] chi2 of 2D model to pixel-level data
for pixels that contributed to each flux bin
Parameters below allow on-the-fly resolution calculation
wsigma: 2D[nspec,nwave] sigma widths for each wavelength bin for all fibers
Notes:
spectrograph input is used only if fibers is None. In this case,
it assumes nspec_per_spectrograph = flux.shape[0] and calculates
the fibers array for this spectrograph, i.e.
fibers = spectrograph * flux.shape[0] + np.arange(flux.shape[0])
Attributes:
All input args become object attributes.
nspec : number of spectra, flux.shape[0]
nwave : number of wavelengths, flux.shape[1]
specmin : minimum fiber number
R: array of sparse Resolution matrix objects converted
from resolution_data
fibermap: fibermap table if provided
"""
assert wave.ndim == 1
assert flux.ndim == 2
assert wave.shape[0] == flux.shape[1]
assert ivar.shape == flux.shape
assert (mask is None) or mask.shape == flux.shape
assert (mask is None) or mask.dtype in \
(int, np.int64, np.int32, np.uint64, np.uint32), "Bad mask type "+str(mask.dtype)
self.wave = wave
self.flux = flux
self.ivar = ivar
self.meta = meta
self.fibermap = fibermap
self.nspec, self.nwave = self.flux.shape
self.chi2pix = chi2pix
self.ndiag = ndiag
fibers_per_spectrograph = 500 #- hardcode; could get from lvmmodel
if mask is None:
self.mask = np.zeros(flux.shape, dtype=np.uint32)
else:
self.mask = util.mask32(mask)
if resolution_data is not None:
if resolution_data.ndim != 3 or \
resolution_data.shape[0] != self.nspec or \
resolution_data.shape[2] != self.nwave:
raise ValueError(
"Wrong dimensions for resolution_data[nspec, ndiag, nwave]"
)
#- Maybe setup non-None identity matrix resolution matrix instead?
self.wsigma = wsigma
self.resolution_data = resolution_data
if resolution_data is not None:
self.wsigma = None #ignore width coefficients if resolution data is given explicitly
self.ndiag = None
self.R = np.array([Resolution(r) for r in resolution_data])
elif wsigma is not None:
from lvmspec.quicklook.qlresolution import QuickResolution
assert ndiag is not None
r = []
for sigma in wsigma:
r.append(QuickResolution(sigma=sigma, ndiag=self.ndiag))
self.R = np.array(r)
else:
#SK I believe this should be error, but looking at the
#tests frame objects are allowed to not to have resolution data
# thus I changed value error to a simple warning message.
log = get_logger()
log.warning("Frame object is constructed without resolution data or respective "\
"sigma widths. Resolution will not be available")
# raise ValueError("Need either resolution_data or coefficients to generate it")
self.spectrograph = spectrograph
# Deal with Fibers (these must be set!)
if fibers is not None:
fibers = np.asarray(fibers)
if len(fibers) != self.nspec:
raise ValueError("len(fibers) != nspec ({} != {})".format(
len(fibers), self.nspec))
if fibermap is not None and np.any(fibers != fibermap['FIBER']):
raise ValueError("fibermap doesn't match fibers")
if (spectrograph is not None):
minfiber = spectrograph * fibers_per_spectrograph
maxfiber = (spectrograph + 1) * fibers_per_spectrograph
if np.any(fibers < minfiber) or np.any(maxfiber <= fibers):
raise ValueError('fibers inconsistent with spectrograph')
self.fibers = fibers
else:
if fibermap is not None:
self.fibers = fibermap['FIBER']
elif spectrograph is not None:
self.fibers = spectrograph * fibers_per_spectrograph + np.arange(
self.nspec, dtype=int)
elif (self.meta is not None) and ('FIBERMIN' in self.meta):
self.fibers = self.meta['FIBERMIN'] + np.arange(self.nspec,
dtype=int)
else:
raise ValueError("Must set fibers by one of the methods!")
