import os import numpy as np import astropy.constants as cons from astropy.table import Table from scipy import interpolate from observation_sim.mock_objects import CatalogBase, Star, Galaxy, Quasar class Catalog(CatalogBase): """An user customizable class for reading in catalog(s) of objects and SEDs. NOTE: must inherit the "CatalogBase" abstract class ... Attributes ---------- cat_dir : str a directory that contains the catalog file(s) star_path : str path to the star catalog file star_SED_path : str path to the star SED data objs : list a list of observation_sim.mock_objects (Star, Galaxy, or Quasar) NOTE: must have "obj" list when implement your own Catalog Methods ---------- load_sed(obj, **kwargs): load the corresponding SED data for one object load_norm_filt(obj): load the filter throughput for the input catalog's photometric system. """ def __init__(self, config, chip, **kwargs): """Constructor method. Parameters ---------- config : dict configuration dictionary which is parsed from the input YAML file chip: observation_sim.instruments.Chip an observation_sim.instruments.Chip instance, can be used to identify the band etc. **kwargs : dict any other needed input parameters (in key-value pairs), please modify corresponding initialization call in "ObservationSim.py" as you need. Returns ---------- None """ super().__init__() self.cat_dir = os.path.join( config["data_dir"], config["catalog_options"]["input_path"]["cat_dir"]) self.chip = chip if "star_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["star_cat"]: star_file = config["catalog_options"]["input_path"]["star_cat"] star_SED_file = config["catalog_options"]["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) # NOTE: must call _load() method here to read in all objects self.objs = [] self._load() def _load(self, **kwargs): """Read in all objects in from the catalog file(s). This is a must implemented method which is used to read in all objects, and then convert them to observation_sim.mock_objects (Star, Galaxy, or Quasar). Currently, the model of observation_sim.mock_objects.Star class requires: param["star"] : int specify the object type: 0: galaxy, 1: star, 2: quasar param["id"] : int ID number of the object param["ra"] : float Right ascension (in degrees) param["dec"] : float Declination (in degrees) param["mag_use_normal"]: float the absolute magnitude in a particular filter NOTE: if that filter is not the corresponding CSST filter, the load_norm_filt(obj) function must be implemented to load the filter throughput of that particular photometric system the model of observation_sim.mock_objects.Galaxy class requires: param["star"] : int specify the object type: 0: galaxy, 1: star, 2: quasar param["id"] : int ID number of the object param["ra"] : float Right ascension (in degrees) param["dec"] : float Declination (in degrees) param["mag_use_normal"]: float the absolute magnitude in a particular filter NOTE: if that filter is not the corresponding CSST filter, the load_norm_filt(obj) function must be implemented to load the filter throughput of that particular photometric system param["bfrac"] : float the bulge fraction param["hlr_bulge"] : float the half-light-radius of the bulge param["hlr_disk"] : float the half-light-radius of the disk param["e1_bulge"], param["e2_bulge"] : float the ellipticity of the bulge components param["e1_disk"], param["e2_disk"] : float the ellipticity of the disk components (Optional parameters): param['disk_sersic_idx']: float Sersic index for galaxy disk component param['bulge_sersic_idx']: float Sersic index for galaxy bulge component param['g1'], param['g2']: float Reduced weak lensing shear components (valid for shear type: catalog) the model of observation_sim.mock_objects.Galaxy class requires: Currently a Quasar is modeled as a point source, just like a Star. NOTE: To construct an object, according to its type, just call: Star(param), Galaxy(param), or Quasar(param) NOTE: All constructed objects should be appened to "self.objs". NOTE: Any other parameters can also be set within "param" dict: Used to calculate required quantities and/or SEDs etc. Parameters ---------- **kwargs : dict any other needed input parameters (in key-value pairs), please modify corresponding initialization call in "ObservationSim.py" as you need. Returns ---------- None """ stars = Table.read(self.star_path) nstars = stars['sourceID'].size for istars in range(nstars): param = self.initialize_param() param['id'] = istars + 1 param['ra'] = stars['RA'][istars] param['dec'] = stars['Dec'][istars] param['sed_type'] = stars['sourceID'][istars] param['model_tag'] = stars['model_tag'][istars] param['z'] = 0.0 param['star'] = 1 # Star param['mag_use_normal'] = stars['app_sdss_g'][istars] obj = Star(param) self.objs.append(obj) def load_sed(self, obj, **kwargs): """Load the corresponding SED data for a particular obj. Parameters ---------- obj : instance of a particular class in observation_sim.mock_objects the object to get SED data for **kwargs : dict other needed input parameters (in key-value pairs), please modify corresponding initialization call in "ObservationSim.py" as you need. Returns ---------- sed : Astropy.Table the SED Table with two columns (namely, "WAVELENGTH", "FLUX"): sed["WAVELENGTH"] : wavelength in Angstroms sed["FLUX"] : fluxes in photons/s/m^2/A NOTE: the range of wavelengthes must at least cover [2450 - 11000] Angstorms """ if obj.type == 'star': wave = Table.read(self.star_SED_path, path=f"/SED/wave_{obj.model_tag}") flux = Table.read(self.star_SED_path, path=f"/SED/{obj.sed_type}") wave, flux = wave['col0'].data, flux['col0'].data else: raise ValueError("Object type not known") speci = interpolate.interp1d(wave, flux) lamb = np.arange(2400, 11001 + 0.5, 0.5) y = speci(lamb) # erg/s/cm^2/A --> photons/s/m^2/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 def load_norm_filt(self, obj): """Load the corresponding thourghput for the input magnitude "param["mag_use_normal"]". NOTE: if the input magnitude is already in CSST magnitude, simply return None Parameters ---------- obj : instance of a particular class in observation_sim.mock_objects the object to get thourghput data for Returns ---------- norm_filt : Astropy.Table the throughput Table with two columns (namely, "WAVELENGTH", "SENSITIVITY"): norm_filt["WAVELENGTH"] : wavelengthes in Angstroms norm_filt["SENSITIVITY"] : efficiencies """ return None