def get_targets(nspec,
program,
tileid=None,
seed=None,
specify_targets=dict(),
specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros((nspec, len(wave)))
meta = empty_metatable(nmodel=nspec, objtype='SKY')
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1 = elg.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1 = lrg.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1 = bgs.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1 = qso.make_templates(nmodel=nobj,
seed=seed,
lyaforest=False,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1 = star.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import FSTD
fstd = FSTD(wave=wave)
simflux, wave1, meta1 = fstd.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.STD_FSTAR
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# todo: mag ranges for different programs of STAR targets should be in desimodel
if 'rmagrange' not in obj_kwargs.keys():
obj_kwargs['rmagrange'] = (15.0, 20.0)
simflux, wave1, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
flux[ii] = simflux
meta[ii] = meta1
fibermap['FILTER'][ii, :6] = [
'DECAM_G', 'DECAM_R', 'DECAM_Z', 'WISE_W1', 'WISE_W2'
]
fibermap['MAG'][ii, 0] = 22.5 - 2.5 * np.log10(meta['FLUX_G'][ii])
fibermap['MAG'][ii, 1] = 22.5 - 2.5 * np.log10(meta['FLUX_R'][ii])
fibermap['MAG'][ii, 2] = 22.5 - 2.5 * np.log10(meta['FLUX_Z'][ii])
fibermap['MAG'][ii, 3] = 22.5 - 2.5 * np.log10(meta['FLUX_W1'][ii])
fibermap['MAG'][ii, 4] = 22.5 - 2.5 * np.log10(meta['FLUX_W2'][ii])
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin + nspec]
y = fiberpos['Y'][specmin:specmin + nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin + nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin + nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxsize, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
fibermap['RA_TARGET'] = ra
fibermap['DEC_TARGET'] = dec
fibermap['X_TARGET'] = x
fibermap['Y_TARGET'] = y
fibermap['X_FVCOBS'] = fibermap['X_TARGET']
fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['RA_OBS'] = fibermap['RA_TARGET']
fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta)
def get_targets(nspec, program, tileid=None, seed=None, specify_targets=dict(), specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
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
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros( (nspec, len(wave)) )
meta, _ = empty_metatable(nmodel=nspec, objtype='SKY')
objmeta = dict()
fibermap = empty_fibermap(nspec)
targetid = np.random.randint(sys.maxsize, size=nspec).astype(np.int64)
meta['TARGETID'] = targetid
fibermap['TARGETID'] = targetid
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1, objmeta1 = elg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1, objmeta1 = lrg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1, objmeta1 = bgs.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1, objmeta1 = qso.make_templates(nmodel=nobj, seed=seed, lyaforest=False, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1, objmeta1 = star.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import STD
std = STD(wave=wave)
simflux, wave1, meta1, objmeta1 = std.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
#- Loop options for forwards/backwards compatibility
for name in ['STD_FAINT', 'STD_FSTAR', 'STD']:
if name in desi_mask.names():
fibermap['DESI_TARGET'][ii] |= desi_mask[name]
break
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# TODO: mag ranges for different programs of STAR targets should be in desimodel
if 'magrange' not in obj_kwargs.keys():
obj_kwargs['magrange'] = (15.0,20.0)
simflux, wave1, meta1, objmeta1 = mwsstar.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] |= desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
# Assign targetid
meta1['TARGETID'] = targetid[ii]
if hasattr(objmeta1, 'data'): # simqso.sqgrids.QsoSimPoints object
objmeta1.data['TARGETID'] = targetid[ii]
else:
if len(objmeta1) > 0:
objmeta1['TARGETID'] = targetid[ii]
# We want the dict key tied to the "true" object type (e.g., STAR),
# not, e.g., QSO_BAD.
objmeta[meta1['OBJTYPE'][0]] = objmeta1
flux[ii] = simflux
meta[ii] = meta1
for band in ['G', 'R', 'Z', 'W1', 'W2']:
key = 'FLUX_'+band
fibermap[key][ii] = meta[key][ii]
#- TODO: FLUX_IVAR
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin+nspec]
y = fiberpos['Y'][specmin:specmin+nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin+nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin+nspec]
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDA_REF'] = np.ones(nspec, dtype=np.float32)*5400
fibermap['TARGET_RA'] = ra
fibermap['TARGET_DEC'] = dec
fibermap['FIBERASSIGN_X'] = x
fibermap['FIBERASSIGN_Y'] = y
fibermap['FIBER_RA'] = fibermap['TARGET_RA']
fibermap['FIBER_DEC'] = fibermap['TARGET_DEC']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta, objmeta)
def get_targets(nspec, program, tileid=None, seed=None, specify_targets=dict(), specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
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
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros( (nspec, len(wave)) )
meta, _ = empty_metatable(nmodel=nspec, objtype='SKY')
objmeta = dict()
fibermap = empty_fibermap(nspec)
targetid = np.random.randint(sys.maxsize, size=nspec).astype(np.int64)
meta['TARGETID'] = targetid
fibermap['TARGETID'] = targetid
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1, objmeta1 = elg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1, objmeta1 = lrg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1, objmeta1 = bgs.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1, objmeta1 = qso.make_templates(nmodel=nobj, seed=seed, lyaforest=False, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1, objmeta1 = star.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import STD
std = STD(wave=wave)
simflux, wave1, meta1, objmeta1 = std.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
#- Loop options for forwards/backwards compatibility
for name in ['STD_FAINT', 'STD_FSTAR', 'STD']:
if name in desi_mask.names():
fibermap['DESI_TARGET'][ii] |= desi_mask[name]
break
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# TODO: mag ranges for different programs of STAR targets should be in desimodel
if 'magrange' not in obj_kwargs.keys():
obj_kwargs['magrange'] = (15.0,20.0)
simflux, wave1, meta1, objmeta1 = mwsstar.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] |= desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
# Assign targetid
meta1['TARGETID'] = targetid[ii]
if hasattr(objmeta1, 'data'): # simqso.sqgrids.QsoSimPoints object
objmeta1.data['TARGETID'] = targetid[ii]
else:
if len(objmeta1) > 0:
objmeta1['TARGETID'] = targetid[ii]
# We want the dict key tied to the "true" object type (e.g., STAR),
# not, e.g., QSO_BAD.
objmeta[meta1['OBJTYPE'][0]] = objmeta1
flux[ii] = simflux
meta[ii] = meta1
for band in ['G', 'R', 'Z', 'W1', 'W2']:
key = 'FLUX_'+band
fibermap[key][ii] = meta[key][ii]
#- TODO: FLUX_IVAR
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin+nspec]
y = fiberpos['Y'][specmin:specmin+nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin+nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin+nspec]
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDA_REF'] = np.ones(nspec, dtype=np.float32)*5400
fibermap['TARGET_RA'] = ra
fibermap['TARGET_DEC'] = dec
fibermap['DESIGN_X'] = x
fibermap['DESIGN_Y'] = y
fibermap['FIBER_RA'] = fibermap['TARGET_RA']
fibermap['FIBER_DEC'] = fibermap['TARGET_DEC']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta, objmeta)