def main():
seed = 123
ngal = 100
npergal = 50
# Read the mock catalog.
cat = h5py.File('galaxy_catalogue_0.hdf5')
gr = cat['Data']['g_r'][:ngal]
mag = cat['Data']['app_mag'][:ngal]
z = cat['Data']['z_obs'][:ngal]
zmin, zmax = np.min(z), np.max(z)
# Read the basis templates and initialize the output metadata table.
baseflux, basewave, basemeta = read_basis_templates('BGS')
meta = empty_metatable(objtype='BGS', nmodel=ngal)
bgs = BGS(minwave=3000, maxwave=11000.0)
for igal in range(ngal):
flux, wave, meta = bgs.make_templates(npergal, zrange=(zmin, zmax),
nocolorcuts=True)
pdb.set_trace()
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 BGSKDTree.bgs().
"""
from desisim.io import empty_metatable
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 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 lrg(self, data, index=None, mockformat='gaussianfield'):
"""Generate magnitudes 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.
"""
objtype = 'LRG'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
meta[inkey] = data[datakey][index]
if mockformat.lower() != 'gaussianfield':
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return meta
meta['FLUX_G'][:] = 10**((22.5 - (data['GR'][index] + data['RZ'][index] + data['MAG'][index]))/2.5) # g-band flux
meta['FLUX_R'][:] = 10**((22.5 - (data['MAG'][index] + data['RZ'][index]))/2.5) # r-band flux
meta['FLUX_Z'][:] = 10**((22.5 - data['MAG'][index])/2.5) # z-band flux
meta['FLUX_W1'][:] = 10**((22.5 - (data['RZ'][index] + data['MAG'][index] - data['RW1'][index]))/2.5)# wise flux 1
meta['FLUX_W2'][:] = 10**((22.5 - (data['RZ'][index] + data['MAG'][index] - data['RW1'][index] - data['W1W2'][index]))/2.5)# wise flux 2
return 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 qso(self, data, index=None, mockformat='gaussianfield'):
"""Generate magnitudes for the QSO or QSO/LYA samples.
"""
from desisim.lya_spectra import get_spectra
objtype = 'QSO'
if index is None:
index = np.arange(len(data['Z']))
nobj = len(index)
meta = empty_metatable(nmodel=nobj, objtype=objtype)
if mockformat.lower() != 'gaussianfield':
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return meta
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT'),
('SEED', 'MAG', 'Z')):
meta[inkey] = data[datakey][index]
meta['FLUX_G'][:] = 10**((22.5 - data['MAG'][index])/2.5) # g-band flux
meta['FLUX_R'][:] = 10**((22.5 - (data['MAG'][index] - data['GR'][index]))/2.5) # r-band flux
meta['FLUX_Z'][:] = 10**((22.5 - (data['MAG'][index] - data['GR'][index] - data['RZ'][index]))/2.5) # z-band flux
meta['FLUX_W1'][:] = 10**((22.5 - (data['MAG'][index] - data['GR'][index] - data['RW1'][index]))/2.5)# wise flux 1
meta['FLUX_W2'][:] = 10**((22.5 - (data['MAG'][index] - data['GR'][index] - data['RW1'][index] - data['W1W2'][index]))/2.5)# wise flux 2
lya = np.where( data['TEMPLATESUBTYPE'][index] == 'LYA' )[0]
if len(lya) > 0:
meta['SUBTYPE'][lya] = 'LYA'
return meta
def mws_wd(self, data, index=None, mockformat='wd'):
"""Generate magnitudes 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)
meta = empty_metatable(nmodel=nobj, objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'TEFF', 'LOGG', 'SUBTYPE'),
('SEED', 'MAG', 'Z', 'TEFF', 'LOGG', 'TEMPLATESUBTYPE')):
meta[inkey] = data[datakey][index]
if mockformat.lower() != 'wd':
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return meta
for subtype in ('DA', 'DB'):
these = np.where(meta['SUBTYPE'] == subtype)[0]
if len(these) > 0:
meta['SUBTYPE'][these] = subtype
meta['TEMPLATEID'][:] = -1
meta['FLUX_G'][:] = 10**((22.5 - data['MAG'][index])/2.5) # g-band flux
return 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 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]
return meta
def mws(self, data, index=None, mockformat='galaxia'):
"""Generate magnitudes for the MWS_NEARBY and MWS_MAIN samples.
"""
objtype = 'STAR'
if index is None:
index = np.arange(len(data['Z']))
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')):
meta[inkey] = data[datakey][index]
if not (mockformat.lower() in ['100pc','galaxia']):
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return meta
meta['FLUX_R'][:] = 10**((22.5 - data['MAG'][index])/2.5) # r-band flux
return meta
def bgs(self, data, index=None, mockformat='durham_mxxl_hdf5'):
"""Generate magnitudes 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.
"""
objtype = 'BGS'
if index is None:
index = np.arange(len(data['Z']))
meta = empty_metatable(nmodel=len(index), objtype=objtype)
for inkey, datakey in zip(('SEED', 'MAG', 'REDSHIFT', 'VDISP'),
('SEED', 'MAG', 'Z', 'VDISP')):
meta[inkey] = data[datakey][index]
if mockformat.lower() != 'durham_mxxl_hdf5':
raise ValueError('Unrecognized mockformat {}!'.format(mockformat))
return meta
meta['FLUX_R'][:] = 10**((22.5 - data['MAG'][index])/2.5) # r-band flux
meta['FLUX_G'][:] = 10**((22.5-(data['SDSS_01gr'][index] + data['MAG'][index]))/2.5) # g-band flux
return meta
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 simulate_one_healpix(ifilename,args,model,obsconditions,decam_and_wise_filters,
bassmzls_and_wise_filters,footprint_healpix_weight,
footprint_healpix_nside,
bal=None,sfdmap=None,eboss=None) :
log = get_logger()
# open filename and extract basic HEALPix information
pixel, nside, hpxnest = get_healpix_info(ifilename)
# using global seed (could be None) get seed for this particular pixel
global_seed = args.seed
seed = get_pixel_seed(pixel, nside, global_seed)
# use this seed to generate future random numbers
np.random.seed(seed)
# get output file (we will write there spectra for this HEALPix pixel)
ofilename = get_spectra_filename(args,nside,pixel)
# get directory name (we will also write there zbest file)
pixdir = os.path.dirname(ofilename)
# get filename for truth file
truth_filename = get_truth_filename(args,pixdir,nside,pixel)
# get filename for zbest file
zbest_filename = get_zbest_filename(args,pixdir,nside,pixel)
if not args.overwrite :
# check whether output exists or not
if args.zbest :
if os.path.isfile(ofilename) and os.path.isfile(zbest_filename) :
log.info("skip existing {} and {}".format(ofilename,zbest_filename))
return
else : # only test spectra file
if os.path.isfile(ofilename) :
log.info("skip existing {}".format(ofilename))
return
# create sub-directories if required
if len(pixdir)>0 :
if not os.path.isdir(pixdir) :
log.info("Creating dir {}".format(pixdir))
os.makedirs(pixdir)
log.info("Read skewers in {}, random seed = {}".format(ifilename,seed))
