def __init__(self, wavemin=None, wavemax=None, dw=0.2, nproc=1,
rand=None, verbose=False):
from desimodel.io import load_throughput
self.tree = TemplateKDTree(nproc=nproc)
# Build a default (buffered) wavelength vector.
if wavemin is None:
wavemin = load_throughput('b').wavemin - 10.0
if wavemax is None:
wavemax = load_throughput('z').wavemax + 10.0
self.wavemin = wavemin
self.wavemax = wavemax
self.dw = dw
self.wave = np.arange(round(wavemin, 1), wavemax, dw)
self.rand = rand
self.verbose = verbose
#self.__normfilter = 'decam2014-r' # default normalization filter
# Initialize the templates once:
from desisim.templates import BGS, ELG, LRG, QSO, STAR, WD
self.bgs_templates = BGS(wave=self.wave, normfilter='sdss2010-r') # Need to generalize this!
self.elg_templates = ELG(wave=self.wave, normfilter='decam2014-r')
self.lrg_templates = LRG(wave=self.wave, normfilter='decam2014-z')
self.qso_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.lya_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.star_templates = STAR(wave=self.wave, normfilter='decam2014-r')
self.wd_da_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DA')
self.wd_db_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DB')
def test_missing_wise_mags(self):
'''QSO and stellar templates don't have WISE mags. Flag that'''
qso = QSO(wave=self.wave)
flux, wave, meta = qso.make_templates(self.nspec)
self.assertTrue(not np.any(np.isnan(meta['W1MAG'])))
self.assertTrue(not np.any(np.isnan(meta['W2MAG'])))
star = STAR(wave=self.wave)
flux, wave, meta = star.make_templates(self.nspec)
self.assertTrue(not np.any(np.isnan(meta['W1MAG'])))
self.assertTrue(not np.any(np.isnan(meta['W2MAG'])))
def test_qso_options(self):
'''Test that the QSO keyword arguments work'''
flux, wave, meta, objmeta = QSO(wave=self.wave, balqso=False).make_templates(
self.nspec, seed=self.seed, lyaforest=False)
self.assertTrue(np.all(meta['SUBTYPE']==''))
flux, wave, meta, objmeta = QSO(wave=self.wave, balqso=False).make_templates(
self.nspec, seed=self.seed, lyaforest=True)
self.assertTrue(np.all(meta['SUBTYPE']=='LYA'))
flux, wave, meta, objmeta = QSO(wave=self.wave, balqso=True).make_templates(
self.nspec, seed=self.seed, balprob=1.0, lyaforest=False)
self.assertTrue(np.all(meta['SUBTYPE']=='BAL'))
self.assertTrue(np.all(objmeta['BAL_TEMPLATEID']!=-1))
flux1, wave1, meta1, objmeta1 = QSO(wave=self.wave, balqso=True).make_templates(
self.nspec, seed=self.seed, balprob=1.0, lyaforest=True)
self.assertTrue(np.all(meta1['SUBTYPE']=='LYA+BAL'))
self.assertTrue(np.all(objmeta1['BAL_TEMPLATEID']!=-1))
# Test that the spectra are reproducible when they include BALs.
I = self.rand.choice(self.nspec)
flux2, wave2, meta2, objmeta2 = QSO(wave=self.wave, balqso=True).make_templates(
1, seed=meta1['SEED'][I], balprob=1.0, lyaforest=True)
self.assertTrue(np.all(flux1[I, :]==flux2))
self.assertTrue(np.all(wave1==wave2))
for key in meta2.colnames:
if key in ['TARGETID', 'TEMPLATEID']: # this won't match in this test
continue
self.assertTrue(np.all(meta2[key]==meta1[key][I]))
for key in objmeta2.colnames:
if key in ['TARGETID', 'TEMPLATEID']: # this won't match in this test
continue
self.assertTrue(np.all(objmeta2[key]==objmeta1[key][I]))
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_spectra(lyafile,
nqso=None,
wave=None,
templateid=None,
normfilter='sdss2010-g',
seed=None,
rand=None,
qso=None,
add_dlas=False,
debug=False,
nocolorcuts=True):
"""Generate a QSO spectrum which includes Lyman-alpha absorption.
Args:
lyafile (str): name of the Lyman-alpha spectrum file to read.
nqso (int, optional): number of spectra to generate (starting from the
first spectrum; if more flexibility is needed use TEMPLATEID).
wave (numpy.ndarray, optional): desired output wavelength vector.
templateid (int numpy.ndarray, optional): indices of the spectra
(0-indexed) to read from LYAFILE (default is to read everything). If
provided together with NQSO, TEMPLATEID wins.
normfilter (str, optional): normalization filter
seed (int, optional): Seed for random number generator.
rand (numpy.RandomState, optional): RandomState object used for the
random number generation. If provided together with SEED, this
optional input superseeds the numpy.RandomState object instantiated by
SEED.
qso (desisim.templates.QSO, optional): object with which to generate
individual spectra/templates.
add_dlas (bool): Inject damped Lya systems into the Lya forest
These are done according to the current best estimates for the incidence dN/dz
(Prochaska et al. 2008, ApJ, 675, 1002)
Set in calc_lz
These are *not* inserted according to overdensity along the sightline
nocolorcuts (bool, optional): Do not apply the fiducial rzW1W2 color-cuts
cuts (default True).
Returns (flux, wave, meta, dla_meta) where:
* flux (numpy.ndarray): Array [nmodel, npix] of observed-frame spectra
(erg/s/cm2/A).
* wave (numpy.ndarray): Observed-frame [npix] wavelength array (Angstrom).
* meta (astropy.Table): Table of meta-data [nmodel] for each output spectrum
with columns defined in desisim.io.empty_metatable *plus* RA, DEC.
* objmeta (astropy.Table): Table of additional object-specific meta-data
[nmodel] for each output spectrum with columns defined in
desisim.io.empty_metatable.
* dla_meta (astropy.Table): Table of meta-data [ndla] for the DLAs injected
into the spectra. Only returned if add_dlas=True
Note: `dla_meta` is only included if add_dlas=True.
