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) == desispec.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, err:
#- it correctly raised an error, so pass
pass
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
) == desispec.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))
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
DESI_SPECTRO_REDUX_DIR = "./quickGen"
if 'DESI_SPECTRO_REDUX' not in os.environ:
log.info('DESI_SPECTRO_REDUX environment is not set.')
else:
DESI_SPECTRO_REDUX_DIR = os.environ['DESI_SPECTRO_REDUX']
if os.path.exists(DESI_SPECTRO_REDUX_DIR):
if not os.path.isdir(DESI_SPECTRO_REDUX_DIR):
raise RuntimeError("Path %s Not a directory" %
DESI_SPECTRO_REDUX_DIR)
else:
try:
os.makedirs(DESI_SPECTRO_REDUX_DIR)
except:
raise
SPECPROD_DIR = 'specprod'
if 'SPECPROD' not in os.environ:
log.info('SPECPROD environment is not set.')
else:
SPECPROD_DIR = os.environ['SPECPROD']
prod_Dir = specprod_root()
if os.path.exists(prod_Dir):
if not os.path.isdir(prod_Dir):
raise RuntimeError("Path %s Not a directory" % prod_Dir)
else:
try:
os.makedirs(prod_Dir)
except:
raise
# 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 / 500
# Read fibermapfile to get object type, night and expid
if args.fibermap:
log.info("Reading fibermap file {}".format(args.fibermap))
fibermap = read_fibermap(args.fibermap)
objtype = get_source_types(fibermap)
stdindx = np.where(objtype == 'STD') # match STD with STAR
mwsindx = np.where(objtype == 'MWS_STAR') # match MWS_STAR with STAR
bgsindx = np.where(objtype == 'BGS') # match BGS with LRG
objtype[stdindx] = 'STAR'
objtype[mwsindx] = 'STAR'
objtype[bgsindx] = 'LRG'
NIGHT = fibermap.meta['NIGHT']
EXPID = fibermap.meta['EXPID']
else:
# Create a blank fake fibermap
fibermap = empty_fibermap(args.nspec)
targetids = random_state.randint(2**62, size=args.nspec)
fibermap['TARGETID'] = targetids
night = get_night()
expid = 0
log.info("Initializing SpecSim with config {}".format(args.config))
desiparams = load_desiparams()
qsim = get_simulator(args.config, num_fibers=1)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = desisim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
log.warning("quickgen doesn't generate flavor=arc outputs")
return
else:
wavelengths = simspec.wave
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 = desisim.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 = desisim.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 = desisim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(
nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = desisim.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':
std = desisim.templates.STD(wave=wavelengths)
flux, tmpwave, meta1 = std.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = desisim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = desisim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'WD':
wd = desisim.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')
# 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 desiparams
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 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 = desiparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = desiparams['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 desispec.
