def qso_select(self, targets, truth, boss_std=None): """Select QSO targets and contaminants.""" from desitarget.cuts import isQSO_colors, isELG gflux, rflux, zflux, w1flux, w2flux = targets['FLUX_G'], targets['FLUX_R'], \ targets['FLUX_Z'], targets['FLUX_W1'], targets['FLUX_W2'] qso = isQSO_colors(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, optical=True) targets['DESI_TARGET'] |= (qso != 0) * self.desi_mask.QSO targets['DESI_TARGET'] |= (qso != 0) * self.desi_mask.QSO_SOUTH targets['OBSCONDITIONS'] |= (qso != 0) * self.obsconditions.mask( self.desi_mask.QSO.obsconditions) targets['OBSCONDITIONS'] |= (qso != 0) * self.obsconditions.mask( self.desi_mask.QSO_SOUTH.obsconditions) # Select QSO contaminants for ELG targets. There'd be no morphology cut # and we're missing the WISE colors, so the density won't be right. elg = isELG(gflux=gflux, rflux=rflux, zflux=zflux) targets['DESI_TARGET'] |= (elg != 0) * self.desi_mask.ELG targets['DESI_TARGET'] |= (elg != 0) * self.desi_mask.ELG_SOUTH targets['OBSCONDITIONS'] |= (elg != 0) * self.obsconditions.mask( self.desi_mask.ELG.obsconditions) targets['OBSCONDITIONS'] |= (elg != 0) * self.obsconditions.mask( self.desi_mask.ELG_SOUTH.obsconditions) truth['CONTAM_TARGET'] |= (elg != 0) * self.contam_mask.ELG_IS_QSO truth['CONTAM_TARGET'] |= (elg != 0) * self.contam_mask.ELG_CONTAM
def lrg_select(self, targets, truth, boss_std=None): """Select LRG targets.""" from desitarget.cuts import isLRG_colors, isQSO_colors gflux, rflux, zflux, w1flux, w2flux = targets['FLUX_G'], targets['FLUX_R'], \ targets['FLUX_Z'], targets['FLUX_W1'], targets['FLUX_W2'] lrg = isLRG_colors(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux) targets['DESI_TARGET'] |= (lrg != 0) * self.desi_mask.LRG targets['DESI_TARGET'] |= (lrg != 0) * self.desi_mask.LRG_SOUTH targets['OBSCONDITIONS'] |= (lrg != 0) * self.obsconditions.mask( self.desi_mask.LRG.obsconditions) targets['OBSCONDITIONS'] |= (lrg != 0) * self.obsconditions.mask( self.desi_mask.LRG_SOUTH.obsconditions) # Select LRG contaminants for QSO targets. There should be a morphology # cut here, too, so we're going to overestimate the number of # contaminants. To make sure we don't reduce the number density of true # LRGs, demand that the QSO contaminants are not also selected as LRGs. qso = isQSO_colors(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, optical=False) # Note optical=False! targets['DESI_TARGET'] |= (qso != 0) * (lrg == 0) * self.desi_mask.QSO targets['DESI_TARGET'] |= (qso != 0) * (lrg == 0) * self.desi_mask.QSO_SOUTH targets['OBSCONDITIONS'] |= (qso != 0) * (lrg == 0) * self.obsconditions.mask( self.desi_mask.QSO.obsconditions) targets['OBSCONDITIONS'] |= (qso != 0) * ( lrg == 0) * self.obsconditions.mask( self.desi_mask.QSO_SOUTH.obsconditions) truth['CONTAM_TARGET'] |= (qso != 0) * ( lrg == 0) * self.contam_mask.QSO_IS_LRG truth['CONTAM_TARGET'] |= (qso != 0) * ( lrg == 0) * self.contam_mask.QSO_IS_GALAXY truth['CONTAM_TARGET'] |= (qso != 0) * ( lrg == 0) * self.contam_mask.QSO_CONTAM