"""
from scipy.interpolate import interp1d
import fitsio
from speclite.filters import load_filters
from desisim.templates import QSO
from desisim.io import empty_metatable
h = fitsio.FITS(lyafile)
if templateid is None:
if nqso is None:
nqso = len(h) - 1
templateid = np.arange(nqso)
else:
templateid = np.asarray(templateid)
nqso = len(np.atleast_1d(templateid))
if rand is None:
rand = np.random.RandomState(seed)
templateseed = rand.randint(2**32, size=nqso)
#heads = [head.read_header() for head in h[templateid + 1]]
heads = []
for indx in templateid:
heads.append(h[indx + 1].read_header())
zqso = np.array([head['ZQSO'] for head in heads])
ra = np.array([head['RA'] for head in heads])
dec = np.array([head['DEC'] for head in heads])
mag_g = np.array([head['MAG_G'] for head in heads])
# Hard-coded filtername! Should match MAG_G!
normfilt = load_filters(normfilter)
if qso is None:
qso = QSO(normfilter_south=normfilter, wave=wave)
wave = qso.wave
flux = np.zeros([nqso, len(wave)], dtype='f4')
meta, objmeta = empty_metatable(objtype='QSO', nmodel=nqso)
meta['TEMPLATEID'][:] = templateid
meta['REDSHIFT'][:] = zqso
meta['MAG'][:] = mag_g
meta['MAGFILTER'][:] = normfilter
meta['SEED'][:] = templateseed
meta['RA'] = ra
meta['DEC'] = dec
# Lists for DLA meta data
if add_dlas:
dla_NHI, dla_z, dla_id = [], [], []
# Loop on quasars
for ii, indx in enumerate(templateid):
flux1, _, meta1, objmeta1 = qso.make_templates(
nmodel=1,
redshift=np.atleast_1d(zqso[ii]),
mag=np.atleast_1d(mag_g[ii]),
seed=templateseed[ii],
nocolorcuts=nocolorcuts,
lyaforest=False)
flux1 *= 1e-17
for col in meta1.colnames:
meta[col][ii] = meta1[col][0]
for col in objmeta1.colnames:
objmeta[col][ii] = objmeta1[col][0]
# read lambda and forest transmission
data = h[indx + 1].read()
la = data['LAMBDA'][:]
tr = data['FLUX'][:]
if len(tr):
# Interpolate the transmission at the spectral wavelengths, if
# outside the forest, the transmission is 1.
itr = interp1d(la, tr, bounds_error=False, fill_value=1.0)
flux1 *= itr(wave)
# Inject a DLA?
if add_dlas:
if np.min(wave / lambda_RF_LYA - 1) < zqso[ii]: # Any forest?
dlas, dla_model = insert_dlas(wave,
zqso[ii],
seed=templateseed[ii])
ndla = len(dlas)
if ndla > 0:
flux1 *= dla_model
# Meta
dla_z += [idla['z'] for idla in dlas]
dla_NHI += [idla['N'] for idla in dlas]
dla_id += [indx] * ndla
padflux, padwave = normfilt.pad_spectrum(flux1, wave, method='edge')
normmaggies = np.array(
normfilt.get_ab_maggies(padflux, padwave,
mask_invalid=True)[normfilter])
factor = 10**(-0.4 * mag_g[ii]) / normmaggies
flux1 *= factor
for key in ('FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'):
meta[key][ii] *= factor
flux[ii, :] = flux1[:]
h.close()
# Finish
if add_dlas:
ndla = len(dla_id)
if ndla > 0:
from astropy.table import Table
dla_meta = Table()
dla_meta['NHI'] = dla_NHI # log NHI values
dla_meta['z'] = dla_z
dla_meta['ID'] = dla_id
else:
dla_meta = None
return flux * 1e17, wave, meta, objmeta, dla_meta
else:
return flux * 1e17, wave, meta, objmeta
def get_spectra(lyafile, nqso=None, wave=None, templateid=None, normfilter='sdss2010-g',
seed=None, rand=None, qso=None, add_dlas=False, debug=False, nocolorcuts=True):
"""Generate a QSO spectrum which includes Lyman-alpha absorption.
Args:
lyafile (str): name of the Lyman-alpha spectrum file to read.
nqso (int, optional): number of spectra to generate (starting from the
first spectrum; if more flexibility is needed use TEMPLATEID).
wave (numpy.ndarray, optional): desired output wavelength vector.
templateid (int numpy.ndarray, optional): indices of the spectra
(0-indexed) to read from LYAFILE (default is to read everything). If
provided together with NQSO, TEMPLATEID wins.
normfilter (str, optional): normalization filter
seed (int, optional): Seed for random number generator.
rand (numpy.RandomState, optional): RandomState object used for the
random number generation. If provided together with SEED, this
optional input superseeds the numpy.RandomState object instantiated by
SEED.
qso (desisim.templates.QSO, optional): object with which to generate
individual spectra/templates.
add_dlas (bool): Inject damped Lya systems into the Lya forest
These are done according to the current best estimates for the incidence dN/dz
(Prochaska et al. 2008, ApJ, 675, 1002)
Set in calc_lz
These are *not* inserted according to overdensity along the sightline
nocolorcuts (bool, optional): Do not apply the fiducial rzW1W2 color-cuts
cuts (default True).
Returns (flux, wave, meta, dla_meta) where:
* flux (numpy.ndarray): Array [nmodel, npix] of observed-frame spectra
(erg/s/cm2/A).
* wave (numpy.ndarray): Observed-frame [npix] wavelength array (Angstrom).
* meta (astropy.Table): Table of meta-data [nmodel] for each output spectrum
with columns defined in desisim.io.empty_metatable *plus* RA, DEC.
* objmeta (astropy.Table): Table of additional object-specific meta-data
[nmodel] for each output spectrum with columns defined in
desisim.io.empty_metatable.
