import os import galsim import random import numpy as np import h5py as h5 import healpy as hp import astropy.constants as cons from astropy.coordinates import spherical_to_cartesian from astropy.table import Table from scipy import interpolate from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar from ObservationSim.MockObject._util import seds, sed_assign, extAv, tag_sed, getObservedSED NSIDE = 128 class C3Catalog(CatalogBase): def __init__(self, config, chip, **kwargs): super().__init__() self.cat_dir = os.path.join(config["data_dir"], config["input_path"]["cat_dir"]) self.seed_Av = config["random_seeds"]["seed_Av"] self.normalize_dir = os.path.join(config["data_dir"], config["SLS_path"]["SLS_norm"]) self.normF_star = Table.read(os.path.join(self.normalize_dir, 'SLOAN_SDSS.g.fits')) self.normF_galaxy = Table.read(os.path.join(self.normalize_dir, 'lsst_throuput_g.fits')) self.chip = chip if "star_cat" in config["input_path"] and config["input_path"]["star_cat"]: star_file = config["input_path"]["star_cat"] star_SED_file = config["SED_templates_path"]["star_SED"] self.star_path = os.path.join(self.cat_dir, star_file) self.star_SED_path = os.path.join(config["data_dir"], star_SED_file) self._load_SED_lib_star() if "galaxy_cat" in config["input_path"] and config["input_path"]["galaxy_cat"]: galaxy_file = config["input_path"]["galaxy_cat"] self.galaxy_path = os.path.join(self.cat_dir, galaxy_file) self.galaxy_SED_path = os.path.join(config["data_dir"], config["SED_templates_path"]["galaxy_SED"]) self._load_SED_lib_gals() if "rotateEll" in config["shear_setting"]: self.rotation = float(int(config["shear_setting"]["rotateEll"]/45.)) else: self.rotation = 0. self._get_healpix_list() self._load() def _get_healpix_list(self): self.sky_coverage = self.chip.getSkyCoverageEnlarged(self.chip.img.wcs, margin=0.2) ra_min, ra_max, dec_min, dec_max = self.sky_coverage.xmin, self.sky_coverage.xmax, self.sky_coverage.ymin, self.sky_coverage.ymax ra = np.deg2rad(np.array([ra_min, ra_max, ra_max, ra_min])) dec = np.deg2rad(np.array([dec_max, dec_max, dec_min, dec_min])) vertices = spherical_to_cartesian(1., dec, ra) self.pix_list = hp.query_polygon(NSIDE, np.array(vertices).T, inclusive=True) print("HEALPix List: ", self.pix_list) def load_norm_filt(self, obj): if obj.type == "star": return self.normF_star elif obj.type == "galaxy" or obj.type == "quasar": return self.normF_galaxy else: return None def _load_SED_lib_star(self): self.tempSED_star = h5.File(self.star_SED_path,'r') def _load_SED_lib_gals(self): self.tempSed_gal, self.tempRed_gal = seds("galaxy.list", seddir=self.galaxy_SED_path) def _load_gals(self, gals, pix_id=None): ngals = len(gals['galaxyID']) self.rng_sedGal = random.Random() self.rng_sedGal.seed(pix_id) # Use healpix index as the random seed self.ud = galsim.UniformDeviate(pix_id) for igals in range(ngals): param = self.initialize_param() param['ra'] = gals['ra_true'][igals] param['dec'] = gals['dec_true'][igals] param['z'] = gals['redshift_true'][igals] param['model_tag'] = 'None' param['gamma1'] = 0 param['gamma2'] = 0 param['kappa'] = 0 param['delta_ra'] = 0 param['delta_dec'] = 0 sersicB = gals['sersic_bulge'][igals] hlrMajB = gals['size_bulge_true'][igals] hlrMinB = gals['size_minor_bulge_true'][igals] sersicD = gals['sersic_disk'][igals] hlrMajD = gals['size_disk_true'][igals] hlrMinD = gals['size_minor_disk_true'][igals] aGal = gals['size_true'][igals] bGal = gals['size_minor_true'][igals] param['bfrac'] = gals['bulge_to_total_ratio_i'][igals] param['theta'] = gals['position_angle_true'][igals] param['hlr_bulge'] = np.sqrt(hlrMajB * hlrMinB) param['hlr_disk'] = np.sqrt(hlrMajD * hlrMinD) param['ell_bulge'] = (hlrMajB - hlrMinB)/(hlrMajB + hlrMinB) param['ell_disk'] = (hlrMajD - hlrMinD)/(hlrMajD + hlrMinD) param['ell_tot'] = (aGal - bGal) / (aGal + bGal) # Assign each galaxy a template SED param['sed_type'] = sed_assign(phz=param['z'], btt=param['bfrac'], rng=self.rng_sedGal) param['redden'] = self.tempRed_gal[param['sed_type']] param['av'] = self.avGal[int(self.ud()*self.nav)] if param['sed_type'] <= 5: param['av'] = 0.0 param['redden'] = 0 param['star'] = 0 # Galaxy if param['sed_type'] >= 29: param['av'] = 0.6 * param['av'] / 3.0 # for quasar, av=[0, 0.2], 3.0=av.max-av.im param['star'] = 2 # Quasar if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200): continue param['mag_use_normal'] = gals['mag_true_g_lsst'][igals] if param['mag_use_normal'] >= 26.5: continue self.ids += 1 param['id'] = self.ids if param['star'] == 0: obj = Galaxy(param, self.rotation) self.objs.append(obj) if param['star'] == 2: obj = Quasar(param) self.objs.append(obj) def _load_stars(self, stars, pix_id=None): nstars = len(stars['sourceID']) for istars in range(nstars): param = self.initialize_param() param['ra'] = stars['RA'][istars] param['dec'] = stars['Dec'][istars] if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200): continue param['mag_use_normal'] = stars['app_sdss_g'][istars] if param['mag_use_normal'] >= 26.5: continue self.ids += 1 param['id'] = self.ids param['sed_type'] = stars['sourceID'][istars] param['model_tag'] = stars['model_tag'][istars] param['teff'] = stars['teff'][istars] param['logg'] = stars['grav'][istars] param['feh'] = stars['feh'][istars] param['z'] = 0.0 param['star'] = 1 # Star obj = Star(param) self.objs.append(obj) def _load(self, **kwargs): self.nav = 15005 self.avGal = extAv(self.nav, seed=self.seed_Av) gals_cat = h5.File(self.galaxy_path, 'r')['galaxies'] star_cat = h5.File(self.star_path, 'r')['catalog'] self.objs = [] self.ids = 0 for pix in self.pix_list: gals = gals_cat[str(pix)] stars = star_cat[str(pix)] self._load_gals(gals, pix_id=pix) self._load_stars(stars, pix_id=pix) print("number of objects in catalog: ", len(self.objs)) del self.avGal def load_sed(self, obj, **kwargs): if obj.type == 'star': _, wave, flux = tag_sed( h5file=self.tempSED_star, model_tag=obj.param['model_tag'], teff=obj.param['teff'], logg=obj.param['logg'], feh=obj.param['feh'] ) elif obj.type == 'galaxy' or obj.type == 'quasar': sed_data = getObservedSED( sedCat=self.tempSed_gal[obj.sed_type], redshift=obj.z, av=obj.param["av"], redden=obj.param["redden"] ) wave, flux = sed_data[0], sed_data[1] else: raise ValueError("Object type not known") speci = interpolate.interp1d(wave, flux) # lamb = np.arange(2500, 10001 + 0.5, 0.5) lamb = np.arange(2400, 11001 + 0.5, 0.5) y = speci(lamb) # erg/s/cm2/A --> photo/s/m2/A all_sed = y * lamb / (cons.h.value * cons.c.value) * 1e-13 sed = Table(np.array([lamb, all_sed]).T, names=('WAVELENGTH', 'FLUX')) return sed