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:
for i in range(10):
cn = camera.name + str(i)
if cn in simspec.cameras:
dw = np.gradient(simspec.cameras[cn].wave)
break
else:
raise RuntimeError(
'Unable to find a {} camera in input simspec'.format(
camera))
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 #- from simspec, b/r/z not b0/r1/z9
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
phot = list()
for j in range(10):
cn = camera.name + str(j)
if cn in simspec.cameras:
camwave = simspec.cameras[cn].wave
dw = np.gradient(camwave)
phot.append(simspec.cameras[cn].phot)
if len(phot) == 0:
raise RuntimeError(
'Unable to find a {} camera in input simspec'.format(
camera))
else:
phot = np.vstack(phot)
meanspec = resample_flux(waves[channel], camwave,
np.average(phot / 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) // 500 + 1):
camera = channel + str(kk)
outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID,
camera)
start = max(500 * kk, args.nstart)
end = min(500 * (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 = desispec.io.findfile("fiberflat", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
fluxunits = 1e-17 * 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 500 spectra
# Looping over spectrograph
for ii in range((args.nspec + args.nstart - 1) // 500 + 1):
start = max(500 * ii,
args.nstart) # first spectrum for a given spectrograph
end = min(500 * (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 = desispec.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]
sh1 = frame_flux.shape[
0] # 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
if (args.nstart == start):
resol = resolution[channel][:sh1, :, :]
else:
resol = resolution[channel][-sh1:, :, :]
# must create desispec.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) )
desispec.io.write_frame(framefileName, frame)
framefilePath = desispec.io.findfile("frame", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName = desispec.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 desispec.Frame object
cframe = Frame(waves[channel], cframeFlux, cframeIvar, \
resolution_data=resol, spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.frame.write_frame(cframeFileName, cframe)
cframefilePath = desispec.io.findfile("cframe", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName = desispec.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 desispec.Sky object
skymodel = SkyModel(waves[channel],
skyflux,
skyivar,
skymask,
header=dict(CAMERA=camera))
desispec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath = desispec.io.findfile("sky", NIGHT, EXPID, camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile = desispec.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 = desispec.io.findfile("calib", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(calibfilePath))
def simulate(airmass=None,
exptime=None,
seeing=None,
moon_frac=None,
moon_sep=None,
moon_alt=None,
seed=1234,
nspec=5000,
brickname='testbrick',
galsim=False,
ra=None,
dec=None):
#- construct the simulator
qsim = simulator.Simulator('desi')
# Initialize random number generator to use.
random_state = np.random.RandomState(seed)
#- Create a blank fake fibermap for bricks
fibermap = empty_fibermap(nspec)
targetids = random_state.randint(2**62, size=nspec)
fibermap['TARGETID'] = targetids
night = get_night()
expid = 0
#- working out only ELG
objtype = 'ELG'
true_objtype = np.tile(np.array([objtype]), (nspec))
#- Initialize the output truth table.
spectra = []
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wavelengths = np.arange(round(wavemin, 1), wavemax, dw)
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = 'ELG' * nspec
truth['FLUX'] = np.zeros((nspec, npix))
truth['WAVE'] = wavelengths
#- get the templates
flux, tmpwave, meta1 = get_templates(wavelengths, seed=seed, nmodel=nspec)
truth['FLUX'] = flux
meta = vstack([meta, meta1])
#- 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')
waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {}
#- Now simulate
maxbin = 0
nmax = nspec
for camera in qsim.instrument.cameras:
# Lookup this camera's resolution matrix and convert to the sparse
# format used in desispec.
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(), [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(
