Loading Catalog/C6_50sqdeg.pydeleted 100644 → 0 +0 −476 Original line number Diff line number Diff line import os import galsim import random import copy import numpy as np import h5py as h5 import healpy as hp import astropy.constants as cons import traceback from astropy.coordinates import spherical_to_cartesian from astropy.table import Table from scipy import interpolate from datetime import datetime from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar from ObservationSim.MockObject._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position # (TEST) from astropy.cosmology import FlatLambdaCDM from astropy import constants from astropy import units as U from astropy.coordinates import SkyCoord from astropy.io import fits try: import importlib.resources as pkg_resources except ImportError: # Try backported to PY<37 'importlib_resources' import importlib_resources as pkg_resources NSIDE = 128 bundle_file_list = ['galaxies_C6_bundle000199.h5','galaxies_C6_bundle000200.h5','galaxies_C6_bundle000241.h5','galaxies_C6_bundle000242.h5','galaxies_C6_bundle000287.h5','galaxies_C6_bundle000288.h5','galaxies_C6_bundle000714.h5','galaxies_C6_bundle000715.h5','galaxies_C6_bundle000778.h5','galaxies_C6_bundle000779.h5','galaxies_C6_bundle000842.h5','galaxies_C6_bundle000843.h5','galaxies_C6_bundle002046.h5','galaxies_C6_bundle002110.h5','galaxies_C6_bundle002111.h5','galaxies_C6_bundle002173.h5','galaxies_C6_bundle002174.h5','galaxies_C6_bundle002238.h5','galaxies_C6_bundle002596.h5','galaxies_C6_bundle002597.h5','galaxies_C6_bundle002656.h5','galaxies_C6_bundle002657.h5','galaxies_C6_bundle002711.h5','galaxies_C6_bundle002712.h5','galaxies_C6_bundle002844.h5','galaxies_C6_bundle002845.h5','galaxies_C6_bundle002884.h5','galaxies_C6_bundle002885.h5','galaxies_C6_bundle002921.h5','galaxies_C6_bundle002922.h5'] qsosed_file_list = ['quickspeclib_interp1d_run1.fits','quickspeclib_interp1d_run2.fits','quickspeclib_interp1d_run3.fits','quickspeclib_interp1d_run4.fits','quickspeclib_interp1d_run5.fits','quickspeclib_interp1d_run6.fits','quickspeclib_interp1d_run7.fits','quickspeclib_interp1d_run8.fits','quickspeclib_interp1d_run9.fits','quickspeclib_interp1d_run10.fits','quickspeclib_interp1d_run11.fits','quickspeclib_interp1d_run12.fits','quickspeclib_interp1d_run13.fits','quickspeclib_interp1d_run14.fits','quickspeclib_interp1d_run15.fits','quickspeclib_interp1d_run16.fits','quickspeclib_interp1d_run17.fits','quickspeclib_interp1d_run18.fits','quickspeclib_interp1d_run19.fits','quickspeclib_interp1d_run20.fits','quickspeclib_interp1d_run21.fits','quickspeclib_interp1d_run22.fits','quickspeclib_interp1d_run23.fits','quickspeclib_interp1d_run24.fits','quickspeclib_interp1d_run25.fits','quickspeclib_interp1d_run26.fits','quickspeclib_interp1d_run27.fits','quickspeclib_interp1d_run28.fits','quickspeclib_interp1d_run29.fits','quickspeclib_interp1d_run30.fits'] star_file_list = ['C7_Gaia_Galaxia_RA170DECm23_healpix.hdf5', 'C7_Gaia_Galaxia_RA180DECp60_healpix.hdf5', 'C7_Gaia_Galaxia_RA240DECp30_healpix.hdf5', 'C7_Gaia_Galaxia_RA300DECm60_healpix.hdf5', 'C7_Gaia_Galaxia_RA30DECm48_healpix.hdf5'] star_center_list = [(170., -23.), (180., 60.), (240., 30.), (300., -60.), (30., -48.)] def get_bundleIndex(healpixID_ring, bundleOrder=4, healpixOrder=7): assert NSIDE == 2**healpixOrder shift = healpixOrder - bundleOrder shift = 2*shift nside_bundle = 2**bundleOrder nside_healpix= 2**healpixOrder healpixID_nest= hp.ring2nest(nside_healpix, healpixID_ring) bundleID_nest = (healpixID_nest >> shift) bundleID_ring = hp.nest2ring(nside_bundle, bundleID_nest) return bundleID_ring def get_agnsed_file(bundle_file_name): return qsosed_file_list[bundle_file_list.index(bundle_file_name)] def get_star_cat(ra_pointing, dec_pointing): pointing_c = SkyCoord(ra=ra_pointing*U.deg, dec=dec_pointing*U.deg) max_dist = 10 return_star_path = None for star_file, center in zip(star_file_list, star_center_list): center_c = SkyCoord(ra=center[0]*U.deg, dec=center[1]*U.deg) dist = pointing_c.separation(center_c).to(U.deg).value if dist < max_dist: return_star_path = star_file max_dist = dist return return_star_path class Catalog(CatalogBase): def __init__(self, config, chip, pointing, chip_output, filt, **kwargs): super().