# Read transmission from files. It might include DLA information, and it
# might add metal transmission as well (from the HDU file).
log.info("Read transmission file {}".format(ifilename))
trans_wave, transmission, metadata, dla_info = read_lya_skewers(ifilename,read_dlas=(args.dla=='file'),add_metals=args.metals_from_file,add_lyb=args.add_LYB)
### Add Finger-of-God, before generate the continua
log.info("Add FOG to redshift with sigma {} to quasar redshift".format(args.sigma_kms_fog))
DZ_FOG = args.sigma_kms_fog/c*(1.+metadata['Z'])*np.random.normal(0,1,metadata['Z'].size)
metadata['Z'] += DZ_FOG
### Select quasar within a given redshift range
w = (metadata['Z']>=args.zmin) & (metadata['Z']<=args.zmax)
transmission = transmission[w]
metadata = metadata[:][w]
DZ_FOG = DZ_FOG[w]
# option to make for BOSS+eBOSS
if not eboss is None:
if args.downsampling or args.desi_footprint:
raise ValueError("eboss option can not be run with "
+"desi_footprint or downsampling")
# Get the redshift distribution from SDSS
selection = sdss_subsample_redshift(metadata["RA"],metadata["DEC"],metadata['Z'],eboss['redshift'])
log.info("Select QSOs in BOSS+eBOSS redshift distribution {} -> {}".format(metadata['Z'].size,selection.sum()))
if selection.sum()==0:
log.warning("No intersection with BOSS+eBOSS redshift distribution")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
# figure out the density of all quasars
N_highz = metadata['Z'].size
# area of healpix pixel, in degrees
area_deg2 = healpy.pixelfunc.nside2pixarea(nside,degrees=True)
input_highz_dens_deg2 = N_highz/area_deg2
selection = sdss_subsample(metadata["RA"], metadata["DEC"],
input_highz_dens_deg2,eboss['footprint'])
log.info("Select QSOs in BOSS+eBOSS footprint {} -> {}".format(transmission.shape[0],selection.size))
if selection.size == 0 :
log.warning("No intersection with BOSS+eBOSS footprint")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
if args.desi_footprint :
footprint_healpix = footprint.radec2pix(footprint_healpix_nside, metadata["RA"], metadata["DEC"])
selection = np.where(footprint_healpix_weight[footprint_healpix]>0.99)[0]
log.info("Select QSOs in DESI footprint {} -> {}".format(transmission.shape[0],selection.size))
if selection.size == 0 :
log.warning("No intersection with DESI footprint")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
nqso=transmission.shape[0]
if args.downsampling is not None :
if args.downsampling <= 0 or args.downsampling > 1 :
log.error("Down sampling fraction={} must be between 0 and 1".format(args.downsampling))
raise ValueError("Down sampling fraction={} must be between 0 and 1".format(args.downsampling))
indices = np.where(np.random.uniform(size=nqso)<args.downsampling)[0]
if indices.size == 0 :
log.warning("Down sampling from {} to 0 (by chance I presume)".format(nqso))
return
transmission = transmission[indices]
metadata = metadata[:][indices]
DZ_FOG = DZ_FOG[indices]
nqso = transmission.shape[0]
if args.nmax is not None :
if args.nmax < nqso :
log.info("Limit number of QSOs from {} to nmax={} (random subsample)".format(nqso,args.nmax))
# take a random subsample
indices = np.random.choice(np.arange(nqso),args.nmax,replace=False) ##Use random.choice instead of random.uniform (rarely but it does cause a duplication of qsos)
transmission = transmission[indices]
metadata = metadata[:][indices]
DZ_FOG = DZ_FOG[indices]
nqso = args.nmax
# In previous versions of the London mocks we needed to enforce F=1 for
# z > z_qso here, but this is not needed anymore. Moreover, now we also
# have metal absorption that implies F < 1 for z > z_qso
#for ii in range(len(metadata)):
# transmission[ii][trans_wave>lambda_RF_LYA*(metadata[ii]['Z']+1)]=1.0
# if requested, add DLA to the transmission skewers
if args.dla is not None :
# if adding random DLAs, we will need a new random generator
if args.dla=='random':
log.info('Adding DLAs randomly')
random_state_just_for_dlas = np.random.RandomState(seed)
elif args.dla=='file':
log.info('Adding DLAs from transmission file')
else:
log.error("Wrong option for args.dla: "+args.dla)
sys.exit(1)
# if adding DLAs, the information will be printed here
dla_filename=os.path.join(pixdir,"dla-{}-{}.fits".format(nside,pixel))
dla_NHI, dla_z, dla_qid,dla_id = [], [], [],[]
# identify minimum Lya redshift in transmission files
min_lya_z = np.min(trans_wave/lambda_RF_LYA - 1)
# loop over quasars in pixel
for ii in range(len(metadata)):
# quasars with z < min_z will not have any DLA in spectrum
if min_lya_z>metadata['Z'][ii]: continue
# quasar ID
idd=metadata['MOCKID'][ii]
dlas=[]
if args.dla=='file':
for dla in dla_info[dla_info['MOCKID']==idd]:
# Adding only DLAs with z < zqso
if dla['Z_DLA_RSD']>=metadata['Z'][ii]: continue
dlas.append(dict(z=dla['Z_DLA_RSD'],N=dla['N_HI_DLA'],dlaid=dla['DLAID']))
transmission_dla = dla_spec(trans_wave,dlas)
elif args.dla=='random':
dlas, transmission_dla = insert_dlas(trans_wave, metadata['Z'][ii], rstate=random_state_just_for_dlas)
for idla in dlas:
idla['dlaid']+=idd*1000 #Added to have unique DLA ids. Same format as DLAs from file.