(nspec, nwave)) # calibrated brick flux
noisyflux[camera.name] = np.empty(
(nspec, nwave)) # brick flux with noise
obsivar[camera.name] = np.empty(
(nspec, nwave)) # inverse variance of brick flux
sflux = np.empty((nspec, npix))
#- Repeat the simulation for all spectra
fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom)
spectra = truth['FLUX'] * 1.0e17
print("Simulating Spectra")
for j in range(nspec):
print("Simulating %s/%s spectra" % (j, nspec), end='\r')
thisobjtype = 'ELG'
sys.stdout.flush()
#- update qsim using conditions
if airmass is None:
thisairmass = None
else:
thisairmass = airmass[j]
if seeing is None:
thisseeing = None
else:
thisseeing = seeing[j]
if moon_frac is None:
thismoon_frac = None
else:
thismoon_frac = moon_frac[j]
if moon_sep is None:
thismoon_sep = None
else:
thismoon_sep = moon_sep[j]
if moon_alt is None:
thismoon_alt = None
else:
thismoon_alt = moon_alt[j]
if exptime is None:
thisexptime = None
else:
thisexptime = exptime[j]
nqsim = update_simulator(qsim,
airmass=thisairmass,
exptime=thisexptime,
seeing=thisseeing,
moon_frac=thismoon_frac,
moon_sep=thismoon_sep,
moon_alt=thismoon_alt,
galsim=galsim)
nqsim.source.update_in('Quickgen source {0}'.format(j),
thisobjtype.lower(), wavelengths * u.Angstrom,
spectra[j, :] * fluxunits)
nqsim.source.update_out()
nqsim.simulate()
nqsim.generate_random_noise(random_state)
sflux[j][:] = 1e17 * qsim.source.flux_in.to(fluxunits).value
for i, output in enumerate(nqsim.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])
#return output
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)
cframe_flux = cframe_observedflux[
j, i, :num_pixels] + cframe_rand_noise[j, i, :num_pixels]
armName = {"b": 0, "r": 1, "z": 2}
for channel in 'brz':
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 500 spectra
# Output brick files
if ra is None or dec is None:
filename = 'brick-{}-{}.fits'.format(channel, brickname)
filepath = os.path.normpath(
os.path.join('{}'.format(brickname), filename))
if os.path.exists(filepath):
os.remove(filepath)
print('Writing {}'.format(filepath))
header = dict(BRICKNAM=brickname, CHANNEL=channel)
brick = Brick(filepath, mode='update', header=header)
brick.add_objects(noisyflux[channel], obsivar[channel],
waves[channel], resolution[channel], fibermap,
night, expid)
brick.close()
"""
# Append truth to the file. Note: we add the resolution-convolved true
# flux, not the high resolution source flux, which makes chi2
# calculations easier.
header = fitsheader(header)
fx = fits.open(filepath, mode='append')
_add_truth(fx, header, meta, trueflux, sflux, wavelengths, channel)
fx.flush()
fx.close()
#sys.stdout.close()
"""
print("Wrote file {}".format(filepath))
else:
bricknames = get_bricknames(ra, dec)
fibermap['BRICKNAME'] = bricknames
bricknames = set(bricknames)
print("No. of bricks: {}".format(len(bricknames)))
print("Writing brick files")
for brick_name in bricknames:
thisbrick = (fibermap['BRICKNAME'] == brick_name)
brickdata = fibermap[thisbrick]
fibers = brickdata['FIBER'] #np.mod(brickdata['FIBER'],nspec)
filename = 'brick-{}-{}.fits'.format(channel, brick_name)
filepath = os.path.normpath(
os.path.join('./{}'.format(brick_name), filename))
if os.path.exists(filepath):
os.remove(filepath)
#print('Writing {}'.format(filepath))
header = dict(BRICKNAM=brick_name, CHANNEL=channel)
brick = Brick(filepath, mode='update', header=header)
brick.add_objects(noisyflux[channel][fibers],
obsivar[channel][fibers], waves[channel],
resolution[channel][fibers], brickdata,
night, expid)
brick.close()
print("Finished writing brick files for {} bricks".format(
len(bricknames)))
#- make a truth file
header = fitsheader(header)
make_truthfile(header, meta, trueflux, sflux, wavelengths)
def sim_spectra(wave,
flux,
program,
spectra_filename,
obsconditions=None,
sourcetype=None,
targetid=None,
redshift=None,
expid=0,
seed=0,
skyerr=0.0,
ra=None,
dec=None):
"""
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
program : dark, lrg, qso, gray, grey, elg, bright, mws, bgs
ignored if obsconditions is not None
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
