Skip to content
GitLab
Commit dd26d370 authored by JX's avatar JX 😵
Browse files

Merge remote-tracking branch 'origin/develop'

parents 50fd7c8d 4c9c940a
Pipeline #4458 passed with stage
in 0 seconds
Hide whitespace changes
Inline Side-by-side
Catalog/C6_50sqdeg.py deleted 100644 → 0
View file @ 50fd7c8d
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
Catalog/C6_Catalog.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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 not config["catalog_options"]["galaxy_only"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
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]))
# vertices = spherical_to_cartesian(1., dec, ra)
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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]
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['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_AGNs()
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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/C6_fits_Catalog.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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"]
# (TEST)
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))
with pkg_resources.path('Catalog.data', 'lsst_throuput_g.fits') as filter_path:
self.normF_galaxy = 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"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
###mock_stamp_START
if "stamp_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["stamp_cat"] and config["catalog_options"]["stamp_yes"]:
stamp_file = config["catalog_options"]["input_path"]["stamp_cat"]
self.stamp_path = os.path.join(self.cat_dir, stamp_file)
self.tempSed_gal, self.tempRed_gal = seds("galaxy.list", seddir="/share/simudata/CSSOSDataProductsSims/data/Templates/Galaxy/") #only for test
###mock_stamp_END
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]))
# 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)
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
###mock_stamp_START
elif obj.type == "stamp":
return self.normF_galaxy ###normalize_filter for stamp
#return None
###mock_stamp_END
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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 > 2000:
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]
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['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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']
# 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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
if iAGNs > 100:
break
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., -1, 0.)
self.objs.append(obj)
###mock_stamp_START
def _load_stamps(self, stamps, pix_id=None):
nstamps = len(stamps['filename'])
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 istamp in range(nstamps):
print('DEBUG:::istamp=', istamp)
fitsfile = os.path.join(self.cat_dir, "stampCats/"+stamps['filename'][istamp].decode('utf-8'))
hdu=fitsio.open(fitsfile)
param = self.initialize_param()
param['id'] = hdu[0].header['index'] #istamp
param['star'] = 3 # Stamp type in .cat file
###param['lensGalaxyID'] = hdu[0].header['lensGID']
param['ra'] = hdu[0].header['ra']
param['dec']= hdu[0].header['dec']
param['pixScale']= hdu[0].header['pixScale']
#param['srcGalaxyID'] = hdu[0].header['srcGID']
#param['mu']= hdu[0].header['mu']
#param['PA']= hdu[0].header['PA']
#param['bfrac']= hdu[0].header['bfrac']
#param['z']= hdu[0].header['z']
param['mag_use_normal'] = 20 #hdu[0].header['m_normal'] #gals['mag_true_g_lsst']
###assert(stamps['lensGID'][istamp] == param['lensGalaxyID'])
# Apply astrometric modeling
# in C3 case only aberration
param['ra_orig'] = param['ra']
param['dec_orig']= param['dec']
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = [param['ra']] #ra_arr.tolist()
dec_list= [param['dec']] #dec_arr.tolist()
pmra_list = np.zeros(1).tolist()
pmdec_list = np.zeros(1).tolist()
rv_list = np.zeros(1).tolist()
parallax_list = [1e-9] * 1
dt = datetime.fromtimestamp(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=1,
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="J2015.5",
input_date_str=date_str,
input_time_str=time_str
)
param['ra'] = ra_arr[0]
param['dec']= dec_arr[0]
# 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'] = 0.0
param['redden'] = 0
#param["CSSTmag"]= True
#param["mag_r"] = 20.
#param['']
###more keywords for stamp###
param['image'] = hdu[0].data
param['image'] = param['image']/(np.sum(param['image']))
obj = Stamp(param)
self.objs.append(obj)
###mock_stamp_END
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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_AGNs()
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
###mock_stamp_START
if "stamp_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["stamp_cat"] and self.config["catalog_options"]["stamp_yes"]:
stamps_cat = h5.File(self.stamp_path, 'r')['Stamps']
for pix in self.pix_list:
try:
stamps = stamps_cat[str(pix)]
self._load_stamps(stamps, pix_id=pix)
del stamps
except Exception as e:
self.logger.error(str(e))
print(e)
###mock_stamp_END
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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
###mock_stamp_START
elif obj.type == 'stamp':
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]
###mock_stamp_END
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/Calib_Catalog.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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 not config["catalog_options"]["galaxy_only"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
if "CALIB_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"][
"CALIB_cat"] and not config["catalog_options"]["star_only"]:
self.CALIB_cat_path = os.path.join(config["data_dir"],
config["catalog_options"]["input_path"]["CALIB_cat"])
self.CALIB_SED_path = os.path.join(config["data_dir"],
config["catalog_options"]["SED_templates_path"]["CALIB_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]))
# vertices = spherical_to_cartesian(1., dec, ra)
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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]
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['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., -1, 0.)