# multiply transmissions and store information for the DLA file
if len(dlas)>0:
transmission[ii] = transmission_dla * transmission[ii]
dla_z += [idla['z'] for idla in dlas]
dla_NHI += [idla['N'] for idla in dlas]
dla_id += [idla['dlaid'] for idla in dlas]
dla_qid += [idd]*len(dlas)
log.info('Added {} DLAs'.format(len(dla_id)))
# write file with DLA information
if len(dla_id)>0:
dla_meta=Table()
dla_meta['NHI'] = dla_NHI
dla_meta['Z_DLA'] = dla_z #This is Z_DLA_RSD in transmision.
dla_meta['TARGETID']=dla_qid
dla_meta['DLAID'] = dla_id
hdu_dla = pyfits.convenience.table_to_hdu(dla_meta)
hdu_dla.name="DLA_META"
del(dla_meta)
log.info("DLA metadata to be saved in {}".format(truth_filename))
else:
hdu_dla=pyfits.PrimaryHDU()
hdu_dla.name="DLA_META"
# if requested, extend transmission skewers to cover full spectrum
if args.target_selection or args.bbflux :
wanted_min_wave = 3329. # needed to compute magnitudes for decam2014-r (one could have trimmed the transmission file ...)
wanted_max_wave = 55501. # needed to compute magnitudes for wise2010-W2
if trans_wave[0]>wanted_min_wave :
log.info("Increase wavelength range from {}:{} to {}:{} to compute magnitudes".format(int(trans_wave[0]),int(trans_wave[-1]),int(wanted_min_wave),int(trans_wave[-1])))
# pad with ones at short wavelength, we assume F = 1 for z <~ 1.7
# we don't need any wavelength resolution here
new_trans_wave = np.append([wanted_min_wave,trans_wave[0]-0.01],trans_wave)
new_transmission = np.ones((transmission.shape[0],new_trans_wave.size))
new_transmission[:,2:] = transmission
trans_wave = new_trans_wave
transmission = new_transmission
if trans_wave[-1]<wanted_max_wave :
log.info("Increase wavelength range from {}:{} to {}:{} to compute magnitudes".format(int(trans_wave[0]),int(trans_wave[-1]),int(trans_wave[0]),int(wanted_max_wave)))
# pad with ones at long wavelength because we assume F = 1
coarse_dwave = 2. # we don't care about resolution, we just need a decent QSO spectrum, there is no IGM transmission in this range
n = int((wanted_max_wave-trans_wave[-1])/coarse_dwave)+1
new_trans_wave = np.append(trans_wave,np.linspace(trans_wave[-1]+coarse_dwave,trans_wave[-1]+coarse_dwave*(n+1),n))
new_transmission = np.ones((transmission.shape[0],new_trans_wave.size))
new_transmission[:,:trans_wave.size] = transmission
trans_wave = new_trans_wave
transmission = new_transmission
# whether to use QSO or SIMQSO to generate quasar continua. Simulate
# spectra in the north vs south separately because they're on different
# photometric systems.
south = np.where( is_south(metadata['DEC']) )[0]
north = np.where( ~is_south(metadata['DEC']) )[0]
meta, qsometa = empty_metatable(nqso, objtype='QSO', simqso=not args.no_simqso)
if args.no_simqso:
log.info("Simulate {} QSOs with QSO templates".format(nqso))
tmp_qso_flux = np.zeros([nqso, len(model.eigenwave)], dtype='f4')
tmp_qso_wave = np.zeros_like(tmp_qso_flux)
else:
log.info("Simulate {} QSOs with SIMQSO templates".format(nqso))
tmp_qso_flux = np.zeros([nqso, len(model.basewave)], dtype='f4')
tmp_qso_wave = model.basewave
for these, issouth in zip( (north, south), (False, True) ):
# number of quasars in these
nt = len(these)
if nt<=0: continue
if not eboss is None:
# for eBOSS, generate only quasars with r<22
magrange = (17.0, 21.3)
_tmp_qso_flux, _tmp_qso_wave, _meta, _qsometa \
= model.make_templates(nmodel=nt,
redshift=metadata['Z'][these], magrange=magrange,
lyaforest=False, nocolorcuts=True,
noresample=True, seed=seed, south=issouth)
else:
_tmp_qso_flux, _tmp_qso_wave, _meta, _qsometa \
= model.make_templates(nmodel=nt,
redshift=metadata['Z'][these],
lyaforest=False, nocolorcuts=True,
noresample=True, seed=seed, south=issouth)
_meta['TARGETID'] = metadata['MOCKID'][these]
_qsometa['TARGETID'] = metadata['MOCKID'][these]
meta[these] = _meta
qsometa[these] = _qsometa
tmp_qso_flux[these, :] = _tmp_qso_flux
if args.no_simqso:
tmp_qso_wave[these, :] = _tmp_qso_wave
log.info("Resample to transmission wavelength grid")
qso_flux=np.zeros((tmp_qso_flux.shape[0],trans_wave.size))
if args.no_simqso:
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=np.interp(trans_wave,tmp_qso_wave[q],tmp_qso_flux[q])
else:
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=np.interp(trans_wave,tmp_qso_wave,tmp_qso_flux[q])
tmp_qso_flux = qso_flux
tmp_qso_wave = trans_wave
# if requested, add BAL features to the quasar continua
if args.balprob:
if args.balprob<=1. and args.balprob >0:
log.info("Adding BALs with probability {}".format(args.balprob))
# save current random state
rnd_state = np.random.get_state()
tmp_qso_flux,meta_bal=bal.insert_bals(tmp_qso_wave,tmp_qso_flux, metadata['Z'],
balprob=args.balprob,seed=seed)
# restore random state to get the same random numbers later
# as when we don't insert BALs
np.random.set_state(rnd_state)
meta_bal['TARGETID'] = metadata['MOCKID']
w = meta_bal['TEMPLATEID']!=-1
meta_bal = meta_bal[:][w]
hdu_bal=pyfits.convenience.table_to_hdu(meta_bal); hdu_bal.name="BAL_META"
del meta_bal
else:
balstr=str(args.balprob)
log.error("BAL probability is not between 0 and 1 : "+balstr)
sys.exit(1)
# Multiply quasar continua by transmitted flux fraction
# (at this point transmission file might include Ly-beta, metals and DLAs)
log.info("Apply transmitted flux fraction")
if not args.no_transmission:
tmp_qso_flux = apply_lya_transmission(tmp_qso_wave,tmp_qso_flux,
trans_wave,transmission)
# if requested, compute metal transmission on the fly
# (if not included already from the transmission file)
if args.metals is not None:
if args.metals_from_file :
log.error('you cannot add metals twice')
raise ValueError('you cannot add metals twice')
if args.no_transmission:
log.error('you cannot add metals if asking for no-transmission')
raise ValueError('can not add metals if using no-transmission')
lstMetals = ''
for m in args.metals: lstMetals += m+', '
log.info("Apply metals: {}".format(lstMetals[:-2]))
tmp_qso_flux = apply_metals_transmission(tmp_qso_wave,tmp_qso_flux,
trans_wave,transmission,args.metals)