targetid : list of targetids for each target. default of None has them generated as str(range(nspec))
redshift : list/array with each index being the redshifts for that target
expid : this expid number will be saved in the Spectra fibermap
seed : random seed
skyerr : fractional sky subtraction error
ra : numpy array with targets RA (deg)
dec : numpy array with targets Dec (deg)
"""
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 = desisim.obs.get_night(utc=dateobs)
program = program.lower()
frame_fibermap = desispec.io.fibermap.empty_fibermap(nspec)
frame_fibermap.meta["FLAVOR"] = "custom"
frame_fibermap.meta["NIGHT"] = night
frame_fibermap.meta["EXPID"] = expid
# add DESI_TARGET
tm = desitarget.targetmask.desi_mask
frame_fibermap['DESI_TARGET'][sourcetype == "star"] = tm.STD_FSTAR
frame_fibermap['DESI_TARGET'][sourcetype == "lrg"] = tm.LRG
frame_fibermap['DESI_TARGET'][sourcetype == "elg"] = tm.ELG
frame_fibermap['DESI_TARGET'][sourcetype == "qso"] = tm.QSO
frame_fibermap['DESI_TARGET'][sourcetype == "sky"] = tm.SKY
frame_fibermap['DESI_TARGET'][sourcetype == "bgs"] = tm.BGS_ANY
if targetid is None:
targetid = np.arange(nspec).astype(int)
# add TARGETID
frame_fibermap['TARGETID'] = targetid
# spectra fibermap has two extra fields : night and expid
# This would be cleaner if desispec would provide the spectra equivalent
# of desispec.io.empty_fibermap()
spectra_fibermap = desispec.io.empty_fibermap(nspec)
spectra_fibermap = desispec.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 ra is not None:
spectra_fibermap["RA_TARGET"] = ra
spectra_fibermap["RA_OBS"] = ra
if dec is not None:
spectra_fibermap["DEC_TARGET"] = dec
spectra_fibermap["DEC_OBS"] = dec
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[
obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
try:
params = desimodel.io.load_desiparams()
wavemin = params['ccd']['b']['wavemin']
wavemax = params['ccd']['z']['wavemax']
except KeyError:
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.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 = desisim.simexp.simulate_spectra(wave,
flux,
fibermap=frame_fibermap,
obsconditions=obsconditions,
redshift=redshift,
seed=seed,
psfconvolve=True)
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])
skyscale = skyerr * random_state.normal(size=sim.num_fibers)
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)
if np.any(skyscale):
flux += ((table['num_sky_electrons'] * skyscale) *
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)
desispec.io.write_spectra(spectra_filename, specdata)
log.info('Wrote ' + spectra_filename)
# need to clear the simulation buffers that keeps growing otherwise
# because of a different number of fibers each time ...
desisim.specsim._simulators.clear()
desisim.specsim._simdefaults.clear()
def sim_spectra(wave,
flux,
program,
spectra_filename,
obsconditions=None,
expid=0,
seed=0,
survey='desi'):
'''
Simulate spectra from input observer wavelength and (redshifted) 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 for multiple input.
Note: Flux has to be in units of 1e-17 [ergs/s/cm2/A].
spectra_filename: Path to output FITS file in the Spectra format
Optional:
obsconditions: Dictionary of observation conditions: {SEEING, EXPTIME, AIRMASS, MOONFRAC, MOONALT, MOONSEP}
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))
tileid = 0
telera = 0
teledec = 0
night = desisim.obs.get_night(utc=time.gmtime())
frame_fibermap = desispec.io.fibermap.empty_fibermap(nspec)
frame_fibermap.meta["FLAVOR"] = "custom"
frame_fibermap.meta["NIGHT"] = night
frame_fibermap.meta["EXPID"] = expid
## Add DESI_TARGET and TARGETID
tm = desitarget.desi_mask ## Contains 'templates' for STD_FSTAR.
for spec in range(nspec):
frame_fibermap['DESI_TARGET'][spec] = tm.STD_FSTAR
frame_fibermap['TARGETID'][spec] = spec
## Spectra fibermap has two extra fields: night and expid.
spectra_fibermap = np.zeros(shape=(nspec, ), dtype=spectra_dtype())
for s in range(nspec):
for tp in frame_fibermap.dtype.fields:
spectra_fibermap[s][tp] = frame_fibermap[s][tp]
spectra_fibermap[:]['EXPID'] = expid ## Needed by spectra.
spectra_fibermap[:]['NIGHT'] = night ## Needed by spectra.
program = program.lower()
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
"""
E.g.