def simulate_one_healpix(ifilename, args, model, obsconditions, decam_and_wise_filters, footprint_healpix_weight, footprint_healpix_nside, seed, bal=None): log = get_logger() # set seed now # we need a seed per healpix because # the spectra simulator REQUIRES a seed np.random.seed(seed) # read the header of the tranmission file to find the healpix pixel number, nside # and if we are lucky the scheme. # if this fails, try to guess it from the filename (for backward compatibility) healpix = -1 nside = -1 hpxnest = True hdulist = pyfits.open(ifilename) if "METADATA" in hdulist: head = hdulist["METADATA"].header for k in ["HPXPIXEL", "PIXNUM"]: if k in head: healpix = int(head[k]) log.info("healpix={}={}".format(k, healpix)) break for k in ["HPXNSIDE", "NSIDE"]: if k in head: nside = int(head[k]) log.info("nside={}={}".format(k, nside)) break for k in ["HPXNEST", "NESTED", "SCHEME"]: if k in head: if k == "SCHEME": hpxnest = (head[k] == "NEST") else: hpxnest = bool(head[k]) log.info("hpxnest from {} = {}".format(k, hpxnest)) break if healpix >= 0 and nside < 0: log.error("Read healpix in header but not nside.") raise ValueError("Read healpix in header but not nside.") if healpix < 0: vals = os.path.basename(ifilename).split(".")[0].split("-") if len(vals) < 3: log.error("Cannot guess nside and healpix from filename {}".format( ifilename)) raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) try: healpix = int(vals[-1]) nside = int(vals[-2]) except ValueError: raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) log.warning( "Guessed healpix and nside from filename, assuming the healpix scheme is 'NESTED'" ) zbest_filename = None if args.outfile: ofilename = args.outfile else: ofilename = os.path.join( args.outdir, "{}/{}/spectra-{}-{}.fits".format(healpix // 100, healpix, nside, healpix)) pixdir = os.path.dirname(ofilename) if args.zbest: zbest_filename = os.path.join( pixdir, "zbest-{}-{}.fits".format(nside, healpix)) 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 log.info("Read skewers in {}, random seed = {}".format(ifilename, seed)) ##ALMA: It reads only the skewers only if there are no DLAs or if they are added randomly. if (not args.dla or args.dla == 'random'): trans_wave, transmission, metadata = read_lya_skewers(ifilename) ok = np.where((metadata['Z'] >= args.zmin) & (metadata['Z'] <= args.zmax))[0] transmission = transmission[ok] metadata = metadata[:][ok] ##ALMA:Added to read dla_info elif (args.dla == 'file'): log.info("Read DLA information in {}".format(ifilename)) trans_wave, transmission, metadata, dla_info = read_lya_skewers( ifilename, dla_='TRUE') ok = np.where((metadata['Z'] >= args.zmin) & (metadata['Z'] <= args.zmax))[0] transmission = transmission[ok] metadata = metadata[:][ok] else: log.error( 'Not a valid option to add DLAs. Valid options are "random" or "file"' ) sys.exit(1) if args.dla: dla_NHI, dla_z, dla_id = [], [], [] dla_filename = os.path.join(pixdir, "dla-{}-{}.fits".format(nside, healpix)) 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] 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] nqso = transmission.shape[0] ##ALMA:added to set transmission to 1 for z>zqso, this can be removed when transmission is corrected. for ii in range(len(metadata)): transmission[ii][trans_wave > 1215.67 * (metadata[ii]['Z'] + 1)] = 1.0 if (args.dla == 'file'): log.info('Adding DLAs from transmision file') min_trans_wave = np.min(trans_wave / 1215.67 - 1) for ii in range(len(metadata)): if min_trans_wave < metadata[ii]['Z']: idd = metadata['MOCKID'][ii] dlas = dla_info[dla_info['MOCKID'] == idd] dlass = [] for i in range(len(dlas)): ##Adding only dlas between zqso and 1.95, check again for the next version of London