# if requested, compute magnitudes and apply target selection. Need to do
# this calculation separately for QSOs in the north vs south.
bbflux=None
if args.target_selection or args.bbflux :
bands=['FLUX_G','FLUX_R','FLUX_Z', 'FLUX_W1', 'FLUX_W2']
bbflux=dict()
bbflux['SOUTH'] = is_south(metadata['DEC'])
for band in bands:
bbflux[band] = np.zeros(nqso)
# need to recompute the magnitudes to account for lya transmission
log.info("Compute QSO magnitudes")
for these, filters in zip( (~bbflux['SOUTH'], bbflux['SOUTH']),
(bassmzls_and_wise_filters, decam_and_wise_filters) ):
if np.count_nonzero(these) > 0:
maggies = filters.get_ab_maggies(1e-17 * tmp_qso_flux[these, :], tmp_qso_wave)
for band, filt in zip( bands, maggies.colnames ):
bbflux[band][these] = np.ma.getdata(1e9 * maggies[filt]) # nanomaggies
if args.target_selection :
log.info("Apply target selection")
isqso = np.ones(nqso, dtype=bool)
for these, issouth in zip( (~bbflux['SOUTH'], bbflux['SOUTH']), (False, True) ):
if np.count_nonzero(these) > 0:
# optical cuts only if using QSO vs SIMQSO
isqso[these] &= isQSO_colors(gflux=bbflux['FLUX_G'][these],
rflux=bbflux['FLUX_R'][these],
zflux=bbflux['FLUX_Z'][these],
w1flux=bbflux['FLUX_W1'][these],
w2flux=bbflux['FLUX_W2'][these],
south=issouth, optical=args.no_simqso)
log.info("Target selection: {}/{} QSOs selected".format(np.sum(isqso),nqso))
selection=np.where(isqso)[0]
if selection.size==0 : return
tmp_qso_flux = tmp_qso_flux[selection]
metadata = metadata[:][selection]
meta = meta[:][selection]
qsometa = qsometa[:][selection]
DZ_FOG = DZ_FOG[selection]
for band in bands :
bbflux[band] = bbflux[band][selection]
bbflux['SOUTH']=bbflux['SOUTH'][selection]
nqso = selection.size
log.info("Resample to a linear wavelength grid (needed by DESI sim.)")
# careful integration of bins, not just a simple interpolation
qso_wave=np.linspace(args.wmin,args.wmax,int((args.wmax-args.wmin)/args.dwave)+1)
qso_flux=np.zeros((tmp_qso_flux.shape[0],qso_wave.size))
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=resample_flux(qso_wave,tmp_qso_wave,tmp_qso_flux[q])
log.info("Simulate DESI observation and write output file")
if "MOCKID" in metadata.dtype.names :
#log.warning("Using MOCKID as TARGETID")
targetid=np.array(metadata["MOCKID"]).astype(int)
elif "ID" in metadata.dtype.names :
log.warning("Using ID as TARGETID")
targetid=np.array(metadata["ID"]).astype(int)
else :
log.warning("No TARGETID")
targetid=None
specmeta={"HPXNSIDE":nside,"HPXPIXEL":pixel, "HPXNEST":hpxnest}
if args.target_selection or args.bbflux :
fibermap_columns = dict(
FLUX_G = bbflux['FLUX_G'],
FLUX_R = bbflux['FLUX_R'],
FLUX_Z = bbflux['FLUX_Z'],
FLUX_W1 = bbflux['FLUX_W1'],
FLUX_W2 = bbflux['FLUX_W2'],
)
photsys = np.full(len(bbflux['FLUX_G']), 'N', dtype='S1')
photsys[bbflux['SOUTH']] = b'S'
fibermap_columns['PHOTSYS'] = photsys
else :
fibermap_columns=None
# Attenuate the spectra for extinction
if not sfdmap is None:
Rv=3.1 #set by default
indx=np.arange(metadata['RA'].size)
extinction =Rv*ext_odonnell(qso_wave)
EBV = sfdmap.ebv(metadata['RA'],metadata['DEC'], scaling=1.0)
qso_flux *=10**( -0.4 * EBV[indx, np.newaxis] * extinction)
if fibermap_columns is not None:
fibermap_columns['EBV']=EBV
EBV0=0.0
EBV_med=np.median(EBV)
Ag = 3.303 * (EBV_med - EBV0)
exptime_fact=np.power(10.0, (2.0 * Ag / 2.5))
obsconditions['EXPTIME']*=exptime_fact
log.info("Dust extinction added")
log.info('exposure time adjusted to {}'.format(obsconditions['EXPTIME']))
sim_spectra(qso_wave,qso_flux, args.program, obsconditions=obsconditions,spectra_filename=ofilename,
sourcetype="qso", skyerr=args.skyerr,ra=metadata["RA"],dec=metadata["DEC"],targetid=targetid,
meta=specmeta,seed=seed,fibermap_columns=fibermap_columns,use_poisson=False) # use Poisson = False to get reproducible results.