__init__() self.cat_dir = config["catalog_options"]["input_path"]["cat_dir"] self.cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111) self.chip_output = chip_output self.filt = filt self.logger = chip_output.logger with pkg_resources.path('Catalog.data', 'SLOAN_SDSS.g.fits') as filter_path: self.normF_star = Table.read(str(filter_path)) self.config = config self.chip = chip self.pointing = pointing self.max_size = 0. if "star_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["star_cat"] and not config["catalog_options"]["galaxy_only"]: # Get the cloest star catalog file star_file_name = get_star_cat(ra_pointing=self.pointing.ra, dec_pointing=self.pointing.dec) star_path = os.path.join(config["catalog_options"]["input_path"]["star_cat"], star_file_name) self.star_path = os.path.join(self.cat_dir, star_path) self.star_SED_path = config["catalog_options"]["SED_templates_path"]["star_SED"] self._load_SED_lib_star() if "galaxy_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["galaxy_cat"] and not config["catalog_options"]["star_only"]: galaxy_dir = config["catalog_options"]["input_path"]["galaxy_cat"] self.galaxy_path = os.path.join(self.cat_dir, galaxy_dir) self.galaxy_SED_path = config["catalog_options"]["SED_templates_path"]["galaxy_SED"] self._load_SED_lib_gals() self.agn_seds = {} if "AGN_SED" in config["catalog_options"]["SED_templates_path"] and not config["catalog_options"]["star_only"]: self.AGN_SED_path = config["catalog_options"]["SED_templates_path"]["AGN_SED"] if "rotateEll" in config["catalog_options"]: self.rotation = np.radians(float(config["catalog_options"]["rotateEll"])) else: self.rotation = 0. # Update output .cat header with catalog specific output columns self._add_output_columns_header() self._get_healpix_list() self._load() def _add_output_columns_header(self): self.add_hdr = " model_tag teff logg feh" self.add_hdr += " bulgemass diskmass detA e1 e2 kappa g1 g2 size galType veldisp " self.add_fmt = " %10s %8.4f %8.4f %8.4f" self.add_fmt += " %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %4d %8.4f " self.chip_output.update_output_header(additional_column_names=self.add_hdr) 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])) self.pix_list = hp.query_polygon( NSIDE, hp.ang2vec(np.radians(90.) - dec, ra), inclusive=True ) if self.logger is not None: msg = str(("HEALPix List: ", self.pix_list)) self.logger.info(msg) else: 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 return None 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): pcs = h5.File(os.path.join(self.galaxy_SED_path, "pcs.h5"), "r") lamb = h5.File(os.path.join(self.galaxy_SED_path, "lamb.h5"), "r") self.lamb_gal = lamb['lamb'][()] self.pcs = pcs['pcs'][()] def _load_gals(self, gals, pix_id=None, cat_id=0, agnsed_file=""): ngals = len(gals['ra']) # Apply astrometric modeling ra_arr = gals['ra'][:] dec_arr = gals['dec'][:] if self.config["obs_setting"]["enable_astrometric_model"]: ra_list = ra_arr.tolist() dec_list = dec_arr.tolist() pmra_list = np.zeros(ngals).tolist() pmdec_list = np.zeros(ngals).tolist() rv_list = np.zeros(ngals).tolist() parallax_list = [1e-9] * ngals dt = datetime.utcfromtimestamp(self.pointing.timestamp) date_str = dt.date().isoformat() time_str = dt.time().isoformat() ra_arr, dec_arr = on_orbit_obs_position( input_ra_list=ra_list, input_dec_list=dec_list, input_pmra_list=pmra_list, input_pmdec_list=pmdec_list, input_rv_list=rv_list, input_parallax_list=parallax_list, input_nstars=ngals, input_x=self.pointing.sat_x, input_y=self.pointing.sat_y, input_z=self.pointing.sat_z, input_vx=self.pointing.sat_vx, input_vy=self.pointing.sat_vy, input_vz=self.pointing.sat_vz, input_epoch="J2000", input_date_str=date_str, input_time_str=time_str ) for igals in range(ngals): # # (TEST) # if igals > 100: # break param = self.initialize_param() param['ra'] = ra_arr[igals] param['dec'] = dec_arr[igals] param['ra_orig'] = gals['ra'][igals] param['dec_orig'] = gals['dec'][igals] if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200): continue # param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals] if self.filt.filter_type == 'NUV': param['mag_use_normal'] = gals['mag_csst_nuv'][igals] else: param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals] if self.filt.is_too_dim(mag=param['mag_use_normal'], margin=self.config["obs_setting"]["mag_lim_margin"]): continue param['z'] = gals['redshift'][igals] param['model_tag'] = 'None' param['g1'] = gals['shear'][igals][0] param['g2'] = gals['shear'][igals][1] param['kappa'] = gals['kappa'][igals] param['e1'] = gals['ellipticity_true'][igals][0] param['e2'] = gals['ellipticity_true'][igals][1] # For shape calculation param['e1'], param['e2'], param['ell_total'] = self.rotate_ellipticity( e1=gals['ellipticity_true'][igals][0], e2=gals['ellipticity_true'][igals][1], rotation=self.rotation, unit='radians') # param['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2) if param['ell_total'] > 0.9: continue # phi_e = cmath.phase(complex(param['e1'], param['e2'])) # param['e1'] = param['ell_total'] * np.cos(phi_e + 2*self.rotation) # param['e2'] = param['ell_total'] * np.sin(phi_e + 2*self.rotation) param['e1_disk'] = param['e1'] param['e2_disk'] = param['e2'] param['e1_bulge'] = param['e1'] param['e2_bulge'] = param['e2'] param['delta_ra'] = 0 param['delta_dec'] = 0 # Masses param['bulgemass'] = gals['bulgemass'][igals] param['diskmass'] = gals['diskmass'][igals] param['size'] = gals['size'][igals] if param['size'] > self.max_size: self.max_size = param['size'] # Sersic index param['disk_sersic_idx'] = 1. param['bulge_sersic_idx'] = 4. # Sizes param['bfrac'] = param['bulgemass']/(param['bulgemass'] + param['diskmass']) if param['bfrac'] >= 0.6: param['hlr_bulge'] = param['size'] param['hlr_disk'] = param['size'] * (1. - param['bfrac']) else: param['hlr_disk'] = param['size'] param['hlr_bulge'] = param['size'] * param['bfrac'] # SED coefficients param['coeff'] = gals['coeff'][igals] param['detA'] = gals['detA'][igals] # Others param['galType'] = gals['type'][igals] param['veldisp'] = gals['veldisp'][igals] # TEST no redening and no extinction param['av'] = 0.0 param['redden'] = 0 # TEMP self.ids += 1 param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals) # Is this an Quasar? param['qsoindex'] = gals['qsoindex'][igals] if param['qsoindex'] == -1: param['star'] = 0 # Galaxy param['agnsed_file'] = "" obj = Galaxy(param, logger=self.logger) else: param_qso = copy.deepcopy(param) param_qso['star'] = 2 # Quasar param_qso['agnsed_file'] = agnsed_file # First add QSO model obj = Quasar(param_qso, logger=self.logger) # Need to deal with additional output columns obj.additional_output_str = self.add_fmt%("n", 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0, 0.) self.objs.append(obj) # Then add host galaxy model param['star'] = 0 # Galaxy param['agnsed_file'] = "" obj = Galaxy(param, logger=self.logger) # Need to deal with additional output columns for (host) galaxy obj.additional_output_str = self.add_fmt%("n", 0., 0., 0., param['bulgemass'], param['diskmass'], param['detA'], param['e1'], param['e2'], param['kappa'], param['g1'], param['g2'], param['size'], param['galType'], param['veldisp']) self.objs.append(obj) def _load_stars(self, stars, pix_id=None): nstars = len(stars['sourceID']) # Apply astrometric modeling ra_arr = stars["RA"][:] dec_arr = stars["Dec"][:] pmra_arr = stars['pmra'][:] pmdec_arr = stars['pmdec'][:] rv_arr = stars['RV'][:] parallax_arr = stars['parallax'][:] if self.config["obs_setting"]["enable_astrometric_model"]: ra_list = ra_arr.tolist() dec_list = dec_arr.tolist() pmra_list = pmra_arr.tolist() pmdec_list = pmdec_arr.tolist() rv_list = rv_arr.tolist() parallax_list = parallax_arr.tolist() dt = datetime.utcfromtimestamp(self.pointing.timestamp) date_str = dt.date().isoformat() time_str = dt.time().isoformat() ra_arr, dec_arr = on_orbit_obs_position( input_ra_list=ra_list, input_dec_list=dec_list, input_pmra_list=pmra_list, input_pmdec_list=pmdec_list, input_rv_list=rv_list, input_parallax_list=parallax_list, input_nstars=nstars, input_x=self.pointing.sat_x, input_y=self.pointing.sat_y, input_z=self.pointing.sat_z, input_vx=self.pointing.sat_vx, input_vy=self.pointing.sat_vy, input_vz=self.pointing.sat_vz, input_epoch="J2000", input_date_str=date_str, input_time_str=time_str ) for istars in range(nstars): # (TEST) # if istars > 100: # break param = self.initialize_param() param['ra'] = ra_arr[istars] param['dec'] = dec_arr[istars] param['ra_orig'] = stars["RA"][istars] param['dec_orig'] = stars["Dec"][istars] param['pmra'] = pmra_arr[istars] param['pmdec'] = pmdec_arr[istars] param['rv'] = rv_arr[istars] param['parallax'] = parallax_arr[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] self.ids += 1 param['id'] = stars['sourceID'][istars] 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, logger=self.logger) # Append additional output columns to the .cat file obj.additional_output_str = self.add_fmt%(param["model_tag"], param['teff'], param['logg'], param['feh'], 0., 0., 0., 0., 0., 0., 0., 0., 0., -1, 0.) self.objs.append(obj) def _load(self, **kwargs): self.objs = [] self.ids = 0 if "star_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["star_cat"] and not self.config["catalog_options"]["galaxy_only"]: star_cat = h5.File(self.star_path, 'r')['catalog'] for pix in self.pix_list: try: stars = star_cat[str(pix)] self._load_stars(stars, pix_id=pix) del stars except Exception as e: self.logger.error(str(e)) print(e) if "galaxy_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["galaxy_cat"] and not self.config["catalog_options"]["star_only"]: for pix in self.pix_list: try: bundleID = get_bundleIndex(pix) bundle_file = "galaxies_C6_bundle{:06}.h5".format(bundleID) file_path = os.path.join(self.galaxy_path, bundle_file) gals_cat = h5.File(file_path, 'r')['galaxies'] gals = gals_cat[str(pix)] # Get corresponding AGN SED file agnsed_file = get_agnsed_file(bundle_file) agnsed_path = os.path.join(self.AGN_SED_path, agnsed_file) self.agn_seds[agnsed_file] = fits.open(agnsed_path)[0].data self._load_gals(gals, pix_id=pix, cat_id=bundleID, agnsed_file=agnsed_file) del gals except Exception as e: traceback.print_exc() self.logger.error(str(e)) print(e) if self.logger is not None: self.logger.info("maximum galaxy size: %.4f"%(self.max_size)) self.logger.info("number of objects in catalog: %d"%(len(self.objs))) else: print("number of objects in catalog: ", len(self.objs)) 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': factor = 10**(-.4 * self.cosmo.distmod(obj.z).value) if obj.type == 'galaxy': flux = np.matmul(self.pcs, obj.coeff) * factor # if np.any(flux < 0): # raise ValueError("Glaxy %s: negative SED fluxes"%obj.id) flux[flux < 0] = 0. sedcat = np.vstack((self.lamb_gal, flux)).T sed_data = getObservedSED( sedCat=sedcat, redshift=obj.z, av=obj.param["av"], redden=obj.param["redden"] ) wave, flux = sed_data[0], sed_data[1] elif obj.type == 'quasar': flux = self.agn_seds[obj.agnsed_file][int(obj.qsoindex)] * 1e-17 flux[flux < 0] = 0. wave = self.lamb_gal * (1.0 + obj.z) else: raise ValueError("Object type not known") speci = interpolate.interp1d(wave, flux) lamb = np.arange(2000, 11001+0.5, 0.5) y = speci(lamb) # erg/s/cm2/A --> photon/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')) if obj.type == 'quasar': # integrate to get the magnitudes sed_photon = np.array([sed['WAVELENGTH'], sed['FLUX']]).T sed_photon = galsim.LookupTable(x=np.array(sed_photon[:, 0]), f=np.array(sed_photon[:, 1]), interpolant='nearest') sed_photon = galsim.SED(sed_photon, wave_type='A', flux_type='1', fast=False) interFlux = integrate_sed_bandpass(sed=sed_photon, bandpass=self.filt.bandpass_full) obj.param['mag_use_normal'] = getABMAG(interFlux, self.filt.bandpass_full) # mag = getABMAG(interFlux, self.filt.bandpass_full) # print("mag diff = %.3f"%(mag - obj.param['mag_use_normal'])) del wave del flux return sed Loading