mocks... if (dlas[i]['Z_DLA'] < metadata[ii]['Z']) and (dlas[i]['Z_DLA'] > 1.95): dlass.append( dict(z=dlas[i]['Z_DLA'] + dlas[i]['DZ_DLA'], N=dlas[i]['N_HI_DLA'])) if len(dlass) > 0: dla_model = dla_spec(trans_wave, dlass) transmission[ii] = dla_model * transmission[ii] dla_z += [idla['z'] for idla in dlass] dla_NHI += [idla['N'] for idla in dlass] dla_id += [idd] * len(dlass) elif (args.dla == 'random'): log.info('Adding DLAs randomly') min_trans_wave = np.min(trans_wave / 1215.67 - 1) for ii in range(len(metadata)): if min_trans_wave < metadata[ii]['Z']: idd = metadata['MOCKID'][ii] dlass, dla_model = insert_dlas(trans_wave, metadata[ii]['Z']) if len(dlass) > 0: transmission[ii] = dla_model * transmission[ii] dla_z += [idla['z'] for idla in dlass] dla_NHI += [idla['N'] for idla in dlass] dla_id += [idd] * len(dlass) if args.dla: if len(dla_id) > 0: dla_meta = Table() dla_meta['NHI'] = dla_NHI dla_meta['z'] = dla_z dla_meta['ID'] = dla_id 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] nqso = args.nmax if args.dla: dla_meta = dla_meta[:][dla_meta['ID'] == metadata['MOCKID']] if args.target_selection or args.mags: 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 zeros at short wavelength because we assume transmission = 0 # and 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.zeros( (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 transmission = 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 log.info("Simulate {} QSOs".format(nqso)) tmp_qso_flux, tmp_qso_wave, meta = model.make_templates( nmodel=nqso, redshift=metadata['Z'], lyaforest=False, nocolorcuts=True, noresample=True, seed=seed) log.info("Resample to transmission wavelength grid") # because we don't want to alter the transmission field with resampling here qso_flux = np.zeros((tmp_qso_flux.shape[0], trans_wave.size)) 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 ##To add BALs to be checked by Luz and Jaime if (args.balprob): if (args.balprob <= 1. and args.balprob > 0): log.info("Adding BALs with probability {}".format(args.balprob)) tmp_qso_flux, meta_bal = bal.insert_bals(tmp_qso_wave, tmp_qso_flux, metadata['Z'], balprob=args.balprob, seed=seed) else: log.error("Probability to add BALs is not between 0 and 1") sys.exit(1) log.info("Apply lya") tmp_qso_flux = apply_lya_transmission(tmp_qso_wave, tmp_qso_flux, trans_wave, transmission) if args.metals is not None: 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) bbflux = None if args.target_selection or args.mags: bands = ['FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'] bbflux = dict() # need to recompute the magnitudes to account for lya transmission log.info("Compute QSO magnitudes") maggies = decam_and_wise_filters.get_ab_maggies( 1e-17 * tmp_qso_flux, tmp_qso_wave) for band, filt in zip(bands, [ 'decam2014-g', 'decam2014-r', 'decam2014-z', 'wise2010-W1', 'wise2010-W2' ]): bbflux[band] = np.ma.getdata(1e9 * maggies[filt]) # nanomaggies if args.target_selection: log.info("Apply target selection") isqso = isQSO_colors(gflux=bbflux['FLUX_G'], rflux=bbflux['FLUX_R'], zflux=bbflux['FLUX_Z'], w1flux=bbflux['FLUX_W1'], w2flux=bbflux['FLUX_W2']) 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] for band in bands: bbflux[band] = bbflux[band][selection] nqso = selection.size log.info("Resample to a linear wavelength grid (needed by DESI sim.)") # we need a