* dla_meta (astropy.Table): Table of meta-data [ndla] for the DLAs injected
into the spectra. Only returned if add_dlas=True
Note: `dla_meta` is only included if add_dlas=True.
"""
from scipy.interpolate import interp1d
import fitsio
from speclite.filters import load_filters
from desisim.templates import QSO
from desisim.io import empty_metatable
h = fitsio.FITS(lyafile)
if templateid is None:
if nqso is None:
nqso = len(h)-1
templateid = np.arange(nqso)
else:
templateid = np.asarray(templateid)
nqso = len(np.atleast_1d(templateid))
if rand is None:
rand = np.random.RandomState(seed)
templateseed = rand.randint(2**32, size=nqso)
#heads = [head.read_header() for head in h[templateid + 1]]
heads = []
for indx in templateid:
heads.append(h[indx + 1].read_header())
zqso = np.array([head['ZQSO'] for head in heads])
ra = np.array([head['RA'] for head in heads])
dec = np.array([head['DEC'] for head in heads])
mag_g = np.array([head['MAG_G'] for head in heads])
# Hard-coded filtername! Should match MAG_G!
normfilt = load_filters(normfilter)
if qso is None:
qso = QSO(normfilter_south=normfilter, wave=wave)
wave = qso.wave
flux = np.zeros([nqso, len(wave)], dtype='f4')
meta, objmeta = empty_metatable(objtype='QSO', nmodel=nqso)
meta['TEMPLATEID'][:] = templateid
meta['REDSHIFT'][:] = zqso
meta['MAG'][:] = mag_g
meta['MAGFILTER'][:] = normfilter
meta['SEED'][:] = templateseed
meta['RA'] = ra
meta['DEC'] = dec
# Lists for DLA meta data
if add_dlas:
dla_NHI, dla_z, dla_id = [], [], []
# Loop on quasars
for ii, indx in enumerate(templateid):
flux1, _, meta1, objmeta1 = qso.make_templates(nmodel=1, redshift=np.atleast_1d(zqso[ii]),
mag=np.atleast_1d(mag_g[ii]), seed=templateseed[ii],
nocolorcuts=nocolorcuts, lyaforest=False)
flux1 *= 1e-17
for col in meta1.colnames:
meta[col][ii] = meta1[col][0]
for col in objmeta1.colnames:
objmeta[col][ii] = objmeta1[col][0]
# read lambda and forest transmission
data = h[indx + 1].read()
la = data['LAMBDA'][:]
tr = data['FLUX'][:]
if len(tr):
# Interpolate the transmission at the spectral wavelengths, if
# outside the forest, the transmission is 1.
itr = interp1d(la, tr, bounds_error=False, fill_value=1.0)
flux1 *= itr(wave)
# Inject a DLA?
if add_dlas:
if np.min(wave/lambda_RF_LYA - 1) < zqso[ii]: # Any forest?
dlas, dla_model = insert_dlas(wave, zqso[ii], seed=templateseed[ii])
ndla = len(dlas)
if ndla > 0:
flux1 *= dla_model
# Meta
dla_z += [idla['z'] for idla in dlas]
dla_NHI += [idla['N'] for idla in dlas]
dla_id += [indx]*ndla
padflux, padwave = normfilt.pad_spectrum(flux1, wave, method='edge')
normmaggies = np.array(normfilt.get_ab_maggies(padflux, padwave,
mask_invalid=True)[normfilter])
factor = 10**(-0.4 * mag_g[ii]) / normmaggies
flux1 *= factor
for key in ('FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'):
meta[key][ii] *= factor
flux[ii, :] = flux1[:]
h.close()
# Finish
if add_dlas:
ndla = len(dla_id)
if ndla > 0:
from astropy.table import Table
dla_meta = Table()
dla_meta['NHI'] = dla_NHI # log NHI values
dla_meta['z'] = dla_z
dla_meta['ID'] = dla_id
else:
dla_meta = None
return flux*1e17, wave, meta, objmeta, dla_meta
else:
return flux*1e17, wave, meta, objmeta
def main(args=None):
log = get_logger()
if isinstance(args, (list, tuple, type(None))):
args = parse(args)
if args.outfile is not None and len(args.infile)>1 :
log.error("Cannot specify single output file with multiple inputs, use --outdir option instead")
return 1
if not os.path.isdir(args.outdir) :
log.info("Creating dir {}".format(args.outdir))
os.makedirs(args.outdir)
if args.mags:
log.warning('--mags is deprecated; please use --bbflux instead')
args.bbflux = True
exptime = args.exptime
if exptime is None :
exptime = 1000. # sec
if args.eboss:
exptime = 1000. # sec (added here in case we change the default)
#- Generate obsconditions with args.program, then override as needed
obsconditions = reference_conditions[args.program.upper()]
if args.airmass is not None:
obsconditions['AIRMASS'] = args.airmass
if args.seeing is not None:
obsconditions['SEEING'] = args.seeing
if exptime is not None:
obsconditions['EXPTIME'] = exptime
if args.moonfrac is not None:
obsconditions['MOONFRAC'] = args.moonfrac
if args.moonalt is not None:
obsconditions['MOONALT'] = args.moonalt
if args.moonsep is not None:
obsconditions['MOONSEP'] = args.moonsep
if args.no_simqso:
log.info("Load QSO model")
model=QSO()
else:
log.info("Load SIMQSO model")
#lya_simqso_model.py is located in $DESISIM/py/desisim/scripts/.
#Uses a different emmision lines model than the default SIMQSO.
#We will update this soon to match with the one used in select_mock_targets.
model=SIMQSO(nproc=1,sqmodel='lya_simqso_model')
decam_and_wise_filters = None
bassmzls_and_wise_filters = None
if args.target_selection or args.bbflux :
log.info("Load DeCAM and WISE filters for target selection sim.")