reference_conditions['DARK']['SEEING'] = 1.1
reference_conditions['DARK']['EXPTIME'] = 1000
reference_conditions['DARK']['AIRMASS'] = 1.0
reference_conditions['DARK']['MOONFRAC'] = 0.0
reference_conditions['DARK']['MOONALT'] = -60
reference_conditions['DARK']['MOONSEP'] = 180
"""
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('Unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[
obsconditions.upper()]
except KeyError:
raise ValueError(
'Input observation conditions {} are not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
if survey == 'pfs':
wavemin = 3796. ## [A]
wavemax = 12605. ## [A]
elif survey == 'desi':
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
elif survey == 'beast':
wavemin = 3796. ## [A]
wavemax = 12605. ## [A]
else:
raise ValueError("\n\nSurvey %s is not available." % survey)
log.info("Setting wave limits to survey: {} ... {} to {} [A]".format(
survey, wavemin, wavemax))
ii = (wavemin <= wave) & (wave <= wavemax)
flux_unit = 1e-17 * u.erg / (u.Angstrom * u.s * u.cm**2)
wave = wave[
ii] * u.Angstrom ## Dimensionful quantities; only between wavelength limits.
flux = flux[:, ii] * flux_unit
sim = desisim.simexp.simulate_spectra(wave,
flux,
fibermap=frame_fibermap,
obsconditions=obsconditions,
survey=survey)
## Add random noise.
random_state = np.random.RandomState(seed)
sim.generate_random_noise(random_state)
specdata = None
scale = 1.e17
resolution = {}
## Methods for sim object.
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)
## Create spectra object for redrock.
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)
log.info("Writing to: %s" % spectra_filename)
desispec.io.write_spectra(spectra_filename, specdata)
log.info('Successfully created %s.' % spectra_filename)
def sim_spectra(wave, flux, program, spectra_filename, obsconditions=None,
sourcetype=None, targetid=None, redshift=None, expid=0, seed=0, skyerr=0.0, ra=None, dec=None, meta=None, fibermap_columns=None, fullsim=False,use_poisson=True):
"""
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
program : dark, lrg, qso, gray, grey, elg, bright, mws, bgs
ignored if obsconditions is not None
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
targetid : list of targetids for each target. default of None has them generated as str(range(nspec))
redshift : list/array with each index being the redshifts for that target
expid : this expid number will be saved in the Spectra fibermap
seed : random seed
skyerr : fractional sky subtraction error
ra : numpy array with targets RA (deg)
dec : numpy array with targets Dec (deg)
meta : dictionnary, saved in primary fits header of the spectra file
fibermap_columns : add these columns to the fibermap
fullsim : if True, write full simulation data in extra file per camera
use_poisson : if False, do not use numpy.random.poisson to simulate the Poisson noise. This is useful to get reproducible random realizations.
"""
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 = desisim.obs.get_night(utc=dateobs)
program = program.lower()
frame_fibermap = desispec.io.fibermap.empty_fibermap(nspec)
frame_fibermap.meta["FLAVOR"]="custom"
frame_fibermap.meta["NIGHT"]=night
frame_fibermap.meta["EXPID"]=expid
# add DESI_TARGET
tm = desitarget.targetmask.desi_mask
frame_fibermap['DESI_TARGET'][sourcetype=="star"]=tm.STD_FAINT
frame_fibermap['DESI_TARGET'][sourcetype=="lrg"]=tm.LRG
frame_fibermap['DESI_TARGET'][sourcetype=="elg"]=tm.ELG
frame_fibermap['DESI_TARGET'][sourcetype=="qso"]=tm.QSO
frame_fibermap['DESI_TARGET'][sourcetype=="sky"]=tm.SKY
frame_fibermap['DESI_TARGET'][sourcetype=="bgs"]=tm.BGS_ANY
if fibermap_columns is not None :
for k in fibermap_columns.keys() :
frame_fibermap[k] = fibermap_columns[k]
if targetid is None:
targetid = np.arange(nspec).astype(int)
# add TARGETID
frame_fibermap['TARGETID'] = targetid