### Keep input redshift
Z_spec = metadata['Z'].copy()
Z_input = metadata['Z'].copy()-DZ_FOG
### Add a shift to the redshift, simulating the systematic imprecision of redrock
DZ_sys_shift = args.shift_kms_los/c*(1.+Z_input)
log.info('Added a shift of {} km/s to the redshift'.format(args.shift_kms_los))
meta['REDSHIFT'] += DZ_sys_shift
metadata['Z'] += DZ_sys_shift
### Add a shift to the redshift, simulating the statistic imprecision of redrock
if args.gamma_kms_zfit:
log.info("Added zfit error with gamma {} to zbest".format(args.gamma_kms_zfit))
DZ_stat_shift = mod_cauchy(loc=0,scale=args.gamma_kms_zfit,size=nqso,cut=3000)/c*(1.+Z_input)
meta['REDSHIFT'] += DZ_stat_shift
metadata['Z'] += DZ_stat_shift
## Write the truth file, including metadata for DLAs and BALs
log.info('Writing a truth file {}'.format(truth_filename))
meta.rename_column('REDSHIFT','Z')
meta.add_column(Column(Z_spec,name='TRUEZ'))
meta.add_column(Column(Z_input,name='Z_INPUT'))
meta.add_column(Column(DZ_FOG,name='DZ_FOG'))
meta.add_column(Column(DZ_sys_shift,name='DZ_SYS'))
if args.gamma_kms_zfit:
meta.add_column(Column(DZ_stat_shift,name='DZ_STAT'))
if 'Z_noRSD' in metadata.dtype.names:
meta.add_column(Column(metadata['Z_noRSD'],name='Z_NORSD'))
else:
log.info('Z_noRSD field not present in transmission file. Z_NORSD not saved to truth file')
#Save global seed and pixel seed to primary header
hdr=pyfits.Header()
hdr['GSEED']=global_seed
hdr['PIXSEED']=seed
hdu = pyfits.convenience.table_to_hdu(meta)
hdu.header['EXTNAME'] = 'TRUTH'
hduqso=pyfits.convenience.table_to_hdu(qsometa)
hduqso.header['EXTNAME'] = 'QSO_META'
hdulist=pyfits.HDUList([pyfits.PrimaryHDU(header=hdr),hdu,hduqso])
if args.dla:
hdulist.append(hdu_dla)
if args.balprob:
hdulist.append(hdu_bal)
hdulist.writeto(truth_filename, overwrite=True)
hdulist.close()
if args.zbest :
log.info("Read fibermap")
fibermap = read_fibermap(ofilename)
log.info("Writing a zbest file {}".format(zbest_filename))
columns = [
('CHI2', 'f8'),
('COEFF', 'f8' , (4,)),
('Z', 'f8'),
('ZERR', 'f8'),
('ZWARN', 'i8'),
('SPECTYPE', (str,96)),
('SUBTYPE', (str,16)),
('TARGETID', 'i8'),
('DELTACHI2', 'f8'),
('BRICKNAME', (str,8))]
zbest = Table(np.zeros(nqso, dtype=columns))
zbest['CHI2'][:] = 0.
zbest['Z'][:] = metadata['Z']
zbest['ZERR'][:] = 0.
zbest['ZWARN'][:] = 0
zbest['SPECTYPE'][:] = 'QSO'
zbest['SUBTYPE'][:] = ''
zbest['TARGETID'][:] = metadata['MOCKID']
zbest['DELTACHI2'][:] = 25.
hzbest = pyfits.convenience.table_to_hdu(zbest); hzbest.name='ZBEST'
hfmap = pyfits.convenience.table_to_hdu(fibermap); hfmap.name='FIBERMAP'
hdulist =pyfits.HDUList([pyfits.PrimaryHDU(),hzbest,hfmap])
hdulist.writeto(zbest_filename, overwrite=True)
hdulist.close() # see if this helps with memory issue
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 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 simulate_one_healpix(ifilename,args,model,obsconditions,decam_and_wise_filters,
bassmzls_and_wise_filters,footprint_healpix_weight,
footprint_healpix_nside,
bal=None,sfdmap=None,eboss=None) :
log = get_logger()
# open filename and extract basic HEALPix information
pixel, nside, hpxnest = get_healpix_info(ifilename)
# using global seed (could be None) get seed for this particular pixel
global_seed = args.seed
seed = get_pixel_seed(pixel, nside, global_seed)
# use this seed to generate future random numbers
np.random.seed(seed)
# get output file (we will write there spectra for this HEALPix pixel)
ofilename = get_spectra_filename(args,nside,pixel)
# get directory name (we will also write there zbest file)
pixdir = os.path.dirname(ofilename)
# get filename for truth file
truth_filename = get_truth_filename(args,pixdir,nside,pixel)
# get filename for zbest file
zbest_filename = get_zbest_filename(args,pixdir,nside,pixel)
if not args.overwrite :
# check whether output exists or not
if args.zbest :
if os.path.isfile(ofilename) and os.path.isfile(zbest_filename) :
log.info("skip existing {} and {}".format(ofilename,zbest_filename))
return
else : # only test spectra file
if os.path.isfile(ofilename) :
log.info("skip existing {}".format(ofilename))
return
# create sub-directories if required
if len(pixdir)>0 :
if not os.path.isdir(pixdir) :
log.info("Creating dir {}".format(pixdir))
os.makedirs(pixdir)
log.info("Read skewers in {}, random seed = {}".format(ifilename,seed))