# ToDo @moustakas -- load north/south filters
decam_and_wise_filters = filters.load_filters('decam2014-g', 'decam2014-r', 'decam2014-z',
'wise2010-W1', 'wise2010-W2')
bassmzls_and_wise_filters = filters.load_filters('BASS-g', 'BASS-r', 'MzLS-z',
'wise2010-W1', 'wise2010-W2')
footprint_healpix_weight = None
footprint_healpix_nside = None
if args.desi_footprint :
if not 'DESIMODEL' in os.environ :
log.error("To apply DESI footprint, I need the DESIMODEL variable to find the file $DESIMODEL/data/footprint/desi-healpix-weights.fits")
sys.exit(1)
footprint_filename=os.path.join(os.environ['DESIMODEL'],'data','footprint','desi-healpix-weights.fits')
if not os.path.isfile(footprint_filename):
log.error("Cannot find $DESIMODEL/data/footprint/desi-healpix-weights.fits")
sys.exit(1)
pixmap=pyfits.open(footprint_filename)[0].data
footprint_healpix_nside=256 # same resolution as original map so we don't loose anything
footprint_healpix_weight = load_pixweight(footprint_healpix_nside, pixmap=pixmap)
if args.gamma_kms_zfit and not args.zbest:
log.info("Setting --zbest to true as required by --gamma_kms_zfit")
args.zbest = True
if args.extinction:
sfdmap= SFDMap()
else:
sfdmap=None
if args.balprob:
bal=BAL()
else:
bal=None
if args.eboss:
eboss = { 'footprint':FootprintEBOSS(), 'redshift':RedshiftDistributionEBOSS() }
else:
eboss = None
if args.nproc > 1:
func_args = [ {"ifilename":filename , \
"args":args, "model":model , \
"obsconditions":obsconditions , \
"decam_and_wise_filters": decam_and_wise_filters , \
"bassmzls_and_wise_filters": bassmzls_and_wise_filters , \
"footprint_healpix_weight": footprint_healpix_weight , \
"footprint_healpix_nside": footprint_healpix_nside , \
"bal":bal,"sfdmap":sfdmap,"eboss":eboss \
} for i,filename in enumerate(args.infile) ]
pool = multiprocessing.Pool(args.nproc)
pool.map(_func, func_args)
else:
for i,ifilename in enumerate(args.infile) :
simulate_one_healpix(ifilename,args,model,obsconditions,
decam_and_wise_filters,bassmzls_and_wise_filters,
footprint_healpix_weight,footprint_healpix_nside,
bal=bal,sfdmap=sfdmap,eboss=eboss)
class MockSpectra(object):
"""Generate spectra for each type of mock. Currently just choose the closest
template; we can get fancier later.
ToDo (@moustakas): apply Galactic extinction.
"""
def __init__(self, wavemin=None, wavemax=None, dw=0.2, nproc=1,
rand=None, verbose=False):
from desimodel.io import load_throughput
self.tree = TemplateKDTree(nproc=nproc)
# Build a default (buffered) wavelength vector.
if wavemin is None:
wavemin = load_throughput('b').wavemin - 10.0
if wavemax is None:
wavemax = load_throughput('z').wavemax + 10.0
self.wavemin = wavemin
self.wavemax = wavemax
self.dw = dw
self.wave = np.arange(round(wavemin, 1), wavemax, dw)
self.rand = rand
self.verbose = verbose
#self.__normfilter = 'decam2014-r' # default normalization filter
# Initialize the templates once:
from desisim.templates import BGS, ELG, LRG, QSO, STAR, WD
self.bgs_templates = BGS(wave=self.wave, normfilter='sdss2010-r') # Need to generalize this!
self.elg_templates = ELG(wave=self.wave, normfilter='decam2014-r')
self.lrg_templates = LRG(wave=self.wave, normfilter='decam2014-z')
self.qso_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.lya_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.star_templates = STAR(wave=self.wave, normfilter='decam2014-r')
self.wd_da_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DA')
self.wd_db_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DB')
def bgs(self, data, index=None, mockformat='durham_mxxl_hdf5'):
"""Generate spectra for BGS.
Currently only the MXXL (durham_mxxl_hdf5) mock is supported. DATA
needs to have Z, SDSS_absmag_r01, SDSS_01gr, VDISP, and SEED, which are
assigned in mock.io.read_durham_mxxl_hdf5. See also
TemplateKDTree.bgs().
"""
objtype = 'BGS'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'durham_mxxl_hdf5':
alldata = np.vstack((data['Z'][index],
data['SDSS_absmag_r01'][index],
data['SDSS_01gr'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.bgs_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def elg(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the ELG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.elg().
"""
objtype = 'ELG'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
alldata = np.vstack((data['Z'][index],
data['GR'][index],
data['RZ'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
if False:
import matplotlib.pyplot as plt
def elg_colorbox(ax):
"""Draw the ELG selection box."""
from matplotlib.patches import Polygon
grlim = ax.get_ylim()
coeff0, coeff1 = (1.15, -0.15), (-1.2, 1.6)
rzmin, rzpivot = 0.3, (coeff1[1] - coeff0[1]) / (coeff0[0] - coeff1[0])
verts = [(rzmin, grlim[0]),
(rzmin, np.polyval(coeff0, rzmin)),
(rzpivot, np.polyval(coeff1, rzpivot)),
((grlim[0] - 0.1 - coeff1[1]) / coeff1[0], grlim[0] - 0.1)
]
ax.add_patch(Polygon(verts, fill=False, ls='--', color='k'))
fig, ax = plt.subplots()
ax.scatter(data['RZ'][index], data['GR'][index])
ax.set_xlim(-0.5, 2) ; plt.ylim(-0.5, 2)
elg_colorbox(ax)
plt.show()
import pdb ; pdb.set_trace()
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.elg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def elg_test(self, data, index=None, mockformat='gaussianfield'):
"""Test script -- generate spectra for the ELG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.elg().