# spectra fibermap has two extra fields : night and expid
# This would be cleaner if desispec would provide the spectra equivalent
# of desispec.io.empty_fibermap()
spectra_fibermap = desispec.io.empty_fibermap(nspec)
spectra_fibermap = desispec.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 ra is not None :
spectra_fibermap["TARGET_RA"] = ra
spectra_fibermap["FIBER_RA"] = ra
if dec is not None :
spectra_fibermap["TARGET_DEC"] = dec
spectra_fibermap["FIBER_DEC"] = dec
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
try:
params = desimodel.io.load_desiparams()
wavemin = params['ccd']['b']['wavemin']
wavemax = params['ccd']['z']['wavemax']
except KeyError:
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.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 = desisim.simexp.simulate_spectra(wave, flux, fibermap=frame_fibermap,
obsconditions=obsconditions, redshift=redshift, seed=seed,
psfconvolve=True)
random_state = np.random.RandomState(seed)
sim.generate_random_noise(random_state,use_poisson=use_poisson)
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])
skyscale = skyerr * random_state.normal(size=sim.num_fibers)
if fullsim :
for table in sim.camera_output :
band = table.meta['name'].strip()[0]
table_filename=spectra_filename.replace(".fits","-fullsim-{}.fits".format(band))
table.write(table_filename,format="fits",overwrite=True)
print("wrote",table_filename)
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)
if np.any(skyscale):
flux += ((table['num_sky_electrons']*skyscale)*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=meta,
single=True)
if specdata is None :
specdata = spec
else :
specdata.update(spec)
desispec.io.write_spectra(spectra_filename, specdata)
log.info('Wrote '+spectra_filename)
# need to clear the simulation buffers that keeps growing otherwise
# because of a different number of fibers each time ...
desisim.specsim._simulators.clear()
desisim.specsim._simdefaults.clear()
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 = desisim.obs.get_night(utc=dateobs)
program = program.lower()
frame_fibermap = desispec.io.fibermap.empty_fibermap(nspec)
frame_fibermap.meta["FLAVOR"] = "custom"
frame_fibermap.meta["NIGHT"] = night
frame_fibermap.meta["EXPID"] = expid
# add DESI_TARGET
tm = desitarget.desi_mask
frame_fibermap['DESI_TARGET'][sourcetype == "star"] = tm.STD_FSTAR
frame_fibermap['DESI_TARGET'][sourcetype == "lrg"] = tm.LRG
frame_fibermap['DESI_TARGET'][sourcetype == "elg"] = tm.ELG
frame_fibermap['DESI_TARGET'][sourcetype == "qso"] = tm.QSO
frame_fibermap['DESI_TARGET'][sourcetype == "sky"] = tm.SKY
frame_fibermap['DESI_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
spectra_fibermap = np.zeros(shape=(nspec, ), dtype=spectra_dtype())
for s in range(nspec):
for tp in frame_fibermap.dtype.fields:
spectra_fibermap[s][tp] = frame_fibermap[s][tp]
spectra_fibermap[:]['EXPID'] = expid # needed by spectra
spectra_fibermap[:]['NIGHT'] = night # needed by spectra
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[
obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
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
random_state = np.random.RandomState(seed)
sim = desisim.simexp.simulate_spectra(wave,
flux,
fibermap=frame_fibermap,
obsconditions=obsconditions)
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)
desispec.io.write_spectra(spectra_filename, specdata)
log.info('Wrote ' + spectra_filename)
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
DESI_SPECTRO_REDUX_DIR="./quickGen"
if 'DESI_SPECTRO_REDUX' not in os.environ:
log.info('DESI_SPECTRO_REDUX environment is not set.')
else:
DESI_SPECTRO_REDUX_DIR=os.environ['DESI_SPECTRO_REDUX']
if os.path.exists(DESI_SPECTRO_REDUX_DIR):
if not os.path.isdir(DESI_SPECTRO_REDUX_DIR):
raise RuntimeError("Path %s Not a directory"%DESI_SPECTRO_REDUX_DIR)
else:
try:
os.makedirs(DESI_SPECTRO_REDUX_DIR)
except:
raise
SPECPROD_DIR='specprod'
if 'SPECPROD' not in os.environ:
log.info('SPECPROD environment is not set.')