Catalog/C6_50sqdeg.pydeleted 100644 → 0 +0 −476 Original line number Diff line number Diff line import os import galsim import random import copy import numpy as np import h5py as h5 import healpy as hp import astropy.constants as cons import traceback from astropy.coordinates import spherical_to_cartesian from astropy.table import Table from scipy import interpolate from datetime import datetime from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar from ObservationSim.MockObject._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position # (TEST) from astropy.cosmology import FlatLambdaCDM from astropy import constants from astropy import units as U from astropy.coordinates import SkyCoord from astropy.io import fits try: import importlib.resources as pkg_resources except ImportError: # Try backported to PY<37 'importlib_resources' import importlib_resources as pkg_resources NSIDE = 128 bundle_file_list = ['galaxies_C6_bundle000199.h5','galaxies_C6_bundle000200.h5','galaxies_C6_bundle000241.h5','galaxies_C6_bundle000242.h5','galaxies_C6_bundle000287.h5','galaxies_C6_bundle000288.h5','galaxies_C6_bundle000714.h5','galaxies_C6_bundle000715.h5','galaxies_C6_bundle000778.h5','galaxies_C6_bundle000779.h5','galaxies_C6_bundle000842.h5','galaxies_C6_bundle000843.h5','galaxies_C6_bundle002046.h5','galaxies_C6_bundle002110.h5','galaxies_C6_bundle002111.h5','galaxies_C6_bundle002173.h5','galaxies_C6_bundle002174.h5','galaxies_C6_bundle002238.h5','galaxies_C6_bundle002596.h5','galaxies_C6_bundle002597.h5','galaxies_C6_bundle002656.h5','galaxies_C6_bundle002657.h5','galaxies_C6_bundle002711.h5','galaxies_C6_bundle002712.h5','galaxies_C6_bundle002844.h5','galaxies_C6_bundle002845.h5','galaxies_C6_bundle002884.h5','galaxies_C6_bundle002885.h5','galaxies_C6_bundle002921.h5','galaxies_C6_bundle002922.h5'] qsosed_file_list = ['quickspeclib_interp1d_run1.fits','quickspeclib_interp1d_run2.fits','quickspeclib_interp1d_run3.fits','quickspeclib_interp1d_run4.fits','quickspeclib_interp1d_run5.fits','quickspeclib_interp1d_run6.fits','quickspeclib_interp1d_run7.fits','quickspeclib_interp1d_run8.fits','quickspeclib_interp1d_run9.fits','quickspeclib_interp1d_run10.fits','quickspeclib_interp1d_run11.fits','quickspeclib_interp1d_run12.fits','quickspeclib_interp1d_run13.fits','quickspeclib_interp1d_run14.fits','quickspeclib_interp1d_run15.fits','quickspeclib_interp1d_run16.fits','quickspeclib_interp1d_run17.fits','quickspeclib_interp1d_run18.fits','quickspeclib_interp1d_run19.fits','quickspeclib_interp1d_run20.fits','quickspeclib_interp1d_run21.fits','quickspeclib_interp1d_run22.fits','quickspeclib_interp1d_run23.fits','quickspeclib_interp1d_run24.fits','quickspeclib_interp1d_run25.fits','quickspeclib_interp1d_run26.fits','quickspeclib_interp1d_run27.fits','quickspeclib_interp1d_run28.fits','quickspeclib_interp1d_run29.fits','quickspeclib_interp1d_run30.fits'] star_file_list = ['C7_Gaia_Galaxia_RA170DECm23_healpix.hdf5', 'C7_Gaia_Galaxia_RA180DECp60_healpix.hdf5', 'C7_Gaia_Galaxia_RA240DECp30_healpix.hdf5', 'C7_Gaia_Galaxia_RA300DECm60_healpix.hdf5', 'C7_Gaia_Galaxia_RA30DECm48_healpix.hdf5'] star_center_list = [(170., -23.), (180., 60.), (240., 30.), (300., -60.), (30., -48.)] def get_bundleIndex(healpixID_ring, bundleOrder=4, healpixOrder=7): assert NSIDE == 2**healpixOrder shift = healpixOrder - bundleOrder shift = 2*shift nside_bundle = 2**bundleOrder nside_healpix= 2**healpixOrder healpixID_nest= hp.ring2nest(nside_healpix, healpixID_ring) bundleID_nest = (healpixID_nest >> shift) bundleID_ring = hp.nest2ring(nside_bundle, bundleID_nest) return bundleID_ring def get_agnsed_file(bundle_file_name): return qsosed_file_list[bundle_file_list.index(bundle_file_name)] def get_star_cat(ra_pointing, dec_pointing): pointing_c = SkyCoord(ra=ra_pointing*U.deg, dec=dec_pointing*U.deg) max_dist = 10 return_star_path = None for star_file, center in zip(star_file_list, star_center_list): center_c = SkyCoord(ra=center[0]*U.deg, dec=center[1]*U.deg) dist = pointing_c.separation(center_c).to(U.deg).value if dist < max_dist: return_star_path = star_file max_dist = dist return return_star_path class Catalog(CatalogBase): def __init__(self, config, chip, pointing, chip_output, filt, **kwargs): super().