fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32)*5400
fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
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, truth
#-------------------------------------------------------------------------
#- Currently unused, but keep around for now
def sample_nz(objtype, n):
"""
Given `objtype` = 'LRG', 'ELG', 'QSO', 'STAR', 'STD'
return array of `n` redshifts that properly sample n(z)
from $DESIMODEL/data/targets/nz*.dat
"""
#- TODO: should this be in desimodel instead?
#- Stars are at redshift 0 for now. Could consider a velocity dispersion.
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO (@moustakas): Deal with the random seed correctly.
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
# - Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
# - 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)
truth = dict()
truth["FLUX"] = np.zeros((nspec, len(wave)))
truth["REDSHIFT"] = np.zeros(nspec, dtype="f4")
truth["TEMPLATEID"] = np.zeros(nspec, dtype="i4")
truth["OIIFLUX"] = np.zeros(nspec, dtype="f4")
truth["D4000"] = np.zeros(nspec, dtype="f4")
truth["OBJTYPE"] = np.zeros(nspec, dtype="S10")
# - Note: unlike other elements, first index of WAVE isn't spectrum index
truth["WAVE"] = wave
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]
truth["OBJTYPE"][ii] = true_objtype[ii]
# Simulate spectra
if objtype == "SKY":
continue
elif objtype == "ELG":
from desisim.templates import ELG
elg = ELG(nmodel=nobj, wave=wave)
simflux, wave1, meta = elg.make_templates()
elif objtype == "LRG":
from desisim.templates import LRG
lrg = LRG(nmodel=nobj, wave=wave)
simflux, wave1, meta = lrg.make_templates()
elif objtype == "QSO":
from desisim.templates import QSO
qso = QSO(nmodel=nobj, wave=wave)
simflux, wave1, meta = qso.make_templates()
# 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.
elif objtype == "QSO_BAD":
from desisim.templates import STAR
star = STAR(nmodel=nobj, wave=wave)
simflux, wave1, meta = star.make_templates()
elif objtype == "STD":
from desisim.templates import STAR
star = STAR(nmodel=nobj, wave=wave, FSTD=True)
simflux, wave1, meta = star.make_templates()
truth["FLUX"][ii] = simflux
truth["TEMPLATEID"][ii] = meta["TEMPLATEID"]
truth["REDSHIFT"][ii] = meta["REDSHIFT"]
# Pack in the photometry. TODO: Include WISE.
magg = meta["GMAG"]
magr = meta["RMAG"]
magz = meta["ZMAG"]
fibermap["MAG"][ii, 0:3] = np.vstack([magg, magr, magz]).T
fibermap["FILTER"][ii, 0:3] = ["DECAM_G", "DECAM_R", "DECAM_Z"]
if objtype == "ELG":
truth["OIIFLUX"][ii] = meta["OIIFLUX"]
truth["D4000"][ii] = meta["D4000"]
if objtype == "LRG":
truth["D4000"][ii] = meta["D4000"]
# - Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
# - Where are these targets? Centered on positioners for now.
x = fiberpos["X"][0:nspec]
y = fiberpos["Y"][0: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"][0:nspec]
fibermap["SPECTROID"] = fiberpos["SPECTROGRAPH"][0:nspec]
fibermap["TARGETID"] = np.random.randint(sys.maxint, size=nspec)
fibermap["TARGETCAT"] = np.zeros(nspec, dtype="|S20")
fibermap["LAMBDAREF"] = np.ones(nspec, dtype=np.float32) * 5400
fibermap["TARGET_MASK0"] = np.zeros(nspec, dtype="i8")
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, truth
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO (@moustakas): Deal with the random seed correctly.
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
#- 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)
truth = dict()
truth['FLUX'] = np.zeros((nspec, len(wave)))
truth['REDSHIFT'] = np.zeros(nspec, dtype='f4')
truth['TEMPLATEID'] = np.zeros(nspec, dtype='i4')
truth['OIIFLUX'] = np.zeros(nspec, dtype='f4')
truth['D4000'] = np.zeros(nspec, dtype='f4')
truth['VDISP'] = np.zeros(nspec, dtype='f4')
truth['OBJTYPE'] = np.zeros(nspec, dtype='S10')
#- Note: unlike other elements, first index of WAVE isn't spectrum index
truth['WAVE'] = wave
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]
truth['OBJTYPE'][ii] = true_objtype[ii]
# Simulate spectra
if objtype == 'SKY':
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta = elg.make_templates(nmodel=nobj)
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta = lrg.make_templates(nmodel=nobj)
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta = qso.make_templates(nmodel=nobj)
# 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.
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
star = STAR(wave=wave)
simflux, wave1, meta = star.make_templates(nmodel=nobj)
elif objtype == 'STD':
from desisim.templates import STAR
star = STAR(wave=wave, FSTD=True)
simflux, wave1, meta = star.make_templates(nmodel=nobj)
truth['FLUX'][ii] = 1e17 * simflux
truth['UNITS'] = '1e-17 erg/s/cm2/A'
truth['TEMPLATEID'][ii] = meta['TEMPLATEID']
truth['REDSHIFT'][ii] = meta['REDSHIFT']
# Pack in the photometry. TODO: Include WISE.
magg = meta['GMAG']
magr = meta['RMAG']
magz = meta['ZMAG']
fibermap['MAG'][ii, 0:3] = np.vstack([magg, magr, magz]).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_G', 'DECAM_R', 'DECAM_Z']
if objtype == 'ELG':
truth['OIIFLUX'][ii] = meta['OIIFLUX']
truth['D4000'][ii] = meta['D4000']
truth['VDISP'][ii] = meta['VDISP']
if objtype == 'LRG':
truth['D4000'][ii] = meta['D4000']
truth['VDISP'][ii] = meta['VDISP']
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][0:nspec]
y = fiberpos['Y'][0: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'][0:nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
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, truth
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
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, truth
#-------------------------------------------------------------------------
#- Currently unused, but keep around for now
def sample_nz(objtype, n):
"""
Given `objtype` = 'LRG', 'ELG', 'QSO', 'STAR', 'STD'
return array of `n` redshifts that properly sample n(z)
from $DESIMODEL/data/targets/nz*.dat
"""
#- TODO: should this be in desimodel instead?
#- Stars are at redshift 0 for now. Could consider a velocity dispersion.