if self.meta is not None:
self.meta['FIBERMIN'] = np.min(self.fibers)
def test_resolution_dense(self):
#- dense with no offsets specified
data = np.random.uniform(size=(10,10))
R = Resolution(data)
Rdense = R.todense()
self.assertTrue(np.all(Rdense == data))
#- with offsets
offsets = np.arange(-2,4)
R = Resolution(data, offsets)
Rdense = R.todense()
for i in offsets:
self.assertTrue(np.all(Rdense.diagonal(i) == data.diagonal(i)), \
"diagonal {} doesn't match".format(i))
#- dense without offsets but larger than default_ndiag
ndiag = lvmspec.resolution.default_ndiag + 5
data = np.random.uniform(size=(ndiag, ndiag))
Rdense = Resolution(data).todense()
for i in range(ndiag):
if i <= lvmspec.resolution.default_ndiag//2:
self.assertTrue(np.all(Rdense.diagonal(i) == data.diagonal(i)), \
"diagonal {} doesn't match".format(i))
self.assertTrue(np.all(Rdense.diagonal(-i) == data.diagonal(-i)), \
"diagonal {} doesn't match".format(-i))
else:
self.assertTrue(np.all(Rdense.diagonal(i) == 0.0), \
"diagonal {} not 0s".format(i))
self.assertTrue(np.all(Rdense.diagonal(-i) == 0.0), \
"diagonal {} not 0s".format(-i))
def test_resolution_sparsedia(self):
data = np.random.uniform(size=(5,10))
offsets = np.arange(-2,3)
#- Original case: symetric and odd number of diagonals
Rdia = scipy.sparse.dia_matrix((data, offsets), shape=(10,10))
R = Resolution(Rdia)
self.assertTrue(np.all(R.diagonal() == Rdia.diagonal()))
#- Non symetric but still odd number of diagonals
Rdia = scipy.sparse.dia_matrix((data, offsets+1), shape=(10,10))
R = Resolution(Rdia)
self.assertTrue(np.all(R.diagonal() == Rdia.diagonal()))
#- Even number of diagonals
Rdia = scipy.sparse.dia_matrix((data[1:,:], offsets[1:]), shape=(10,10))
R = Resolution(Rdia)
self.assertTrue(np.all(R.diagonal() == Rdia.diagonal()))
#- Unordered diagonals
data = np.random.uniform(size=(5,10))
offsets = [0,1,-1,2,-2]
Rdia = scipy.sparse.dia_matrix((data, offsets), shape=(10,10))
R1 = Resolution(Rdia)
R2 = Resolution(data, offsets)
self.assertTrue(np.all(R1.diagonal() == Rdia.diagonal()))
self.assertTrue(np.all(R2.diagonal() == Rdia.diagonal()))
self.assertTrue(np.all(R1.data == R2.data))
def test_resolution(self, n = 100):
dense = np.arange(n*n).reshape(n,n)
R1 = Resolution(dense)
assert scipy.sparse.isspmatrix_dia(R1),'Resolution is not recognized as a scipy.sparse.dia_matrix.'
assert len(R1.offsets) == lvmspec.resolution.default_ndiag, 'Resolution.offsets has wrong size'
R2 = Resolution(R1)
assert np.array_equal(R1.toarray(),R2.toarray()),'Constructor broken for dia_matrix input.'
R3 = Resolution(R1.data)
assert np.array_equal(R1.toarray(),R3.toarray()),'Constructor broken for array data input.'
sparse = scipy.sparse.dia_matrix((R1.data[::-1],R1.offsets[::-1]),(n,n))
R4 = Resolution(sparse)
assert np.array_equal(R1.toarray(),R4.toarray()),'Constructor broken for permuted offsets input.'
R5 = Resolution(R1.to_fits_array())
assert np.array_equal(R1.toarray(),R5.toarray()),'to_fits_array() is broken.'
#- test different sizes of input diagonals
for ndiag in [3,5,11]:
R6 = Resolution(np.ones((ndiag, n)))
assert len(R6.offsets) == ndiag, 'Constructor broken for ndiag={}'.format(ndiag)
#- An even number if diagonals is not allowed
try:
ndiag = 10
R7 = Resolution(np.ones((ndiag, n)))
raise RuntimeError('Incorrectly created Resolution with even number of diagonals')
except ValueError as err:
#- it correctly raised an error, so pass
pass
#- Test creation with sigmas - it should conserve flux
R9 = Resolution(np.linspace(1.0, 2.0, n))
self.assertTrue(np.allclose(np.sum(R9.data, axis=0), 1.0))