__init__() self.cat_dir = config["catalog_options"]["input_path"]["cat_dir"] self.cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111) self.chip_output = chip_output self.filt = filt self.logger = chip_output.logger with pkg_resources.path('Catalog.data', 'SLOAN_SDSS.g.fits') as filter_path: self.normF_star = Table.read(str(filter_path)) self.config = config self.chip = chip self.pointing = pointing self.max_size = 0. if "star_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["star_cat"] and not config["catalog_options"]["galaxy_only"]: # Get the cloest star catalog file star_file_name = get_star_cat(ra_pointing=self.pointing.ra, dec_pointing=self.pointing.dec) star_path = os.path.join(config["catalog_options"]["input_path"]["star_cat"], star_file_name) self.star_path = os.path.join(self.cat_dir, star_path) self.star_SED_path = config["catalog_options"]["SED_templates_path"]["star_SED"] self._load_SED_lib_star() if "galaxy_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["galaxy_cat"] and not config["catalog_options"]["star_only"]: galaxy_dir = config["catalog_options"]["input_path"]["galaxy_cat"] self.galaxy_path = os.path.join(self.cat_dir, galaxy_dir) self.galaxy_SED_path = config["catalog_options"]["SED_templates_path"]["galaxy_SED"] self._load_SED_lib_gals() self.agn_seds = {} if "AGN_SED" in config["catalog_options"]["SED_templates_path"] and not config["catalog_options"]["star_only"]: self.AGN_SED_path = config["catalog_options"]["SED_templates_path"]["AGN_SED"] if "rotateEll" in config["catalog_options"]: self.rotation = np.radians(float(config["catalog_options"]["rotateEll"])) else: self.rotation = 0. # Update output .cat header with catalog specific output columns self._add_output_columns_header() self._get_healpix_list() self._load() def _add_output_columns_header(self): self.add_hdr = " model_tag teff logg feh" self.add_hdr += " bulgemass diskmass detA e1 e2 kappa g1 g2 size galType veldisp " self.add_fmt = " %10s %8.4f %8.4f %8.4f" self.add_fmt += " %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %4d %8.4f " self.chip_output.update_output_header(additional_column_names=self.add_hdr) 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])) self.pix_list = hp.query_polygon( NSIDE, hp.ang2vec(np.radians(90.) - dec, ra), inclusive=True ) if self.logger is not None: msg = str(("HEALPix List: ", self.pix_list)) self.logger.info(msg) else: 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 return None 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): pcs = h5.File(os.path.join(self.galaxy_SED_path, "pcs.h5"), "r") lamb = h5.File(os.path.join(self.galaxy_SED_path, "lamb.h5"), "r") self.lamb_gal = lamb['lamb'][()] self.pcs = pcs['pcs'][()] def _load_gals(self, gals, pix_id=None, cat_id=0, agnsed_file=""): ngals = len(gals['ra']) # Apply astrometric modeling ra_arr = gals['ra'][:] dec_arr = gals['dec'][:] if self.config["obs_setting"]["enable_astrometric_model"]: ra_list = ra_arr.tolist() dec_list = dec_arr.tolist() pmra_list = np.zeros(ngals).tolist() pmdec_list = np.zeros(ngals).tolist() rv_list = np.zeros(ngals).tolist() parallax_list = [1e-9] * ngals dt = datetime.utcfromtimestamp(self.pointing.timestamp) date_str = dt.date().isoformat() time_str = dt.time().isoformat() ra_arr, dec_arr = on_orbit_obs_position( input_ra_list=ra_list, input_dec_list=dec_list, input_pmra_list=pmra_list, input_pmdec_list=pmdec_list, input_rv_list=rv_list, input_parallax_list=parallax_list, input_nstars=ngals, input_x=self.pointing.sat_x, input_y=self.pointing.sat_y, input_z=self.pointing.sat_z, input_vx=self.pointing.sat_vx, input_vy=self.pointing.sat_vy, input_vz=self.pointing.sat_vz, input_epoch="J2000", input_date_str=date_str, input_time_str=time_str ) for igals in range(ngals): # # (TEST) # if igals > 100: # break param = self.initialize_param() param['ra'] = ra_arr[igals] param['dec'] = dec_arr[igals] param['ra_orig'] = gals['ra'][igals] param['dec_orig'] = gals['dec'][igals] if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200): continue # param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals] if self.filt.filter_type == 'NUV': param['mag_use_normal'] = gals['mag_csst_nuv'][igals] else: param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals] if self.filt.is_too_dim(mag=param['mag_use_normal'], margin=self.config["obs_setting"]["mag_lim_margin"]): continue param['z'] = gals['redshift'][igals] param['model_tag'] = 'None' param['g1'] = gals['shear'][igals][0] param['g2'] = gals['shear'][igals][1] param['kappa'] = gals['kappa'][igals] param['e1'] = gals['ellipticity_true'][igals][0] param['e2'] = gals['ellipticity_true'][igals][1] # For shape calculation param['e1'], param['e2'], param['ell_total'] = self.rotate_ellipticity( e1=gals['ellipticity_true'][igals][0], e2=gals['ellipticity_true'][igals][1], rotation=self.rotation, unit='radians') # param['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2) if param['ell_total'] > 0.9: continue # phi_e = cmath.phase(complex(param['e1'], param['e2'])) # param['e1'] = param['ell_total'] * np.cos(phi_e + 2*self.rotation) # param['e2'] = param['ell_total'] * np.sin(phi_e + 2*self.rotation) param['e1_disk'] = param['e1'] param['e2_disk'] = param['e2'] param['e1_bulge'] = param['e1'] param['e2_bulge'] = param['e2'] param['delta_ra'] = 0 param['delta_dec'] = 0 # Masses param['bulgemass'] = gals['bulgemass'][igals] param['diskmass'] = gals['diskmass'][igals] param['size'] = gals['size'][igals] if param['size'] > self.max_size: self.max_size = param['size'] # Sersic index param['disk_sersic_idx'] = 1. param['bulge_sersic_idx'] = 4. # Sizes param['bfrac'] = param['bulgemass']/(param['bulgemass'] + param['diskmass']) if param['bfrac'] >= 0.6: param['hlr_bulge'] = param['size'] param['hlr_disk'] = param['size'] * (1. - param['bfrac']) else: param['hlr_disk'] = param['size'] param['hlr_bulge'] = param['size'] * param['bfrac'] # SED coefficients param['coeff'] = gals['coeff'][igals] param['detA'] = gals['detA'][igals] # Others param['galType'] = gals['type'][igals] param['veldisp'] = gals['veldisp'][igals] # TEST no redening and no extinction param['av'] = 0.0 param['redden'] = 0 # TEMP self.ids += 1 param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals) # Is this an Quasar? param['qsoindex'] = gals['qsoindex'][igals] if param['qsoindex'] == -1: param['star'] = 0 # Galaxy param['agnsed_file'] = "" obj = Galaxy(param, logger=self.logger) else: param_qso = copy.deepcopy(param) param_qso['star'] = 2 # Quasar param_qso['agnsed_file'] = agnsed_file # First add QSO model obj = Quasar(param_qso, logger=self.logger) # Need to deal with additional output columns obj.additional_output_str = self.add_fmt%("n", 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0, 0.) self.objs.append(obj) # Then add host galaxy model param['star'] = 0 # Galaxy param['agnsed_file'] = "" obj = Galaxy(param, logger=self.logger) # Need to deal with additional output columns for (host) galaxy obj.additional_output_str = self.add_fmt%("n", 0., 0., 0., param['bulgemass'], param['diskmass'], param['detA'], param['e1'], param['e2'], param['kappa'], param['g1'], param['g2'], param['size'], param['galType'], param['veldisp']) self.objs.append(obj) def _load_stars(self, stars, pix_id=None): nstars = len(stars['sourceID']) # Apply astrometric modeling ra_arr = stars["RA"][:] dec_arr = stars["Dec"][:] pmra_arr = stars['pmra'][:] pmdec_arr = stars['pmdec'][:] rv_arr = stars['RV'][:] parallax_arr = stars['parallax'][:] if self.config["obs_setting"]["enable_astrometric_model"]: ra_list = ra_arr.tolist() dec_list = dec_arr.tolist() pmra_list = pmra_arr.tolist() pmdec_list = pmdec_arr.tolist() rv_list = rv_arr.tolist() parallax_list = parallax_arr.tolist() dt = datetime.utcfromtimestamp(self.pointing.timestamp) date_str = dt.date().isoformat() time_str = dt.time().isoformat() ra_arr, dec_arr = on_orbit_obs_position( input_ra_list=ra_list, input_dec_list=dec_list, input_pmra_list=pmra_list, input_pmdec_list=pmdec_list, input_rv_list=rv_list, input_parallax_list=parallax_list, input_nstars=nstars, input_x=self.pointing.sat_x, input_y=self.pointing.sat_y, input_z=self.pointing.sat_z, input_vx=self.pointing.sat_vx, input_vy=self.pointing.sat_vy, input_vz=self.pointing.sat_vz, input_epoch="J2000", input_date_str=date_str, input_time_str=time_str ) for istars in range(nstars): # (TEST) # if istars > 100: # break param = self.initialize_param() param['ra'] = ra_arr[istars] param['dec'] = dec_arr[istars] param['ra_orig'] = stars["RA"][istars] param['dec_orig'] = stars["Dec"][istars] param['pmra'] = pmra_arr[istars] param['pmdec'] = pmdec_arr[istars] param['rv'] = rv_arr[istars] param['parallax'] = parallax_arr[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] self.ids += 1 param['id'] = stars['sourceID'][istars] 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, logger=self.logger) # Append additional output columns to the .cat file obj.additional_output_str = self.add_fmt%(param["model_tag"], param['teff'], param['logg'], param['feh'], 0., 0., 0., 0., 0., 0., 0., 0., 0., -1, 0.) self.objs.append(obj) def _load(self, **kwargs): self.objs = [] self.ids = 0 if "star_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["star_cat"] and not self.config["catalog_options"]["galaxy_only"]: star_cat = h5.File(self.star_path, 'r')['catalog'] for pix in self.pix_list: try: stars = star_cat[str(pix)] self._load_stars(stars, pix_id=pix) del stars except Exception as e: self.logger.error(str(e)) print(e) if "galaxy_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["galaxy_cat"] and not self.config["catalog_options"]["star_only"]: for pix in self.pix_list: try: bundleID = get_bundleIndex(pix) bundle_file = "galaxies_C6_bundle{:06}.h5".format(bundleID) file_path = os.path.join(self.galaxy_path, bundle_file) gals_cat = h5.File(file_path, 'r')['galaxies'] gals = gals_cat[str(pix)] # Get corresponding AGN SED file agnsed_file = get_agnsed_file(bundle_file) agnsed_path = os.path.join(self.AGN_SED_path, agnsed_file) self.agn_seds[agnsed_file] = fits.open(agnsed_path)[0].data self._load_gals(gals, pix_id=pix, cat_id=bundleID, agnsed_file=agnsed_file) del gals except Exception as e: traceback.print_exc() self.logger.error(str(e)) print(e) if self.logger is not None: self.logger.info("maximum galaxy size: %.4f"%(self.max_size)) self.logger.info("number of objects in catalog: %d"%(len(self.objs))) else: print("number of objects in catalog: ", len(self.objs)) 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': factor = 10**(-.4 * self.cosmo.distmod(obj.z).value) if obj.type == 'galaxy': flux = np.matmul(self.pcs, obj.coeff) * factor # if np.any(flux < 0): # raise ValueError("Glaxy %s: negative SED fluxes"%obj.id) flux[flux < 0] = 0. sedcat = np.vstack((self.lamb_gal, flux)).T sed_data = getObservedSED( sedCat=sedcat, redshift=obj.z, av=obj.param["av"], redden=obj.param["redden"] ) wave, flux = sed_data[0], sed_data[1] elif obj.type == 'quasar': flux = self.agn_seds[obj.agnsed_file][int(obj.qsoindex)] * 1e-17 flux[flux < 0] = 0. wave = self.lamb_gal * (1.0 + obj.z) else: raise ValueError("Object type not known") speci = interpolate.interp1d(wave, flux) lamb = np.arange(2000, 11001+0.5, 0.5) y = speci(lamb) # erg/s/cm2/A --> photon/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')) if obj.type == 'quasar': # integrate to get the magnitudes sed_photon = np.array([sed['WAVELENGTH'], sed['FLUX']]).T sed_photon = galsim.LookupTable(x=np.array(sed_photon[:, 0]), f=np.array(sed_photon[:, 1]), interpolant='nearest') sed_photon = galsim.SED(sed_photon, wave_type='A', flux_type='1', fast=False) interFlux = integrate_sed_bandpass(sed=sed_photon, bandpass=self.filt.bandpass_full) obj.param['mag_use_normal'] = getABMAG(interFlux, self.filt.bandpass_full) # mag = getABMAG(interFlux, self.filt.bandpass_full) # print("mag diff = %.3f"%(mag - obj.param['mag_use_normal'])) del wave del flux return sed
