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fd6c3108
import os
import galsim
import random
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, Stamp
from ObservationSim.MockObject._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
import astropy.io.fits as fitsio
from ObservationSim.MockObject._util import seds, sed_assign, extAv
# (TEST)
from astropy.cosmology import FlatLambdaCDM
from astropy import constants
from astropy import units as U
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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 ObservationSim.MockObject (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, pointing, chip_output, filt, **kwargs):
"""Constructor method.
Parameters
----------
config : dict
configuration dictionary which is parsed from the input YAML file
chip: ObservationSim.Instrument.Chip
an ObservationSim.Instrument.Chip instance, can be used to identify the band etc.
pointing: ObservationSim.Config.Pointing
an ObservationSim.Config.Pointing instance, can be used to configure the astrometry module
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chip_output: ObservationSim.Config.ChipOutput
an ObservationSim.Config.ChipOutput instance, can be used to setup the output format
filt: ObservationSim.Instrument.Filter
an ObservationSim.Instrument.Filter instance, can be used to identify the filter type
**kwargs : dict
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.seed_Av = config["catalog_options"]["seed_Av"]
#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 config["catalog_options"]["star_yes"]:
self.star_path = os.path.join(self.cat_dir, config["catalog_options"]["input_path"]["star_cat"])
self.star_SED_path = os.path.join(self.cat_dir, config["catalog_options"]["SED_templates_path"]["star_SED"])
self._load_SED_lib_star()
if "rotateEll" in config["catalog_options"]:
self.rotation = float(int(config["catalog_options"]["rotateEll"]/45.))
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_ouptut_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]))
# vertices = spherical_to_cartesian(1., dec, ra)
self.pix_list = hp.query_polygon(
NSIDE,
hp.ang2vec(np.radians(90.) - dec, ra),
inclusive=True
)
# self.pix_list = hp.query_polygon(NSIDE, np.array(vertices).T, inclusive=True)
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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):
"""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 : ObservationSim.MockObject
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
"""
if obj.type == "star":
return self.normF_star
else:
return None
def _load_SED_lib_star(self):
self.tempSED_star = h5.File(self.star_SED_path,'r')
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)
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# 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]
# if param['mag_use_normal'] >= 26.5:
# continue
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):
"""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 ObservationSim.MockObject (Star, Galaxy, or Quasar).
Currently,
the model of ObservationSim.MockObject.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 ObservationSim.MockObject.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
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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 ObservationSim.MockObject.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
other needed input parameters (in key-value pairs), please modify corresponding
initialization call in "ObservationSim.py" as you need.
Returns
----------
None
"""
self.objs = []
self.ids = 0
#if "star_cat" in self.config["input_path"] and self.config["input_path"]["star_cat"] and not self.config["run_option"]["galaxy_only"]:
if "star_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["star_cat"] and self.config["catalog_options"]["star_yes"]:
star_cat = h5.File(self.star_path, 'r')['stars']
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 self.logger is not None:
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):
"""Load the corresponding SED data for a particular obj.
Parameters
----------
obj : ObservationSim.MockObject
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
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----------
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, 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']
)
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'))
del wave
del flux
return sed