self.objs.append(obj)
def _load_calibObj(self):
data = Table.read(self.CALIB_cat_path)
ra_arr = data['RA']
dec_arr = data['DEC']
ngals = len(data)
# Apply astrometric modeling
# in C3 case only aberration
# 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'] = data['RA'][igals]
param['dec_orig'] = data['DEC'][igals]
param['mag_use_normal'] = data['MAG_g'][igals]
# if self.filt.is_too_dim(mag=param['mag_use_normal'], margin=self.config["obs_setting"]["mag_lim_margin"]):
# continue
param['z'] = -99
param['model_tag'] = 'None'
param['g1'] = 0
param['g2'] = 0
param['kappa'] = 0
param['e1'] = 0
param['e2'] = 0
# For shape calculation
param['ell_total'] = np.sqrt(param['e1'] ** 2 + param['e2'] ** 2)
if param['ell_total'] > 0.9:
continue
param['e1_disk'] = 0
param['e2_disk'] = 0
param['e1_bulge'] = 0
param['e2_bulge'] = 0
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'] = data['SERSIC_N'][igals]
param['bulge_sersic_idx'] = 1.
param['hlr_bulge'] = data['RE'][igals]
param['hlr_disk'] = data['RE'][igals]
param['bfrac'] = 0
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 4
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = data['SPEC_FN'][igals][0:-5]
if param['star'] == 4:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
# 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(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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_AGNs()
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "CALIB_cat" in self.config["catalog_options"]["input_path"] and \
self.config["catalog_options"]["input_path"][
"CALIB_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_calibObj()
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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
elif obj.type == 'calib':
data = Table.read(os.path.join(self.CALIB_SED_path,obj.id+'.fits'))
obj_w = data['WAVELENGTH']
obj_f = data['FLUX']
input_delt_w = np.min(obj_w[1:]-obj_w[0:-1])
if input_delt_w > 0.5:
lamb = np.arange(2000, 11000 + 0.5, 0.5)
speci = interpolate.interp1d(obj_w, obj_f)
y1 = speci(lamb)
else:
lamb = obj_w
y1 = obj_f
# erg/s/cm2/A --> photon/s/m2/A
y1_phot = y1 * lamb / (cons.h.value * cons.c.value) * 1e-13
sed = Table(np.array([lamb, y1_phot]).T,
names=('WAVELENGTH', 'FLUX'))
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)
return sed
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/FGS_Catalog.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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"]
# (TEST)
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"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
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]))
# vertices = spherical_to_cartesian(1., dec, ra)
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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]
# 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['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_AGNs()
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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
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':
if obj.type == 'galaxy' or 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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/NGPCatalog.py deleted 100644 → 0
View file @ 50fd7c8d
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 datetime import datetime
from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar
from ObservationSim.MockObject._util import seds, sed_assign, extAv, tag_sed, getObservedSED
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
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):
def __init__(self, config, chip, pointing, chip_output, **kwargs):
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.chip_output = chip_output
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))
with pkg_resources.path('Catalog.data', 'lsst_throuput_g.fits') as filter_path:
self.normF_galaxy = Table.read(str(filter_path))
self.config = config
self.chip = chip
self.pointing = pointing
if "star_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["star_cat"] and not config["catalog_options"]["galaxy_only"]:
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)
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_file = config["catalog_options"]["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["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
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_fmt = " %10s %8.4f %8.4f %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]))
vertices = spherical_to_cartesian(1., dec, ra)
self.pix_list = hp.query_polygon(NSIDE, np.array(vertices).T, 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
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)
# Apply astrometric modeling
# in C3 case only aberration
ra_arr = gals['ra_true'][:]
dec_arr = gals['dec_true'][:]
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="J2015.5",
input_date_str=date_str,
input_time_str=time_str
)
for igals in range(ngals):
param = self.initialize_param()
param['ra'] = ra_arr[igals]
param['dec'] = dec_arr[igals]
param['ra_orig'] = gals['ra_true'][igals]
param['dec_orig'] = gals['dec_true'][igals]
param['mag_use_normal'] = gals['mag_true_g_lsst'][igals]
# if param['mag_use_normal'] >= 26.5:
# continue
param['z'] = gals['redshift_true'][igals]
param['model_tag'] = 'None'
param['g1'] = 0
param['g2'] = 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)]
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
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
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
self.ids += 1
# param['id'] = self.ids
param['id'] = gals['galaxyID'][igals]
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Need to deal with additional output columns
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.)