linear grid. for this resampling we take care of integrating in bins # we do not do 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") pixdir = os.path.dirname(ofilename) if len(pixdir) > 0: if not os.path.isdir(pixdir): log.info("Creating dir {}".format(pixdir)) os.makedirs(pixdir) 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 meta = {"HPXNSIDE": nside, "HPXPIXEL": healpix, "HPXNEST": hpxnest} if args.target_selection or args.mags: # today we write mags because that's what is in the fibermap mags = np.zeros((qso_flux.shape[0], 5)) for i, band in enumerate(bands): jj = (bbflux[band] > 0) mags[jj, i] = 22.5 - 2.5 * np.log10(bbflux[band][jj]) # AB magnitudes fibermap_columns = {"MAG": mags} else: fibermap_columns = None 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=meta, seed=seed, fibermap_columns=fibermap_columns) 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"] = fibermap["TARGETID"] 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, clobber=True) hdulist.close() # see if this helps with memory issue if args.dla: #This will change according to discussion log.info("Updating the spectra file to add DLA metadata {}".format( ofilename)) hdudla = pyfits.table_to_hdu(dla_meta) hdudla.name = "DLA_META" hdul = pyfits.open(ofilename, mode='update') hdul.append(hdudla) hdul.flush() hdul.close()
def test_single_cuts(self): """Test cuts of individual target classes """ targets = Table.read(self.sweepfiles[0]) flux = cuts.unextinct_fluxes(targets) gflux = flux['GFLUX'] rflux = flux['RFLUX'] zflux = flux['ZFLUX'] w1flux = flux['W1FLUX'] w2flux = flux['W2FLUX'] zfiberflux = flux['ZFIBERFLUX'] gfluxivar = targets['FLUX_IVAR_G'] rfluxivar = targets['FLUX_IVAR_R'] zfluxivar = targets['FLUX_IVAR_Z'] gsnr = targets['FLUX_G'] * np.sqrt(targets['FLUX_IVAR_G']) rsnr = targets['FLUX_R'] * np.sqrt(targets['FLUX_IVAR_R']) zsnr = targets['FLUX_Z'] * np.sqrt(targets['FLUX_IVAR_Z']) w1snr = targets['FLUX_W1'] * np.sqrt(targets['FLUX_IVAR_W1']) w2snr = targets['FLUX_W2'] * np.sqrt(targets['FLUX_IVAR_W2']) dchisq = targets['DCHISQ'] deltaChi2 = dchisq[..., 0] - dchisq[..., 1] gnobs, rnobs, znobs = targets['NOBS_G'], targets['NOBS_R'], targets[ 'NOBS_Z'] gallmask = targets['ALLMASK_G'] rallmask = targets['ALLMASK_R'] zallmask = targets['ALLMASK_Z'] gfracflux = targets['FRACFLUX_G'] rfracflux = targets['FRACFLUX_R'] zfracflux = targets['FRACFLUX_Z'] gfracmasked = targets['FRACMASKED_G'] rfracmasked = targets['FRACMASKED_R'] zfracmasked = targets['FRACMASKED_Z'] gfracin = targets['FRACIN_G'] rfracin = targets['FRACIN_R'] zfracin = targets['FRACIN_Z'] maskbits = targets['MASKBITS'] gaiagmag = targets['GAIA_PHOT_G_MEAN_MAG'] Grr = gaiagmag - 22.5 + 2.5 * np.log10(targets['FLUX_R']) if 'BRICK_PRIMARY' in targets.colnames: primary = targets['BRICK_PRIMARY'] else: primary = np.ones_like(gflux, dtype='?') lrg1 = cuts.isLRG(primary=primary, gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, zfiberflux=zfiberflux, rflux_snr=rsnr, zflux_snr=zsnr, w1flux_snr=w1snr) lrg2 = cuts.isLRG(primary=None, gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, zfiberflux=zfiberflux, rflux_snr=rsnr, zflux_snr=zsnr, w1flux_snr=w1snr) self.assertTrue(np.all(lrg1 == lrg2)) # ADM also check that the color selections alone work. This tripped us up once # ADM with the mocks part of the code calling a non-existent LRG colors function. lrg1 = cuts.isLRG_colors(primary=primary, gflux=gflux, rflux=rflux, zflux=zflux, zfiberflux=zfiberflux, w1flux=w1flux, w2flux=w2flux) lrg2 = cuts.isLRG_colors(primary=None, gflux=gflux, rflux=rflux, zflux=zflux, zfiberflux=zfiberflux, w1flux=w1flux, w2flux=w2flux) self.assertTrue(np.all(lrg1 == lrg2)) elg1 = cuts.isELG(gflux=gflux, rflux=rflux, zflux=zflux, gsnr=gsnr, rsnr=rsnr, zsnr=zsnr, gnobs=gnobs, rnobs=rnobs, znobs=znobs, maskbits=maskbits, primary=primary) elg2 = cuts.isELG(gflux=gflux, rflux=rflux, zflux=zflux, gsnr=gsnr, rsnr=rsnr, zsnr=zsnr, gnobs=gnobs, rnobs=rnobs, znobs=znobs, maskbits=maskbits, primary=None) self.assertTrue(np.all(elg1 == elg2)) elg1 = cuts.isELG_colors(gflux=gflux, rflux=rflux, zflux=zflux, primary=primary) elg2 = cuts.isELG_colors(gflux=gflux, rflux=rflux, zflux=zflux, primary=None) self.assertTrue(np.all(elg1 == elg2)) for targtype in ["bright", "faint", "wise"]: bgs = [] for primary in [primary, None]: bgs.append( cuts.isBGS(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, gnobs=gnobs, rnobs=rnobs, znobs=znobs, gfracmasked=gfracmasked, rfracmasked=rfracmasked, zfracmasked=zfracmasked, gfracflux=gfracflux, rfracflux=rfracflux, zfracflux=zfracflux, gfracin=gfracin, rfracin=rfracin, zfracin=zfracin, gfluxivar=gfluxivar, rfluxivar=rfluxivar, zfluxivar=zfluxivar, maskbits=maskbits, Grr=Grr, w1snr=w1snr, gaiagmag=gaiagmag, primary=primary, targtype=targtype)) self.assertTrue(np.all(bgs[0] == bgs[1])) # ADM need to include RELEASE for quasar cuts, at least. release = targets['RELEASE'] # - Test that objtype and primary are optional psftype = targets['TYPE'] qso1 = cuts.isQSO_cuts(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, deltaChi2=deltaChi2, maskbits=maskbits, w1snr=w1snr, w2snr=w2snr, objtype=psftype, primary=primary, release=release) qso2 = cuts.isQSO_cuts(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, deltaChi2=deltaChi2, maskbits=maskbits, w1snr=w1snr, w2snr=w2snr, objtype=None, primary=None, release=release) self.assertTrue(np.all(qso1 == qso2)) # ADM also check that the color selections alone work. This tripped us up once # ADM with the mocks part of the code calling a non-existent LRG colors function. qso1 = cuts.isQSO_colors(gflux, rflux, zflux, w1flux, w2flux, optical=False) qso2 = cuts.isQSO_colors(gflux, rflux, zflux, w1flux, w2flux, optical=None) self.assertTrue(np.all(qso1 == qso2)) fstd1 = cuts.isSTD_colors(gflux=gflux, rflux=rflux, zflux=zflux, primary=None) fstd2 = cuts.isSTD_colors(gflux=gflux, rflux=rflux, zflux=zflux, primary=primary) self.assertTrue(np.all(fstd1 == fstd2))
def simulate_one_healpix(ifilename, args, model, obsconditions, decam_and_wise_filters, footprint_healpix_weight, footprint_healpix_nside, seed): log = get_logger() # set seed now # we need a seed per healpix because # the spectra simulator REQUIRES a seed np.random.seed(seed) healpix = 0 nside = 0 vals = os.path.basename(ifilename).split(".")