# Read transmission from files. It might include DLA information, and it
# might add metal transmission as well (from the HDU file).
log.info("Read transmission file {}".format(ifilename))
trans_wave, transmission, metadata, dla_info = read_lya_skewers(ifilename,read_dlas=(args.dla=='file'),add_metals=args.metals_from_file)
### Add Finger-of-God, before generate the continua
log.info("Add FOG to redshift with sigma {} to quasar redshift".format(args.sigma_kms_fog))
DZ_FOG = args.sigma_kms_fog/c*(1.+metadata['Z'])*np.random.normal(0,1,metadata['Z'].size)
metadata['Z'] += DZ_FOG
### Select quasar within a given redshift range
w = (metadata['Z']>=args.zmin) & (metadata['Z']<=args.zmax)
transmission = transmission[w]
metadata = metadata[:][w]
DZ_FOG = DZ_FOG[w]
# option to make for BOSS+eBOSS
if not eboss is None:
if args.downsampling or args.desi_footprint:
raise ValueError("eboss option can not be run with "
+"desi_footprint or downsampling")
# Get the redshift distribution from SDSS
selection = sdss_subsample_redshift(metadata["RA"],metadata["DEC"],metadata['Z'],eboss['redshift'])
log.info("Select QSOs in BOSS+eBOSS redshift distribution {} -> {}".format(metadata['Z'].size,selection.sum()))
if selection.sum()==0:
log.warning("No intersection with BOSS+eBOSS redshift distribution")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
# figure out the density of all quasars
N_highz = metadata['Z'].size
# area of healpix pixel, in degrees
area_deg2 = healpy.pixelfunc.nside2pixarea(nside,degrees=True)
input_highz_dens_deg2 = N_highz/area_deg2
selection = sdss_subsample(metadata["RA"], metadata["DEC"],
input_highz_dens_deg2,eboss['footprint'])
log.info("Select QSOs in BOSS+eBOSS footprint {} -> {}".format(transmission.shape[0],selection.size))
if selection.size == 0 :
log.warning("No intersection with BOSS+eBOSS footprint")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
if args.desi_footprint :
footprint_healpix = footprint.radec2pix(footprint_healpix_nside, metadata["RA"], metadata["DEC"])
selection = np.where(footprint_healpix_weight[footprint_healpix]>0.99)[0]
log.info("Select QSOs in DESI footprint {} -> {}".format(transmission.shape[0],selection.size))
if selection.size == 0 :
log.warning("No intersection with DESI footprint")
return
transmission = transmission[selection]
metadata = metadata[:][selection]
DZ_FOG = DZ_FOG[selection]
nqso=transmission.shape[0]
if args.downsampling is not None :
if args.downsampling <= 0 or args.downsampling > 1 :
log.error("Down sampling fraction={} must be between 0 and 1".format(args.downsampling))
raise ValueError("Down sampling fraction={} must be between 0 and 1".format(args.downsampling))
indices = np.where(np.random.uniform(size=nqso)<args.downsampling)[0]
if indices.size == 0 :
log.warning("Down sampling from {} to 0 (by chance I presume)".format(nqso))
return
transmission = transmission[indices]
metadata = metadata[:][indices]
DZ_FOG = DZ_FOG[indices]
nqso = transmission.shape[0]
if args.nmax is not None :
if args.nmax < nqso :
log.info("Limit number of QSOs from {} to nmax={} (random subsample)".format(nqso,args.nmax))
# take a random subsample
indices = (np.random.uniform(size=args.nmax)*nqso).astype(int)
transmission = transmission[indices]
metadata = metadata[:][indices]
DZ_FOG = DZ_FOG[indices]
nqso = args.nmax
# In previous versions of the London mocks we needed to enforce F=1 for
# z > z_qso here, but this is not needed anymore. Moreover, now we also
# have metal absorption that implies F < 1 for z > z_qso
#for ii in range(len(metadata)):
# transmission[ii][trans_wave>lambda_RF_LYA*(metadata[ii]['Z']+1)]=1.0
# if requested, add DLA to the transmission skewers
if args.dla is not None :
# if adding random DLAs, we will need a new random generator
if args.dla=='random':
log.info('Adding DLAs randomly')
random_state_just_for_dlas = np.random.RandomState(seed)
elif args.dla=='file':
log.info('Adding DLAs from transmission file')
else:
log.error("Wrong option for args.dla: "+args.dla)
sys.exit(1)
# if adding DLAs, the information will be printed here
dla_filename=os.path.join(pixdir,"dla-{}-{}.fits".format(nside,pixel))
dla_NHI, dla_z, dla_qid,dla_id = [], [], [],[]
# identify minimum Lya redshift in transmission files
min_lya_z = np.min(trans_wave/lambda_RF_LYA - 1)
# loop over quasars in pixel
for ii in range(len(metadata)):
# quasars with z < min_z will not have any DLA in spectrum
if min_lya_z>metadata['Z'][ii]: continue
# quasar ID
idd=metadata['MOCKID'][ii]
dlas=[]
if args.dla=='file':
for dla in dla_info[dla_info['MOCKID']==idd]:
# Adding only DLAs with z < zqso
if dla['Z_DLA_RSD']>=metadata['Z'][ii]: continue
dlas.append(dict(z=dla['Z_DLA_RSD'],N=dla['N_HI_DLA'],dlaid=dla['DLAID']))
transmission_dla = dla_spec(trans_wave,dlas)
elif args.dla=='random':
dlas, transmission_dla = insert_dlas(trans_wave, metadata['Z'][ii], rstate=random_state_just_for_dlas)
for idla in dlas:
idla['dlaid']+=idd*1000 #Added to have unique DLA ids. Same format as DLAs from file.
# multiply transmissions and store information for the DLA file
if len(dlas)>0:
transmission[ii] = transmission_dla * transmission[ii]
dla_z += [idla['z'] for idla in dlas]
dla_NHI += [idla['N'] for idla in dlas]
dla_id += [idla['dlaid'] for idla in dlas]
dla_qid += [idd]*len(dlas)
log.info('Added {} DLAs'.format(len(dla_id)))
# write file with DLA information
if len(dla_id)>0:
dla_meta=Table()
dla_meta['NHI'] = dla_NHI
dla_meta['Z_DLA'] = dla_z #This is Z_DLA_RSD in transmision.
dla_meta['TARGETID']=dla_qid
dla_meta['DLAID'] = dla_id
hdu_dla = pyfits.convenience.table_to_hdu(dla_meta)
hdu_dla.name="DLA_META"
del(dla_meta)
log.info("DLA metadata to be saved in {}".format(truth_filename))
else:
hdu_dla=pyfits.PrimaryHDU()
hdu_dla.name="DLA_META"
# if requested, extend transmission skewers to cover full spectrum
if args.target_selection or args.bbflux :
wanted_min_wave = 3329. # needed to compute magnitudes for decam2014-r (one could have trimmed the transmission file ...)
wanted_max_wave = 55501. # needed to compute magnitudes for wise2010-W2
if trans_wave[0]>wanted_min_wave :
log.info("Increase wavelength range from {}:{} to {}:{} to compute magnitudes".format(int(trans_wave[0]),int(trans_wave[-1]),int(wanted_min_wave),int(trans_wave[-1])))
# pad with ones at short wavelength, we assume F = 1 for z <~ 1.7
# we don't need any wavelength resolution here
new_trans_wave = np.append([wanted_min_wave,trans_wave[0]-0.01],trans_wave)
new_transmission = np.ones((transmission.shape[0],new_trans_wave.size))
new_transmission[:,2:] = transmission
trans_wave = new_trans_wave
transmission = new_transmission
if trans_wave[-1]<wanted_max_wave :
log.info("Increase wavelength range from {}:{} to {}:{} to compute magnitudes".format(int(trans_wave[0]),int(trans_wave[-1]),int(trans_wave[0]),int(wanted_max_wave)))
# pad with ones at long wavelength because we assume F = 1
coarse_dwave = 2. # we don't care about resolution, we just need a decent QSO spectrum, there is no IGM transmission in this range
n = int((wanted_max_wave-trans_wave[-1])/coarse_dwave)+1
new_trans_wave = np.append(trans_wave,np.linspace(trans_wave[-1]+coarse_dwave,trans_wave[-1]+coarse_dwave*(n+1),n))
new_transmission = np.ones((transmission.shape[0],new_trans_wave.size))