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="J2015.5",
input_date_str=date_str,
input_time_str=time_str
)
for istars in range(nstars):
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'] = self.ids
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'])
self.objs.append(obj)
def _load(self, **kwargs):
self.nav = 15005
self.avGal = extAv(self.nav, seed=self.seed_Av)
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"]:
gals_cat = h5.File(self.galaxy_path, 'r')['galaxies']
for pix in self.pix_list:
try:
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix)
del gals
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))
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(2000, 18001 + 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'))
del wave
del flux
return sed
Catalog/fd_test_C6.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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 not config["catalog_options"]["galaxy_only"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
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]))
# vertices = spherical_to_cartesian(1., dec, ra)
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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):
for igals in range(0, ngals, 5):
# # (TEST)
# if igals > 1000:
# 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]
# param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals]
param['mag_use_normal'] = 20.
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['g1'] = 0.
param['g2'] = 0.
param['kappa'] = gals['kappa'][igals]
param['e1'] = gals['ellipticity_true'][igals][0]
param['e2'] = gals['ellipticity_true'][igals][1]
# For shape calculation
param['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
# param['e1_disk'] = param['e1']
# param['e2_disk'] = param['e2']
# param['e1_bulge'] = param['e1']
# param['e2_bulge'] = param['e2']
param['e1_disk'] = 0.
param['e2_disk'] = 0.
param['e1_bulge'] = 0.
param['e2_bulge'] = 0.
param['delta_ra'] = 0
param['delta_dec'] = 0
# Masses
param['bulgemass'] = gals['bulgemass'][igals]
param['diskmass'] = gals['diskmass'][igals]
# param['size'] = gals['size'][igals]
param['size'] = 1.
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'])
param['bfrac'] = 0.
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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
# if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
# try:
# self._load_AGNs()
# 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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/testCat_fits.py
View file @ dd26d370
......@@ -34,8 +34,8 @@ NSIDE = 128
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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.cat_dir = config["catalog_options"]["input_path"]["cat_dir"]
self.seed_Av = 121212 #config["catalog_options"]["seed_Av"]
# (TEST)
self.cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111)
......@@ -46,6 +46,8 @@ class Catalog(CatalogBase):
with pkg_resources.path('Catalog.data', 'SLOAN_SDSS.g.fits') as filter_path:
self.normF_star = Table.read(str(filter_path))
with pkg_resources.path('Catalog.data', 'lsst_throuput_g.fits') as filter_path:
self.normF_galaxy = Table.read(str(filter_path))
self.config = config
self.chip = chip
......@@ -58,16 +60,9 @@ class Catalog(CatalogBase):
self.stamp_path = os.path.join(self.cat_dir, stamp_file)
#self.stamp_SED_path = os.path.join(config["data_dir"], config["SED_templates_path"]["stamp_SED"]) ###shoule be stamp-SED
#self._load_SED_lib_stamps() ###shoule be stamp-SED
self.tempSed_gal, self.tempRed_gal = seds("galaxy.list", seddir="/share/simudata/CSSOSDataProductsSims/data/Templates/Galaxy/") #only for test
if "rotateEll" in config["catalog_options"]:
self.rotation = float(int(config["catalog_options"]["rotateEll"]/45.))
else:
self.rotation = 0.