[0].split("-") if len(vals) < 3: log.error("Cannot guess nside and healpix from filename {}".format( ifilename)) raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) try: healpix = int(vals[-1]) nside = int(vals[-2]) except ValueError: raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) zbest_filename = None if args.outfile: ofilename = args.outfile else: ofilename = os.path.join( args.outdir, "{}/{}/spectra-{}-{}.fits".format(healpix // 100, healpix, nside, healpix)) pixdir = os.path.dirname(ofilename) if args.zbest: zbest_filename = os.path.join( pixdir, "zbest-{}-{}.fits".format(nside, healpix)) 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 log.info("Read skewers in {}, random seed = {}".format(ifilename, seed)) trans_wave, transmission, metadata = read_lya_skewers(ifilename) ok = np.where((metadata['Z'] >= args.zmin) & (metadata['Z'] <= args.zmax))[0] transmission = transmission[ok] metadata = metadata[:][ok] # create quasars 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] 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] 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] nqso = args.nmax if args.target_selection or args.mags: 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 zeros at short wavelength because we assume transmission = 0 # and 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.zeros( (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 transmission = 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 log.info("Simulate {} QSOs".format(nqso)) tmp_qso_flux, tmp_qso_wave, meta = model.make_templates( nmodel=nqso, redshift=metadata['Z'], lyaforest=False, nocolorcuts=True, noresample=True, seed=seed) log.info("Resample to transmission wavelength grid") # because we don't want to alter the transmission field with resampling here qso_flux = np.zeros((tmp_qso_flux.shape[0], trans_wave.size)) 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 log.info("Apply lya") tmp_qso_flux = apply_lya_transmission(tmp_qso_wave, tmp_qso_flux, trans_wave, transmission) bbflux = None if args.target_selection or args.mags: bands = ['FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'] bbflux = dict() # need to recompute the magnitudes to account for lya transmission log.info("Compute QSO magnitudes") maggies = decam_and_wise_filters.get_ab_maggies( 1e-17 * tmp_qso_flux, tmp_qso_wave) for band, filt in zip(bands, [ 'decam2014-g', 'decam2014-r', 'decam2014-z', 'wise2010-W1', 'wise2010-W2' ]): bbflux[band] = np.ma.getdata(1e9 * maggies[filt]) # nanomaggies if args.target_selection: log.info("Apply target selection") isqso = isQSO_colors(gflux=bbflux['FLUX_G'], rflux=bbflux['FLUX_R'], zflux=bbflux['FLUX_Z'], w1flux=bbflux['FLUX_W1'], w2flux=bbflux['FLUX_W2']) 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] for band in bands: bbflux[band] = bbflux[band][selection] nqso = selection.size log.info("Resample to a linear wavelength grid (needed by DESI sim.)") # we need a linear grid. for this resampling we take care of integrating in bins # we do not do 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") pixdir = os.path.dirname(ofilename) if len(pixdir) > 0: if not os.path.isdir(pixdir): log.info("Creating dir {}".format(pixdir)) os.makedirs(pixdir) 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 log.warning("Assuming the healpix scheme is 'NESTED'") meta = {"HPXNSIDE": nside, "HPXPIXEL": healpix, "HPXNEST": True} if args.target_selection or args.mags: # today we write mags because that's what is in the fibermap mags = np.zeros((qso_flux.shape[0], 5)) for i, band in enumerate(bands): jj = (bbflux[band] > 0) mags[jj, i] = 22.5 - 2.5 * np.log10(bbflux[band][jj]) # AB magnitudes fibermap_columns = {"MAG": mags} else: fibermap_columns = None 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=meta, seed=seed, fibermap_columns=fibermap_columns) 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"] = fibermap["TARGETID"] 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, clobber=True) hdulist.close() # see if this helps with memory issue