"""
objtype = 'ELG'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
alldata = np.vstack((data['Z'][index],
data['GR'][index],
data['RZ'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
f1 = interp1d(np.squeeze(self.tree.elg_kcorr['REDSHIFT']), np.squeeze(self.tree.elg_kcorr['GR']), axis=0)
gr = f1(data['Z'][index])
plt.plot(np.squeeze(self.tree.elg_kcorr['REDSHIFT']), np.squeeze(self.tree.elg_kcorr['GR'])[:, 500])
plt.scatter(data['Z'][index], gr[:, 500], marker='x', color='red', s=15)
plt.show()
def elg_colorbox(ax):
"""Draw the ELG selection box."""
from matplotlib.patches import Polygon
grlim = ax.get_ylim()
coeff0, coeff1 = (1.15, -0.15), (-1.2, 1.6)
rzmin, rzpivot = 0.3, (coeff1[1] - coeff0[1]) / (coeff0[0] - coeff1[0])
verts = [(rzmin, grlim[0]),
(rzmin, np.polyval(coeff0, rzmin)),
(rzpivot, np.polyval(coeff1, rzpivot)),
((grlim[0] - 0.1 - coeff1[1]) / coeff1[0], grlim[0] - 0.1)
]
ax.add_patch(Polygon(verts, fill=False, ls='--', color='k'))
fig, ax = plt.subplots()
ax.scatter(data['RZ'][index], data['GR'][index])
ax.set_xlim(-0.5, 2) ; plt.ylim(-0.5, 2)
elg_colorbox(ax)
plt.show()
import pdb ; pdb.set_trace()
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.elg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def lrg(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the LRG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.lrg().
"""
objtype = 'LRG'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
# This is wrong: choose a template with equal probability.
templateid = self.rand.choice(self.tree.lrg_meta['TEMPLATEID'], len(index))
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.lrg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def mws(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the MWS_NEARBY and MWS_MAIN samples.
"""
objtype = 'STAR'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'TEFF', 'LOGG', 'FEH'),
('SEED', 'MAG', 'Z', 'TEFF', 'LOGG', 'FEH')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == '100pc':
alldata = np.vstack((data['TEFF'][index],
data['LOGG'][index],
data['FEH'][index])).T
_, templateid = self.tree.query(objtype, alldata)
elif mockformat.lower() == 'galaxia':
alldata = np.vstack((data['TEFF'][index],
data['LOGG'][index],
data['FEH'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.star_templates.make_templates(input_meta=input_meta,
verbose=self.verbose) # Note! No colorcuts.
return flux, meta
def mws_nearby(self, data, index=None, mockformat='100pc'):
"""Generate spectra for the MWS_NEARBY sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def mws_main(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the MWS_MAIN sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def faintstar(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the FAINTSTAR (faint stellar) sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def mws_wd(self, data, index=None, mockformat='wd'):
"""Generate spectra for the MWS_WD sample. Deal with DA vs DB white dwarfs
separately.
"""
objtype = 'WD'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
input_meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'TEFF', 'LOGG', 'SUBTYPE'),
('SEED', 'MAG', 'Z', 'TEFF', 'LOGG', 'TEMPLATESUBTYPE')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'wd':
meta = empty_metatable(nmodel=nobj, objtype=objtype)
flux = np.zeros([nobj, len(self.wave)], dtype='f4')
for subtype in ('DA', 'DB'):
these = np.where(input_meta['SUBTYPE'] == subtype)[0]
if len(these) > 0:
alldata = np.vstack((data['TEFF'][index][these],
data['LOGG'][index][these])).T
_, templateid = self.tree.query(objtype, alldata, subtype=subtype)
input_meta['TEMPLATEID'][these] = templateid
template_function = 'wd_{}_templates'.format(subtype.lower())
flux1, _, meta1 = getattr(self, template_function).make_templates(input_meta=input_meta[these],
verbose=self.verbose)
meta[these] = meta1
flux[these, :] = flux1
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return flux, meta
def qso(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the QSO or QSO/LYA samples.
Note: We need to make sure NORMFILTER matches!
"""
from desisim.lya_spectra import get_spectra
objtype = 'QSO'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
if mockformat.lower() == 'gaussianfield':
input_meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT'),
('SEED', 'MAG', 'Z')):
input_meta[inkey] = data[datakey][index]
# Build the tracer and Lya forest QSO spectra separately.
meta = empty_metatable(nmodel=nobj, objtype=objtype)
flux = np.zeros([nobj, len(self.wave)], dtype='f4')
lya = np.where( data['TEMPLATESUBTYPE'][index] == 'LYA' )[0]
tracer = np.where( data['TEMPLATESUBTYPE'][index] == '' )[0]
if len(tracer) > 0:
flux1, _, meta1 = self.qso_templates.make_templates(input_meta=input_meta[tracer],
lyaforest=False,
nocolorcuts=True,
verbose=self.verbose)
meta[tracer] = meta1
flux[tracer, :] = flux1
if len(lya) > 0:
alllyafile = data['LYAFILES'][index][lya]
alllyahdu = data['LYAHDU'][index][lya]
for lyafile in sorted(set(alllyafile)):
these = np.where( lyafile == alllyafile )[0]
templateid = alllyahdu[these] - 1 # templateid is 0-indexed
flux1, _, meta1 = get_spectra(lyafile, templateid=templateid, normfilter=data['FILTERNAME'],
rand=self.rand, qso=self.lya_templates, nocolorcuts=True)
meta1['SUBTYPE'] = 'LYA'
meta[lya[these]] = meta1
flux[lya[these], :] = flux1
elif mockformat.lower() == 'lya':
# Build spectra for Lyman-alpha QSOs. Deprecated!
from desisim.lya_spectra import get_spectra
from desitarget.mock.io import decode_rownum_filenum
meta = empty_metatable(nmodel=nobj, objtype=objtype)
flux = np.zeros([nobj, len(self.wave)], dtype='f4')
rowindx, fileindx = decode_rownum_filenum(data['MOCKID'][index])
for indx1 in set(fileindx):
lyafile = data['FILES'][indx1]
these = np.where(indx1 == fileindx)[0]
templateid = rowindx[these].astype('int')
flux1, _, meta1 = get_spectra(lyafile, templateid=templateid,
normfilter=data['FILTERNAME'],
rand=self.rand, qso=self.lya_templates)
meta[these] = meta1
flux[these, :] = flux1
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return flux, meta
def sky(self, data, index=None, mockformat=None):
"""Generate spectra for SKY.