else:
SPECPROD_DIR=os.environ['SPECPROD']
prod_Dir=specprod_root()
if os.path.exists(prod_Dir):
if not os.path.isdir(prod_Dir):
raise RuntimeError("Path %s Not a directory"%prod_Dir)
else:
try:
os.makedirs(prod_Dir)
except:
raise
# 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 / 500
# Read fibermapfile to get object type, night and expid
if args.fibermap:
log.info("Reading fibermap file {}".format(args.fibermap))
fibermap=read_fibermap(args.fibermap)
objtype = get_source_types(fibermap)
stdindx=np.where(objtype=='STD') # match STD with STAR
mwsindx=np.where(objtype=='MWS_STAR') # match MWS_STAR with STAR
bgsindx=np.where(objtype=='BGS') # match BGS with LRG
objtype[stdindx]='STAR'
objtype[mwsindx]='STAR'
objtype[bgsindx]='LRG'
NIGHT=fibermap.meta['NIGHT']
EXPID=fibermap.meta['EXPID']
else:
# Create a blank fake fibermap
fibermap = empty_fibermap(args.nspec)
targetids = random_state.randint(2**62, size=args.nspec)
fibermap['TARGETID'] = targetids
night = get_night()
expid = 0
log.info("Initializing SpecSim with config {}".format(args.config))
desiparams = load_desiparams()
qsim = get_simulator(args.config, num_fibers=1)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = desisim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
log.warning("quickgen doesn't generate flavor=arc outputs")
return
else:
wavelengths = simspec.wave
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 = desisim.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 = desisim.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 = desisim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = desisim.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':
std = desisim.templates.STD(wave=wavelengths)
flux, tmpwave, meta1 = std.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = desisim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = desisim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'WD':
wd = desisim.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')
# 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 desiparams
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 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 = desiparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = desiparams['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 desispec.
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:
for i in range(10):
cn = camera.name + str(i)
if cn in simspec.cameras:
dw = np.gradient(simspec.cameras[cn].wave)
break
else:
raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera))
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 #- from simspec, b/r/z not b0/r1/z9
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
phot = list()
for j in range(10):
cn = camera.name + str(j)
if cn in simspec.cameras:
camwave = simspec.cameras[cn].wave
dw = np.gradient(camwave)
phot.append(simspec.cameras[cn].phot)
if len(phot) == 0:
raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera))
else:
phot = np.vstack(phot)
meanspec = resample_flux(
waves[channel], camwave, np.average(phot/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)//500+1):
camera = channel+str(kk)
outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID, camera)
start=max(500*kk,args.nstart)
end=min(500*(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=desispec.io.findfile("fiberflat",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
fluxunits = 1e-17 * 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 500 spectra
# Looping over spectrograph
for ii in range((args.nspec+args.nstart-1)//500+1):
start=max(500*ii,args.nstart) # first spectrum for a given spectrograph
end=min(500*(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=desispec.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]
sh1=frame_flux.shape[0] # 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
if (args.nstart==start):
resol=resolution[channel][:sh1,:,:]
else:
resol=resolution[channel][-sh1:,:,:]
# must create desispec.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) )
desispec.io.write_frame(framefileName, frame)
framefilePath=desispec.io.findfile("frame",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName=desispec.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 desispec.Frame object
cframe = Frame(waves[channel], cframeFlux, cframeIvar, \
resolution_data=resol, spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.frame.write_frame(cframeFileName,cframe)
cframefilePath=desispec.io.findfile("cframe",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName=desispec.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 desispec.Sky object
skymodel = SkyModel(waves[channel], skyflux, skyivar, skymask,
header=dict(CAMERA=camera))
desispec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath=desispec.io.findfile("sky",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile=desispec.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=desispec.io.findfile("calib",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(calibfilePath))