self.tempSed_gal, self.tempRed_gal = seds("galaxy.list", seddir="/public/home/chengliang/CSSOSDataProductsSims/testCats/Templates/Galaxy/") #only for test
# Update output .cat header with catalog specific output columns
self._add_output_columns_header()
self._get_healpix_list()
self._load()
......@@ -99,19 +94,21 @@ class Catalog(CatalogBase):
def load_norm_filt(self, obj):
if obj.type == "stamp":
#return self.normF_galaxy ###normalize_filter for stamp
return None
return self.normF_galaxy ###normalize_filter for stamp
else:
return None
def _load_stamps(self, stamps, pix_id=None):
print("debug:: load_stamps")
nstamps = len(stamps['filename'])
self.rng_sedGal = random.Random()
self.rng_sedGal.seed(pix_id) # Use healpix index as the random seed
self.rng_sedGal.seed(float(pix_id)) # Use healpix index as the random seed
self.ud = galsim.UniformDeviate(pix_id)
for istamp in range(nstamps):
print("debug::", istamp)
fitsfile = os.path.join(self.cat_dir, "stampCats/"+stamps['filename'][istamp].decode('utf-8'))
print("debug::", istamp, fitsfile)
hdu=fitsio.open(fitsfile)
param = self.initialize_param()
......@@ -167,6 +164,7 @@ class Catalog(CatalogBase):
param['redden'] = self.tempRed_gal[param['sed_type']]
param['av'] = 0.0
param['redden'] = 0
param['mu'] = 1
#param["CSSTmag"]= True
#param["mag_r"] = 20.
......@@ -183,9 +181,12 @@ class Catalog(CatalogBase):
if "stamp_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["stamp_cat"] and self.config["catalog_options"]["stamp_yes"]:
stamps_cat = h5.File(self.stamp_path, 'r')['Stamps']
print("debug::",stamps_cat.keys())
for pix in self.pix_list:
try:
stamps = stamps_cat[str(pix)]
print("debug::",stamps.keys())
self._load_stamps(stamps, pix_id=pix)
del stamps
except Exception as e:
......
Catalog/testCat_galaxy.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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"]
# (TEST)
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 "galaxy_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["galaxy_cat"] and config["catalog_options"]["galaxy_yes"]:
self.galaxy_path = os.path.join(self.cat_dir, config["catalog_options"]["input_path"]["galaxy_cat"])
self.galaxy_SED_path = os.path.join(self.cat_dir, config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and config["catalog_options"]["galaxy_yes"]:
self.AGN_path = os.path.join(self.cat_dir, config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(self.cat_dir, config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(self.cat_dir, config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
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]))
# 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)
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 == "galaxy" or obj.type == "quasar":
# return self.normF_galaxy
return None
else:
return None
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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
# if igals < 3447:
# continue
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]
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
# if param['mag_use_normal'] >= 26.5:
# 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['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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']
# 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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
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 "galaxy_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["galaxy_cat"] and self.config["catalog_options"]["galaxy_yes"]:
# for i in range(76):
# file_path = os.path.join(self.galaxy_path, "galaxies%04d_C6.h5"%(i))
# gals_cat = h5.File(file_path, 'r')['galaxies']
# for pix in self.pix_list:
# try:
# gals = gals_cat[str(pix)]
# self._load_gals(gals, pix_id=pix, cat_id=i)
# del gals
# except Exception as e:
# traceback.print_exc()
# self.logger.error(str(e))
# print(e)
for pix in self.pix_list:
try:
bundleID = get_bundleIndex(pix)
# # (TEST C6):
# if pix != 35421 or bundleID != 523:
# continue
gals_cat = h5.File(self.galaxy_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
#if "AGN_cat" in self.config["input_path"] and self.config["input_path"]["AGN_cat"] and not self.config["run_option"]["star_only"]:
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and self.config["catalog_options"]["galaxy_yes"]:
try:
self._load_AGNs()
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))
# (TEST)
# def convert_mags(self, obj):
# spec = np.matmul(obj.coeff, self.pcs.T)
# lamb = self.lamb_gal * U.angstrom
# unit_sed = ((lamb * lamb)/constants.c*U.erg/U.second/U.cm**2/U.angstrom).to(U.jansky)
# unit_sed = unit_sed.value
# lamb = lamb.value
# lamb *= (1 + obj.z)
# spec *= (1 + obj.z)
# mags = -2.5 * np.log10(unit_sed*spec) + 8.9
# mags += self.cosmo.distmod(obj.z).value
# return lamb, mags
def load_sed(self, obj, **kwargs):
if obj.type == 'galaxy' or obj.type == 'quasar':
# dist_L_pc = (1 + obj.z) * comoving_dist(z=obj.z)[0]
# factor = (10 / dist_L_pc)**2
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.SED_AGN[int(obj.id)] * factor * 1e-17
flux = self.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
Catalog/testCat_star.py deleted 100644 → 0
View file @ 50fd7c8d
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
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_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]))
# 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)
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)