"""
objtype = 'SKY'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'REDSHIFT'),
('SEED', 'Z')):
meta[inkey] = data[datakey][index]
flux = np.zeros((nobj, len(self.wave)), dtype='i1')
return flux, meta
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
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,
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)
class MockSpectra(object):
"""Generate spectra for each type of mock. Currently just choose the closest
template; we can get fancier later.
ToDo (@moustakas): apply Galactic extinction.
"""
def __init__(self, wavemin=None, wavemax=None, dw=0.2, nproc=1,
rand=None, verbose=False):
from lvmmodel.io import load_throughput
self.tree = TemplateKDTree(nproc=nproc)
# Build a default (buffered) wavelength vector.
if wavemin is None:
wavemin = load_throughput('b').wavemin - 10.0
if wavemax is None:
wavemax = load_throughput('z').wavemax + 10.0
self.wavemin = wavemin
self.wavemax = wavemax
self.dw = dw
self.wave = np.arange(round(wavemin, 1), wavemax, dw)
self.rand = rand
self.verbose = verbose
#self.__normfilter = 'decam2014-r' # default normalization filter
# Initialize the templates once:
from desisim.templates import BGS, ELG, LRG, QSO, STAR, WD
self.bgs_templates = BGS(wave=self.wave, normfilter='sdss2010-r') # Need to generalize this!
self.elg_templates = ELG(wave=self.wave, normfilter='decam2014-r')
self.lrg_templates = LRG(wave=self.wave, normfilter='decam2014-z')
self.qso_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.lya_templates = QSO(wave=self.wave, normfilter='decam2014-g')
self.star_templates = STAR(wave=self.wave, normfilter='decam2014-r')
self.wd_da_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DA')
self.wd_db_templates = WD(wave=self.wave, normfilter='decam2014-g', subtype='DB')
def bgs(self, data, index=None, mockformat='durham_mxxl_hdf5'):
"""Generate spectra for BGS.
Currently only the MXXL (durham_mxxl_hdf5) mock is supported. DATA
needs to have Z, SDSS_absmag_r01, SDSS_01gr, VDISP, and SEED, which are
assigned in mock.io.read_durham_mxxl_hdf5. See also
TemplateKDTree.bgs().
"""
objtype = 'BGS'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'durham_mxxl_hdf5':
alldata = np.vstack((data['Z'][index],
data['SDSS_absmag_r01'][index],
data['SDSS_01gr'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.bgs_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def elg(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the ELG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.elg().
"""
objtype = 'ELG'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
alldata = np.vstack((data['Z'][index],
data['GR'][index],
data['RZ'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
if False:
import matplotlib.pyplot as plt
def elg_colorbox(ax):
"""Draw the ELG selection box."""
from matplotlib.patches import Polygon
grlim = ax.get_ylim()
coeff0, coeff1 = (1.15, -0.15), (-1.2, 1.6)
rzmin, rzpivot = 0.3, (coeff1[1] - coeff0[1]) / (coeff0[0] - coeff1[0])
verts = [(rzmin, grlim[0]),
(rzmin, np.polyval(coeff0, rzmin)),
(rzpivot, np.polyval(coeff1, rzpivot)),
((grlim[0] - 0.1 - coeff1[1]) / coeff1[0], grlim[0] - 0.1)
]
ax.add_patch(Polygon(verts, fill=False, ls='--', color='k'))
fig, ax = plt.subplots()
ax.scatter(data['RZ'][index], data['GR'][index])
ax.set_xlim(-0.5, 2) ; plt.ylim(-0.5, 2)
elg_colorbox(ax)
plt.show()
import pdb ; pdb.set_trace()
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.elg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def elg_test(self, data, index=None, mockformat='gaussianfield'):
"""Test script -- generate spectra for the ELG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.elg().
"""
objtype = 'ELG'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
alldata = np.vstack((data['Z'][index],
data['GR'][index],
data['RZ'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
f1 = interp1d(np.squeeze(self.tree.elg_kcorr['REDSHIFT']), np.squeeze(self.tree.elg_kcorr['GR']), axis=0)
gr = f1(data['Z'][index])
plt.plot(np.squeeze(self.tree.elg_kcorr['REDSHIFT']), np.squeeze(self.tree.elg_kcorr['GR'])[:, 500])
plt.scatter(data['Z'][index], gr[:, 500], marker='x', color='red', s=15)
plt.show()
def elg_colorbox(ax):
"""Draw the ELG selection box."""
from matplotlib.patches import Polygon
grlim = ax.get_ylim()
coeff0, coeff1 = (1.15, -0.15), (-1.2, 1.6)
rzmin, rzpivot = 0.3, (coeff1[1] - coeff0[1]) / (coeff0[0] - coeff1[0])
verts = [(rzmin, grlim[0]),
(rzmin, np.polyval(coeff0, rzmin)),
(rzpivot, np.polyval(coeff1, rzpivot)),
((grlim[0] - 0.1 - coeff1[1]) / coeff1[0], grlim[0] - 0.1)
]
ax.add_patch(Polygon(verts, fill=False, ls='--', color='k'))
fig, ax = plt.subplots()
ax.scatter(data['RZ'][index], data['GR'][index])
ax.set_xlim(-0.5, 2) ; plt.ylim(-0.5, 2)
elg_colorbox(ax)
plt.show()
import pdb ; pdb.set_trace()
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.elg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def lrg(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the LRG sample.
Currently only the GaussianField mock sample is supported. DATA needs
to have Z, GR, RZ, VDISP, and SEED, which are assigned in
mock.io.read_gaussianfield. See also TemplateKDTree.lrg().
"""
objtype = 'LRG'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'gaussianfield':
# This is wrong: choose a template with equal probability.
templateid = self.rand.choice(self.tree.lrg_meta['TEMPLATEID'], len(index))
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.lrg_templates.make_templates(input_meta=input_meta,
nocolorcuts=True, novdisp=False,
verbose=self.verbose)
return flux, meta
def mws(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the MWS_NEARBY and MWS_MAIN samples.