new_transmission[:,:trans_wave.size] = transmission
trans_wave = new_trans_wave
transmission = new_transmission
# whether to use QSO or SIMQSO to generate quasar continua. Simulate
# spectra in the north vs south separately because they're on different
# photometric systems.
south = np.where( is_south(metadata['DEC']) )[0]
north = np.where( ~is_south(metadata['DEC']) )[0]
meta, qsometa = empty_metatable(nqso, objtype='QSO', simqso=not args.no_simqso)
if args.no_simqso:
log.info("Simulate {} QSOs with QSO templates".format(nqso))
tmp_qso_flux = np.zeros([nqso, len(model.eigenwave)], dtype='f4')
tmp_qso_wave = np.zeros_like(tmp_qso_flux)
else:
log.info("Simulate {} QSOs with SIMQSO templates".format(nqso))
tmp_qso_flux = np.zeros([nqso, len(model.basewave)], dtype='f4')
tmp_qso_wave = model.basewave
for these, issouth in zip( (north, south), (False, True) ):
# number of quasars in these
nt = len(these)
if nt<=0: continue
if not eboss is None:
# for eBOSS, generate only quasars with r<22
magrange = (17.0, 21.3)
_tmp_qso_flux, _tmp_qso_wave, _meta, _qsometa \
= model.make_templates(nmodel=nt,
redshift=metadata['Z'][these], magrange=magrange,
lyaforest=False, nocolorcuts=True,
noresample=True, seed=seed, south=issouth)
else:
_tmp_qso_flux, _tmp_qso_wave, _meta, _qsometa \
= model.make_templates(nmodel=nt,
redshift=metadata['Z'][these],
lyaforest=False, nocolorcuts=True,
noresample=True, seed=seed, south=issouth)
_meta['TARGETID'] = metadata['MOCKID'][these]
_qsometa['TARGETID'] = metadata['MOCKID'][these]
meta[these] = _meta
qsometa[these] = _qsometa
tmp_qso_flux[these, :] = _tmp_qso_flux
if args.no_simqso:
tmp_qso_wave[these, :] = _tmp_qso_wave
log.info("Resample to transmission wavelength grid")
qso_flux=np.zeros((tmp_qso_flux.shape[0],trans_wave.size))
if args.no_simqso:
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=np.interp(trans_wave,tmp_qso_wave[q],tmp_qso_flux[q])
else:
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=np.interp(trans_wave,tmp_qso_wave,tmp_qso_flux[q])
tmp_qso_flux = qso_flux
tmp_qso_wave = trans_wave
# if requested, add BAL features to the quasar continua
if args.balprob:
if args.balprob<=1. and args.balprob >0:
log.info("Adding BALs with probability {}".format(args.balprob))
# save current random state
rnd_state = np.random.get_state()
tmp_qso_flux,meta_bal=bal.insert_bals(tmp_qso_wave,tmp_qso_flux, metadata['Z'],
balprob=args.balprob,seed=seed)
# restore random state to get the same random numbers later
# as when we don't insert BALs
np.random.set_state(rnd_state)
meta_bal['TARGETID'] = metadata['MOCKID']
w = meta_bal['TEMPLATEID']!=-1
meta_bal = meta_bal[:][w]
hdu_bal=pyfits.convenience.table_to_hdu(meta_bal); hdu_bal.name="BAL_META"
del meta_bal
else:
balstr=str(args.balprob)
log.error("BAL probability is not between 0 and 1 : "+balstr)
sys.exit(1)
# Multiply quasar continua by transmitted flux fraction
# (at this point transmission file might include Ly-beta, metals and DLAs)
log.info("Apply transmitted flux fraction")
if not args.no_transmission:
tmp_qso_flux = apply_lya_transmission(tmp_qso_wave,tmp_qso_flux,
trans_wave,transmission)
# if requested, compute metal transmission on the fly
# (if not included already from the transmission file)
if args.metals is not None:
if args.metals_from_file:
log.error('you cannot add metals twice')
raise ValueError('you cannot add metals twice')
if args.no_transmission:
log.error('you cannot add metals if asking for no-transmission')
raise ValueError('can not add metals if using no-transmission')
lstMetals = ''
for m in args.metals: lstMetals += m+', '
log.info("Apply metals: {}".format(lstMetals[:-2]))
tmp_qso_flux = apply_metals_transmission(tmp_qso_wave,tmp_qso_flux,
trans_wave,transmission,args.metals)
# if requested, compute magnitudes and apply target selection. Need to do
# this calculation separately for QSOs in the north vs south.
bbflux=None
if args.target_selection or args.bbflux :
bands=['FLUX_G','FLUX_R','FLUX_Z', 'FLUX_W1', 'FLUX_W2']
bbflux=dict()
bbflux['SOUTH'] = is_south(metadata['DEC'])
for band in bands:
bbflux[band] = np.zeros(nqso)
# need to recompute the magnitudes to account for lya transmission
log.info("Compute QSO magnitudes")
for these, filters in zip( (~bbflux['SOUTH'], bbflux['SOUTH']),
(bassmzls_and_wise_filters, decam_and_wise_filters) ):
if np.count_nonzero(these) > 0:
maggies = filters.get_ab_maggies(1e-17 * tmp_qso_flux[these, :], tmp_qso_wave)
for band, filt in zip( bands, maggies.colnames ):
bbflux[band][these] = np.ma.getdata(1e9 * maggies[filt]) # nanomaggies
if args.target_selection :
log.info("Apply target selection")
isqso = np.ones(nqso, dtype=bool)
for these, issouth in zip( (~bbflux['SOUTH'], bbflux['SOUTH']), (False, True) ):
if np.count_nonzero(these) > 0:
# optical cuts only if using QSO vs SIMQSO
isqso[these] &= isQSO_colors(gflux=bbflux['FLUX_G'][these],
rflux=bbflux['FLUX_R'][these],
zflux=bbflux['FLUX_Z'][these],
w1flux=bbflux['FLUX_W1'][these],
w2flux=bbflux['FLUX_W2'][these],
south=issouth, optical=args.no_simqso)
log.info("Target selection: {}/{} QSOs selected".format(np.sum(isqso),nqso))
selection=np.where(isqso)[0]
if selection.size==0 : return
tmp_qso_flux = tmp_qso_flux[selection]
metadata = metadata[:][selection]
meta = meta[:][selection]
qsometa = qsometa[:][selection]
DZ_FOG = DZ_FOG[selection]
for band in bands :
bbflux[band] = bbflux[band][selection]
nqso = selection.size
log.info("Resample to a linear wavelength grid (needed by DESI sim.)")