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 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 _star_select(self, targets, truth): """Select stellar (faint and bright) contaminants for the extragalactic targets. """ from desitarget.cuts import isBGS_faint, isELG, isLRG_colors, isQSO_colors gflux, rflux, zflux, w1flux, w2flux = targets['FLUX_G'], targets['FLUX_R'], \ targets['FLUX_Z'], targets['FLUX_W1'], targets['FLUX_W2'] # Select stellar contaminants for BGS_FAINT targets. bgs_faint = isBGS_faint(rflux=rflux) targets['BGS_TARGET'] |= (bgs_faint != 0) * self.bgs_mask.BGS_FAINT targets['BGS_TARGET'] |= (bgs_faint != 0) * self.bgs_mask.BGS_FAINT_SOUTH targets['DESI_TARGET'] |= (bgs_faint != 0) * self.desi_mask.BGS_ANY targets['OBSCONDITIONS'] |= (bgs_faint != 0) * self.obsconditions.mask( self.bgs_mask.BGS_FAINT.obsconditions) targets['OBSCONDITIONS'] |= (bgs_faint != 0) * self.obsconditions.mask( self.bgs_mask.BGS_FAINT_SOUTH.obsconditions) targets['OBSCONDITIONS'] |= (bgs_faint != 0) * self.obsconditions.mask( self.desi_mask.BGS_ANY.obsconditions) truth['CONTAM_TARGET'] |= (bgs_faint != 0) * self.contam_mask.BGS_IS_STAR truth['CONTAM_TARGET'] |= (bgs_faint != 0) * self.contam_mask.BGS_CONTAM # Select stellar contaminants for ELG targets. elg = isELG(gflux=gflux, rflux=rflux, zflux=zflux) targets['DESI_TARGET'] |= (elg != 0) * self.desi_mask.ELG targets['DESI_TARGET'] |= (elg != 0) * self.desi_mask.ELG_SOUTH targets['OBSCONDITIONS'] |= (elg != 0) * self.obsconditions.mask( self.desi_mask.ELG.obsconditions) targets['OBSCONDITIONS'] |= (elg != 0) * self.obsconditions.mask( self.desi_mask.ELG_SOUTH.obsconditions) truth['CONTAM_TARGET'] |= (elg != 0) * self.contam_mask.ELG_IS_STAR truth['CONTAM_TARGET'] |= (elg != 0) * self.contam_mask.ELG_CONTAM # Select stellar contaminants for LRG targets. lrg = isLRG_colors(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux) targets['DESI_TARGET'] |= (lrg != 0) * self.desi_mask.LRG targets['DESI_TARGET'] |= (lrg != 0) * self.desi_mask.LRG_SOUTH targets['OBSCONDITIONS'] |= (lrg != 0) * self.obsconditions.mask( self.desi_mask.LRG.obsconditions) targets['OBSCONDITIONS'] |= (lrg != 0) * self.obsconditions.mask( self.desi_mask.LRG_SOUTH.obsconditions) truth['CONTAM_TARGET'] |= (lrg != 0) * self.contam_mask.LRG_IS_STAR truth['CONTAM_TARGET'] |= (lrg != 0) * self.contam_mask.LRG_CONTAM # Select stellar contaminants for QSO targets. qso = isQSO_colors(gflux=gflux, rflux=rflux, zflux=zflux, w1flux=w1flux, w2flux=w2flux, optical=False) # Note optical=False! targets['DESI_TARGET'] |= (qso != 0) * self.desi_mask.QSO targets['DESI_TARGET'] |= (qso != 0) * self.desi_mask.QSO_SOUTH targets['OBSCONDITIONS'] |= (qso != 0) * self.obsconditions.mask( self.desi_mask.QSO.obsconditions) targets['OBSCONDITIONS'] |= (qso != 0) * self.obsconditions.mask( self.desi_mask.QSO_SOUTH.obsconditions) truth['CONTAM_TARGET'] |= (qso != 0) * self.contam_mask.QSO_IS_STAR truth['CONTAM_TARGET'] |= (qso != 0) * self.contam_mask.QSO_CONTAM
