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)