# careful integration of bins, not just a simple interpolation
qso_wave=np.linspace(args.wmin,args.wmax,int((args.wmax-args.wmin)/args.dwave)+1)
qso_flux=np.zeros((tmp_qso_flux.shape[0],qso_wave.size))
for q in range(tmp_qso_flux.shape[0]) :
qso_flux[q]=resample_flux(qso_wave,tmp_qso_wave,tmp_qso_flux[q])
log.info("Simulate DESI observation and write output file")
if "MOCKID" in metadata.dtype.names :
#log.warning("Using MOCKID as TARGETID")
targetid=np.array(metadata["MOCKID"]).astype(int)
elif "ID" in metadata.dtype.names :
log.warning("Using ID as TARGETID")
targetid=np.array(metadata["ID"]).astype(int)
else :
log.warning("No TARGETID")
targetid=None
specmeta={"HPXNSIDE":nside,"HPXPIXEL":pixel, "HPXNEST":hpxnest}
if args.target_selection or args.bbflux :
fibermap_columns = dict(
FLUX_G = bbflux['FLUX_G'],
FLUX_R = bbflux['FLUX_R'],
FLUX_Z = bbflux['FLUX_Z'],
FLUX_W1 = bbflux['FLUX_W1'],
FLUX_W2 = bbflux['FLUX_W2'],
)
photsys = np.full(len(bbflux['FLUX_G']), 'N', dtype='S1')
photsys[bbflux['SOUTH']] = b'S'
fibermap_columns['PHOTSYS'] = photsys
else :
fibermap_columns=None
# Attenuate the spectra for extinction
if not sfdmap is None:
Rv=3.1 #set by default
indx=np.arange(metadata['RA'].size)
extinction =Rv*ext_odonnell(qso_wave)
EBV = sfdmap.ebv(metadata['RA'],metadata['DEC'], scaling=1.0)
qso_flux *=10**( -0.4 * EBV[indx, np.newaxis] * extinction)
if fibermap_columns is not None:
fibermap_columns['EBV']=EBV
EBV0=0.0
EBV_med=np.median(EBV)
Ag = 3.303 * (EBV_med - EBV0)
exptime_fact=np.power(10.0, (2.0 * Ag / 2.5))
obsconditions['EXPTIME']*=exptime_fact
log.info("Dust extinction added")
log.info('exposure time adjusted to {}'.format(obsconditions['EXPTIME']))
sim_spectra(qso_wave,qso_flux, args.program, obsconditions=obsconditions,spectra_filename=ofilename,
sourcetype="qso", skyerr=args.skyerr,ra=metadata["RA"],dec=metadata["DEC"],targetid=targetid,
meta=specmeta,seed=seed,fibermap_columns=fibermap_columns,use_poisson=False) # use Poisson = False to get reproducible results.
### Keep input redshift
Z_spec = metadata['Z'].copy()
Z_input = metadata['Z'].copy()-DZ_FOG
### Add a shift to the redshift, simulating the systematic imprecision of redrock
DZ_sys_shift = args.shift_kms_los/c*(1.+Z_input)
log.info('Added a shift of {} km/s to the redshift'.format(args.shift_kms_los))
meta['REDSHIFT'] += DZ_sys_shift
metadata['Z'] += DZ_sys_shift
### Add a shift to the redshift, simulating the statistic imprecision of redrock
if args.gamma_kms_zfit:
log.info("Added zfit error with gamma {} to zbest".format(args.gamma_kms_zfit))
DZ_stat_shift = mod_cauchy(loc=0,scale=args.gamma_kms_zfit,size=nqso,cut=3000)/c*(1.+Z_input)
meta['REDSHIFT'] += DZ_stat_shift
metadata['Z'] += DZ_stat_shift
## Write the truth file, including metadata for DLAs and BALs
log.info('Writing a truth file {}'.format(truth_filename))
meta.rename_column('REDSHIFT','Z')
meta.add_column(Column(Z_spec,name='TRUEZ'))
meta.add_column(Column(Z_input,name='Z_INPUT'))
meta.add_column(Column(DZ_FOG,name='DZ_FOG'))
meta.add_column(Column(DZ_sys_shift,name='DZ_SYS'))
if args.gamma_kms_zfit:
meta.add_column(Column(DZ_stat_shift,name='DZ_STAT'))
if 'Z_noRSD' in metadata.dtype.names:
meta.add_column(Column(metadata['Z_noRSD'],name='Z_NORSD'))
else:
log.info('Z_noRSD field not present in transmission file. Z_NORSD not saved to truth file')
hdu = pyfits.convenience.table_to_hdu(meta)
hdu.header['EXTNAME'] = 'TRUTH'
hduqso=pyfits.convenience.table_to_hdu(qsometa)
hduqso.header['EXTNAME'] = 'QSO_META'
hdulist=pyfits.HDUList([pyfits.PrimaryHDU(),hdu,hduqso])
if args.dla:
hdulist.append(hdu_dla)
if args.balprob:
hdulist.append(hdu_bal)
hdulist.writeto(truth_filename, overwrite=True)
hdulist.close()
if args.zbest :
log.info("Read fibermap")
fibermap = read_fibermap(ofilename)
log.info("Writing a zbest file {}".format(zbest_filename))
columns = [
('CHI2', 'f8'),
('COEFF', 'f8' , (4,)),
('Z', 'f8'),
('ZERR', 'f8'),
('ZWARN', 'i8'),
('SPECTYPE', (str,96)),
('SUBTYPE', (str,16)),
('TARGETID', 'i8'),
('DELTACHI2', 'f8'),
('BRICKNAME', (str,8))]
zbest = Table(np.zeros(nqso, dtype=columns))
zbest['CHI2'][:] = 0.
zbest['Z'][:] = metadata['Z']
zbest['ZERR'][:] = 0.
zbest['ZWARN'][:] = 0
zbest['SPECTYPE'][:] = 'QSO'
zbest['SUBTYPE'][:] = ''
zbest['TARGETID'][:] = metadata['MOCKID']
zbest['DELTACHI2'][:] = 25.
hzbest = pyfits.convenience.table_to_hdu(zbest); hzbest.name='ZBEST'
hfmap = pyfits.convenience.table_to_hdu(fibermap); hfmap.name='FIBERMAP'
hdulist =pyfits.HDUList([pyfits.PrimaryHDU(),hzbest,hfmap])
hdulist.writeto(zbest_filename, overwrite=True)
hdulist.close() # see if this helps with memory issue
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_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 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 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)