# 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
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
----------
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
Catalog/wcs_test_C6.py deleted 100644 → 0
View file @ 50fd7c8d
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
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
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
NSIDE = 128
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
class Catalog(CatalogBase):
def __init__(self, config, chip, pointing, chip_output, filt, **kwargs):
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 not config["catalog_options"]["galaxy_only"]:
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)
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 = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "AGN_cat" in config["catalog_options"]["input_path"] and config["catalog_options"]["input_path"]["AGN_cat"] and not config["catalog_options"]["star_only"]:
AGN_dir = config["catalog_options"]["input_path"]["AGN_cat"]
self.AGN_path = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["AGN_cat"])
self.AGN_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["AGN_SED"])
self.AGN_SED_wave_path = os.path.join(config['data_dir'], config["catalog_options"]["SED_templates_path"]["AGN_SED_WAVE"])
self._load_SED_lib_AGN()
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]))
# vertices = spherical_to_cartesian(1., dec, ra)
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_SED_lib_AGN(self):
from astropy.io import fits
self.SED_AGN = fits.open(self.AGN_SED_path)[0].data
self.lamb_AGN = np.load(self.AGN_SED_wave_path)
def _load_gals(self, gals, pix_id=None, cat_id=0):
ngals = len(gals['ra'])
# Apply astrometric modeling
# in C3 case only aberration
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]
param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals]
# param['mag_use_normal'] = 20.
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['g1'] = 0.
param['g2'] = 0.
param['kappa'] = gals['kappa'][igals]
# param['e1'] = gals['ellipticity_true'][igals][0]
# param['e2'] = gals['ellipticity_true'][igals][1]
param['e1'] = 0.
param['e2'] = 0.
# For shape calculation
param['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
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
param['star'] = 0 # Galaxy
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, logger=self.logger)
# Need to deal with additional output columns
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]
param['mag_use_normal'] = 20.
# 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_AGNs(self):
data = Table.read(self.AGN_path)
ra_arr = data['ra']
dec_arr = data['dec']
nAGNs = len(data)
if self.config["obs_setting"]["enable_astrometric_model"]:
ra_list = ra_arr.tolist()
dec_list = dec_arr.tolist()
pmra_list = np.zeros(nAGNs).tolist()
pmdec_list = np.zeros(nAGNs).tolist()
rv_list = np.zeros(nAGNs).tolist()
parallax_list = [1e-9] * nAGNs
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=nAGNs,
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 iAGNs in range(nAGNs):
param = self.initialize_param()
param['ra'] = ra_arr[iAGNs]
param['dec'] = dec_arr[iAGNs]
param['ra_orig'] = data['ra'][iAGNs]
param['dec_orig'] = data['dec'][iAGNs]
param['z'] = data['z'][iAGNs]
param['appMag'] = data['appMag'][iAGNs]
param['absMag'] = data['absMag'][iAGNs]
# NOTE: this cut cannot be put before the SED type has been assigned
if not self.chip.isContainObj(ra_obj=param['ra'], dec_obj=param['dec'], margin=200):
continue
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 2 # Quasar
param['id'] = data['igmlos'][iAGNs]
if param['star'] == 2:
obj = Quasar(param, logger=self.logger)
# Append additional output columns to the .cat file
obj.additional_output_str = self.add_fmt%("n", 0., 0., 0.,
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)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
self._load_gals(gals, pix_id=pix, cat_id=bundleID)
del gals
except Exception as e:
traceback.print_exc()
self.logger.error(str(e))
print(e)
if "AGN_cat" in self.config["catalog_options"]["input_path"] and self.config["catalog_options"]["input_path"]["AGN_cat"] and not self.config["catalog_options"]["star_only"]:
try:
self._load_AGNs()
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.SED_AGN[int(obj.id)] * 1e-17
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
# sedcat = np.vstack((self.lamb_AGN, flux)).T
wave = self.lamb_AGN
# print("sed (erg/s/cm2/A) = ", sed_data)
# np.savetxt(os.path.join(self.config["work_dir"], "%s_sed.txt"%(obj.id)), sedcat)
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)
# if obj.param['mag_use_normal'] >= 30:
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# np.savetxt('./AGN_SED_test/sed_objID_%d.txt'%obj.id, np.transpose([self.lamb_AGN, self.SED_AGN[int(obj.id)]]))
# print("obj ID = %d"%obj.id)
# print("mag_use_normal = %.3f"%obj.param['mag_use_normal'])
# print("integrated flux = %.7f"%(interFlux))
# print("app mag = %.3f"%obj.param['appMag'])
# print("abs mag = %.3f"%obj.param['absMag'])
# mag = getABMAG(interFlux, self.filt.bandpass_full)
# print("mag diff = %.3f"%(mag - obj.param['mag_use_normal']))
del wave
del flux
return sed
config/config_50sqdeg.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/home/weichengliang/CSST_git/test_new_sim/outputs/"
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "testRun0"
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: ""
# star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
star_cat: "starcat/"
galaxy_cat: "qsocat/cat2CSSTSim_bundle-50sqDeg/"
# AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "qsocat/qsosed/"
# AGN_SED_WAVE: "wave_ross13.npy"
# Only simulate stars?
star_only: YES
# Only simulate galaxies?
galaxy_only: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
survey_type: "Photometric"
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_50_combined.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [8]
# Whether to enable astrometric modeling
enable_astrometric_model: True
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
mag_sat_margin: -2.5
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: ON # Whether to add field distortions
add_back: ON # Whether to add sky background
add_dark: ON # Whether to add dark noise
add_readout: ON # Whether to add read-out (Gaussian) noise
add_bias: ON # Whether to add bias-level to images
add_prescan: OFF
bias_16channel: ON # Whether to add different biases for 16 channels
gain_16channel: ON # Whether to make different gains for 16 channels
shutter_effect: ON # Whether to add shutter effect
flat_fielding: ON # Whether to add flat-fielding effect
prnu_effect: ON # Whether to add PRNU effect
non_linear: ON # Whether to add non-linearity
cosmic_ray: ON # Whether to add cosmic-ray
cray_differ: ON # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: OFF # Whether to simulate CTE trails
saturbloom: ON # Whether to simulate Saturation & Blooming
add_badcolumns: ON # Whether to add bad columns
add_hotpixels: ON # Whether to add hot pixels
add_deadpixels: ON # Whether to add dead(dark) pixels
bright_fatter: ON # Whether to simulate Brighter-Fatter (also diffusion) effect
format_output: OFF
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
config/config_C6.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/home/fangyuedong/new_sim/workplace/"
# work_dir: "/share/C6_new_sim_2sq"
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "C6_new_sim_2sq_run1"
project_cycle: 6
run_counter: 1
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_C6_20221212"
star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
galaxy_cat: "cat2CSSTSim_bundle/"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
# Only simulate stars?
star_only: YES
# Only simulate galaxies?
galaxy_only: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
survey_type: "Photometric"
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [8]
# Whether to enable astrometric modeling
enable_astrometric_model: True
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
# mag_sat_margin: -2.5
mag_sat_margin: -15.
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
psf_sls_dir: "/share/simudata/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: YES # Whether to add field distortions
add_back: YES # Whether to add sky background
add_dark: YES # Whether to add dark noise
add_readout: YES # Whether to add read-out (Gaussian) noise
add_bias: YES # Whether to add bias-level to images
bias_16channel: YES # Whether to add different biases for 16 channels
gain_16channel: YES # Whether to make different gains for 16 channels
shutter_effect: YES # Whether to add shutter effect
flat_fielding: YES # Whether to add flat-fielding effect
prnu_effect: YES # Whether to add PRNU effect
non_linear: YES # Whether to add non-linearity
cosmic_ray: YES # Whether to add cosmic-ray
cray_differ: YES # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: YES # Whether to simulate CTE trails
saturbloom: YES # Whether to simulate Saturation & Blooming
add_badcolumns: YES # Whether to add bad columns
add_hotpixels: YES # Whether to add hot pixels
add_deadpixels: YES # Whether to add dead(dark) pixels
bright_fatter: YES # Whether to simulate Brighter-Fatter (also diffusion) effect
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
config/config_C6_dev.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/home/fangyuedong/20231211/workplace/"
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "test_spec"
project_cycle: 6
run_counter: 1
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_C6_20221212"
star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
galaxy_cat: "cat2CSSTSim_bundle/"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
# Only simulate stars?
star_only: NO
# Only simulate galaxies?
galaxy_only: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
# "CALIBRATION": falt, bias, dark with or without postflash
survey_type: "Spectroscopic"
#"LED": ['LED1','LED2','LED3','LED4','LED5','LED6','LED7','LED8','LED9','LED10','LED11','LED12','LED13','LED14'] or null
#'LED1': '275', 'LED2': '310', 'LED3': '430', 'LED4': '505', 'LED5': '545', 'LED6': '590', 'LED7': '670',
#'LED8': '760', 'LED9': '940', 'LED10': '940', 'LED11': '1050', 'LED12': '1050','LED13': '340', 'LED14': '365'
LED_TYPE: ['LED5']
LED_TIME: [1.]