"""
objtype = 'STAR'
if index is None:
index = np.arange(len(data['Z']))
input_meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'TEFF', 'LOGG', 'FEH'),
('SEED', 'MAG', 'Z', 'TEFF', 'LOGG', 'FEH')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == '100pc':
alldata = np.vstack((data['TEFF'][index],
data['LOGG'][index],
data['FEH'][index])).T
_, templateid = self.tree.query(objtype, alldata)
elif mockformat.lower() == 'galaxia':
alldata = np.vstack((data['TEFF'][index],
data['LOGG'][index],
data['FEH'][index])).T
_, templateid = self.tree.query(objtype, alldata)
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
input_meta['TEMPLATEID'] = templateid
flux, _, meta = self.star_templates.make_templates(input_meta=input_meta,
verbose=self.verbose) # Note! No colorcuts.
return flux, meta
def mws_nearby(self, data, index=None, mockformat='100pc'):
"""Generate spectra for the MWS_NEARBY sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def mws_main(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the MWS_MAIN sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def faintstar(self, data, index=None, mockformat='galaxia'):
"""Generate spectra for the FAINTSTAR (faint stellar) sample.
"""
flux, meta = self.mws(data, index=index, mockformat=mockformat)
return flux, meta
def mws_wd(self, data, index=None, mockformat='wd'):
"""Generate spectra for the MWS_WD sample. Deal with DA vs DB white dwarfs
separately.
"""
objtype = 'WD'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
input_meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'TEFF', 'LOGG', 'SUBTYPE'),
('SEED', 'MAG', 'Z', 'TEFF', 'LOGG', 'TEMPLATESUBTYPE')):
input_meta[inkey] = data[datakey][index]
if mockformat.lower() == 'wd':
meta = empty_metatable(nmodel=nobj, objtype=objtype)
flux = np.zeros([nobj, len(self.wave)], dtype='f4')
for subtype in ('DA', 'DB'):
these = np.where(input_meta['SUBTYPE'] == subtype)[0]
if len(these) > 0:
alldata = np.vstack((data['TEFF'][index][these],
data['LOGG'][index][these])).T
_, templateid = self.tree.query(objtype, alldata, subtype=subtype)
input_meta['TEMPLATEID'][these] = templateid
template_function = 'wd_{}_templates'.format(subtype.lower())
flux1, _, meta1 = getattr(self, template_function).make_templates(input_meta=input_meta[these],
verbose=self.verbose)
meta[these] = meta1
flux[these, :] = flux1
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return flux, meta
def qso(self, data, index=None, mockformat='gaussianfield'):
"""Generate spectra for the QSO or QSO/LYA samples.
Note: We need to make sure NORMFILTER matches!
"""
from desisim.lya_spectra import get_spectra
from desisim.lya_spectra import read_lya_skewers,apply_lya_transmission
import fitsio
log = get_logger()
objtype = 'QSO'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
if mockformat.lower() == 'gaussianfield':
input_meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT'),
('SEED', 'MAG', 'Z')):
input_meta[inkey] = data[datakey][index]
# Build the tracer and Lya forest QSO spectra separately.
meta = empty_metatable(nmodel=nobj, objtype=objtype)
flux = np.zeros([nobj, len(self.wave)], dtype='f4')
lya = np.where( data['TEMPLATESUBTYPE'][index] == 'LYA' )[0]
tracer = np.where( data['TEMPLATESUBTYPE'][index] == '' )[0]
if len(tracer) > 0:
flux1, _, meta1 = self.qso_templates.make_templates(input_meta=input_meta[tracer],
lyaforest=False,
nocolorcuts=True,
verbose=self.verbose)
meta[tracer] = meta1
flux[tracer, :] = flux1
if len(lya) > 0:
ilya=index[lya].astype(int)
nqso=ilya.size
log.info("Generating spectra of %d lya QSOs"%nqso)
if 'LYAHDU' in data :
# this is the old format with one HDU per spectrum
alllyafile = data['LYAFILES'][ilya]
alllyahdu = data['LYAHDU'][ilya]
for lyafile in sorted(set(alllyafile)):
these = np.where( lyafile == alllyafile )[0]
templateid = alllyahdu[these] - 1 # templateid is 0-indexed
flux1, _, meta1 = get_spectra(lyafile, templateid=templateid, normfilter=data['FILTERNAME'],
rand=self.rand, qso=self.lya_templates, nocolorcuts=True)
meta1['SUBTYPE'] = 'LYA'
meta[lya[these]] = meta1
flux[lya[these], :] = flux1
else : # new format
# read skewers
skewer_wave=None
skewer_trans=None
skewer_meta=None
# all the files that contain at least one QSO skewer
alllyafile = data['LYAFILES'][ilya]
uniquelyafiles = sorted(set(alllyafile))
for lyafile in uniquelyafiles :
these = np.where( alllyafile == lyafile )[0]
objid_in_data=data['OBJID'][ilya][these]
objid_in_mock=(fitsio.read(lyafile, columns=['MOCKID'],upper=True,ext=1).astype(float)).astype(int)
o2i=dict()
for i,o in enumerate(objid_in_mock) :
o2i[o]=i
indices_in_mock_healpix=np.zeros(objid_in_data.size).astype(int)
for i,o in enumerate(objid_in_data) :
if not o in o2i :
log.error("No MOCKID={} in {}. It's a bug, should never happen".format(o,lyafile))
raise(KeyError("No MOCKID={} in {}. It's a bug, should never happen".format(o,lyafile)))
indices_in_mock_healpix[i]=o2i[o]
tmp_wave,tmp_trans,tmp_meta = read_lya_skewers(lyafile,indices=indices_in_mock_healpix)
if skewer_wave is None :
skewer_wave=tmp_wave
dw=skewer_wave[1]-skewer_wave[0] # this is just to check same wavelength
skewer_trans=np.zeros((nqso,skewer_wave.size)) # allocate skewer_array
skewer_meta=dict()
for k in tmp_meta.dtype.names :
skewer_meta[k]=np.zeros(nqso).astype(tmp_meta[k].dtype)
else :
# check wavelength is the same for all skewers
assert(np.max(np.abs(wave-tmp_wave))<0.001*dw)
skewer_trans[these] = tmp_trans
for k in skewer_meta.keys() :
skewer_meta[k][these]=tmp_meta[k]
# check we matched things correctly
assert(np.max(np.abs(skewer_meta["Z"]-data['Z'][ilya]))<0.000001)
assert(np.max(np.abs(skewer_meta["RA"]-data['RA'][ilya]))<0.000001)
assert(np.max(np.abs(skewer_meta["DEC"]-data['DEC'][ilya]))<0.000001)
# now we create a series of QSO spectra all at once
# this is faster than calling each one at a time
# we use the provided QSO template class
seed = self.rand.randint(2**32)
qso = self.lya_templates
qso_flux, qso_wave, qso_meta = qso.make_templates(nmodel=nqso,
redshift=data['Z'][ilya],
mag=data['MAG'][ilya],
seed=seed,
lyaforest=False,
nocolorcuts=True)
# apply transmission to QSOs
qso_flux = apply_lya_transmission(qso_wave,qso_flux,skewer_wave,skewer_trans)
# save this
qso_meta['SUBTYPE'] = 'LYA'
meta[lya] = qso_meta
flux[lya, :] = qso_flux
else:
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return flux, meta
def sky(self, data, index=None, mockformat=None):
"""Generate spectra for SKY.