def simulate_one_healpix(ifilename, args, model, obsconditions, decam_and_wise_filters, footprint_healpix_weight, footprint_healpix_nside): log = get_logger() healpix = 0 nside = 0 vals = os.path.basename(ifilename).split(".")[0].split("-") if len(vals) < 3: log.error("Cannot guess nside and healpix from filename {}".format( ifilename)) raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) try: healpix = int(vals[-1]) nside = int(vals[-2]) except ValueError: raise ValueError( "Cannot guess nside and healpix from filename {}".format( ifilename)) zbest_filename = None if args.outfile: ofilename = args.outfile else: ofilename = os.path.join( args.outdir, "{}/{}/spectra-{}-{}.fits".format(healpix // 100, healpix, nside, healpix)) pixdir = os.path.dirname(ofilename) if not args.overwrite: # check whether output exists or not if args.zbest: zbest_filename = os.path.join( pixdir, "zbest-{}-{}.fits".format(nside, healpix)) 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 log.info("Read skewers in {}".format(ifilename)) trans_wave, transmission, metadata = read_lya_skewers(ifilename) ok = np.where((metadata['Z'] >= args.zmin) & (metadata['Z'] <= args.zmax))[0] transmission = transmission[ok] metadata = metadata[:][ok] # set seed now in case we are downsampling np.random.seed(args.seed) # create quasars 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] 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] 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] nqso = args.nmax log.info("Simulate {} QSOs".format(nqso)) tmp_qso_flux, tmp_qso_wave, meta = model.make_templates( nmodel=nqso, redshift=metadata['Z'], seed=args.seed, lyaforest=False, nocolorcuts=True, noresample=True) log.info("Resample to transmission wavelength grid") # because we don't want to alter the transmission field with resampling here qso_flux = np.zeros((tmp_qso_flux.shape[0], trans_wave.size)) 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 log.info("Apply lya") tmp_qso_flux = apply_lya_transmission(tmp_qso_wave, tmp_qso_flux, trans_wave, transmission) if args.target_selection: log.info("Compute QSO magnitudes for target selection") maggies = decam_and_wise_filters.get_ab_maggies(1e-17 * tmp_qso_flux, tmp_qso_wave.copy(), mask_invalid=True) for band, filt in zip( ('FLUX_G', 'FLUX_R', 'FLUX_Z', 'FLUX_W1', 'FLUX_W2'), ('decam2014-g', 'decam2014-r', 'decam2014-z', 'wise2010-W1', 'wise2010-W2')): meta[band] = np.ma.getdata(1e9 * maggies[filt]) # nanomaggies isqso = isQSO_colors(gflux=meta['FLUX_G'], rflux=meta['FLUX_R'], zflux=meta['FLUX_Z'], w1flux=meta['FLUX_W1'], w2flux=meta['FLUX_W2']) 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] nqso = selection.size log.info("Resample to a linear wavelength grid (needed by DESI sim.)") # we need a linear grid. for this resampling we take care of integrating in bins # we do not do 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") pixdir = os.path.dirname(ofilename) if not os.path.isdir(pixdir): log.info("Creating dir {}".format(pixdir)) os.makedirs(pixdir) 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 sim_spectra(qso_wave, qso_flux, args.program, obsconditions=obsconditions, spectra_filename=ofilename, seed=args.seed, sourcetype="qso", skyerr=args.skyerr, ra=metadata["RA"], dec=metadata["DEC"], targetid=targetid) 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"] = fibermap["TARGETID"] 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, clobber=True) hdulist.close() # see if this helps with memory issue