"""
objtype = 'SKY'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'REDSHIFT'),
('SEED', 'Z')):
meta[inkey] = data[datakey][index]
flux = np.zeros((nobj, len(self.wave)), dtype='i1')
return flux, meta
def get_spectra(lyafile,
nqso=None,
wave=None,
templateid=None,
normfilter='sdss2010-g',
seed=None,
rand=None,
qso=None,
nocolorcuts=False):
'''Generate a QSO spectrum which includes Lyman-alpha absorption.
Args:
lyafile (str): name of the Lyman-alpha spectrum file to read.
nqso (int, optional): number of spectra to generate (starting from the
first spectrum; if more flexibility is needed use TEMPLATEID).
wave (numpy.ndarray, optional): desired output wavelength vector.
templateid (int numpy.ndarray, optional): indices of the spectra
(0-indexed) to read from LYAFILE (default is to read everything). If
provided together with NQSO, TEMPLATEID wins.
normfilter (str, optional): normalization filter
seed (int, optional): Seed for random number generator.
rand (numpy.RandomState, optional): RandomState object used for the
random number generation. If provided together with SEED, this
optional input superseeds the numpy.RandomState object instantiated by
SEED.
qso (desisim.templates.QSO, optional): object with which to generate
individual spectra/templates.
nocolorcuts (bool, optional): Do not apply the fiducial rzW1W2 color-cuts
cuts (default False).
Returns:
flux (numpy.ndarray):
Array [nmodel, npix] of observed-frame spectra (erg/s/cm2/A).
wave (numpy.ndarray):
Observed-frame [npix] wavelength array (Angstrom).
meta (astropy.Table):
Table of meta-data [nmodel] for each output spectrum
with columns defined in desisim.io.empty_metatable *plus* RA, DEC.
'''
import numpy as np
from scipy.interpolate import interp1d
import fitsio
from speclite.filters import load_filters
from desisim.templates import QSO
from desisim.io import empty_metatable
h = fitsio.FITS(lyafile)
if templateid is None:
if nqso is None:
nqso = len(h) - 1
templateid = np.arange(nqso)
else:
templateid = np.asarray(templateid)
nqso = len(templateid)
if rand is None:
rand = np.random.RandomState(seed)
templateseed = rand.randint(2**32, size=nqso)
#heads = [head.read_header() for head in h[templateid + 1]]
heads = []
for indx in templateid:
heads.append(h[indx + 1].read_header())
zqso = np.array([head['ZQSO'] for head in heads])
ra = np.array([head['RA'] for head in heads])
dec = np.array([head['DEC'] for head in heads])
mag_g = np.array([head['MAG_G'] for head in heads])
# Hard-coded filtername!
normfilt = load_filters(normfilter)
if qso is None:
qso = QSO(normfilter=normfilter, wave=wave)
wave = qso.wave
flux = np.zeros([nqso, len(wave)], dtype='f4')
meta = empty_metatable(objtype='QSO', nmodel=nqso)
meta['TEMPLATEID'] = templateid
meta['REDSHIFT'] = zqso
meta['MAG'] = mag_g
meta['SEED'] = templateseed
meta['RA'] = ra
meta['DEC'] = dec
for ii, indx in enumerate(templateid):
flux1, _, meta1 = qso.make_templates(nmodel=1,
redshift=np.atleast_1d(zqso[ii]),
mag=np.atleast_1d(mag_g[ii]),
seed=templateseed[ii],
nocolorcuts=nocolorcuts)
flux1 *= 1e-17
for col in meta1.colnames:
meta[col][ii] = meta1[col][0]
# read lambda and forest transmission
data = h[indx + 1].read()
la = data['LAMBDA'][:]
tr = data['FLUX'][:]
if len(tr):
# Interpolate the transmission at the spectral wavelengths, if
# outside the forest, the transmission is 1.
itr = interp1d(la, tr, bounds_error=False, fill_value=1.0)
flux1 *= itr(wave)
padflux, padwave = normfilt.pad_spectrum(flux1, wave, method='edge')
normmaggies = np.array(
normfilt.get_ab_maggies(padflux, padwave,
mask_invalid=True)[normfilter])
factor = 10**(-0.4 * mag_g[ii]) / normmaggies
flux1 *= factor
for key in ('FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'):
meta[key][ii] *= factor
flux[ii, :] = flux1[:]
h.close()
return 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['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)