# unit e- ,flat level
FLAT_LEVEL: 20000
FLAT_LEVEL_FIL: 'g'
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [1]
# Whether to enable astrometric modeling
enable_astrometric_model: True
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
# mag_sat_margin: -2.5
mag_sat_margin: -15.
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
psf_sls_dir: "/share/simudata/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: YES # Whether to add field distortions
add_back: YES # Whether to add sky background
add_dark: YES # Whether to add dark noise
add_readout: YES # Whether to add read-out (Gaussian) noise
add_bias: YES # Whether to add bias-level to images
add_prescan: YES
bias_16channel: YES # Whether to add different biases for 16 channels
gain_16channel: YES # Whether to make different gains for 16 channels
shutter_effect: YES # Whether to add shutter effect
flat_fielding: YES # Whether to add flat-fielding effect
prnu_effect: YES # Whether to add PRNU effect
non_linear: YES # Whether to add non-linearity
cosmic_ray: YES # Whether to add cosmic-ray
cray_differ: YES # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: YES # Whether to simulate CTE trails, CTI_lgl_v0.3.tar.gz
saturbloom: YES # Whether to simulate Saturation & Blooming
add_badcolumns: YES # Whether to add bad columns
add_hotpixels: YES # Whether to add hot pixels
add_deadpixels: YES # Whether to add dead(dark) pixels
bright_fatter: YES # Whether to simulate Brighter-Fatter (also diffusion) effect
add_prescan: YES # Whether to add pre/over-scan
format_output: YES ##1*16 output
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
config/config_C6_fits.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/simudata/CSSOSDataProductsSims/data/CSSTSimImage_C8/"
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "testRun_FGS"
project_cycle: 6
run_counter: 1
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
#output PSF
out_psf: YES
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_C6_20221212"
star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
galaxy_cat: "cat2CSSTSim_bundle/"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
stamp_cat: "stampCatsIndex.hdf5"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
#stamp_SED:
# Only simulate stars?
star_only: YES
# Only simulate galaxies?
galaxy_only: NO
# With stamp?
stamp_yes: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
# "CALIBRATION": falt, bias, dark with or without postflash
survey_type: "All"
# "LED": ['LED1','LED2','LED3','LED4','LED5','LED6','LED7','LED8','LED9','LED10','LED11','LED12','LED13','LED14'] or null
# 'LED1': '275', 'LED2': '310', 'LED3': '430', 'LED4': '505', 'LED5': '545', 'LED6': '590', 'LED7': '670',
# 'LED8': '760', 'LED9': '940', 'LED10': '940', 'LED11': '1050', 'LED12': '1050','LED13': '340', 'LED14': '365'
# LED_TYPE: ['LED5']
# LED_TIME: [1.]
# # unit e- ,flat level
# FLAT_LEVEL: 20000
# FLAT_LEVEL_FIL: 'g'
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [39,40,41,42]
# Whether to enable astrometric modeling
enable_astrometric_model: True
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
# mag_sat_margin: -2.5
mag_sat_margin: -15.
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
psf_sls_dir: "/share/simudata/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: YES # Whether to add field distortions
add_back: YES # Whether to add sky background
add_dark: YES # Whether to add dark noise
add_readout: YES # Whether to add read-out (Gaussian) noise
add_bias: YES # Whether to add bias-level to images
bias_16channel: YES # Whether to add different biases for 16 channels
gain_16channel: YES # Whether to make different gains for 16 channels
shutter_effect: NO # Whether to add shutter effect
flat_fielding: YES # Whether to add flat-fielding effect
prnu_effect: YES # Whether to add PRNU effect
non_linear: YES # Whether to add non-linearity
cosmic_ray: YES # Whether to add cosmic-ray
cray_differ: YES # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: NO # Whether to simulate CTE trails, CTI_lgl_v0.3.tar.gz
saturbloom: YES # Whether to simulate Saturation & Blooming
add_badcolumns: YES # Whether to add bad columns
add_hotpixels: YES # Whether to add hot pixels
add_deadpixels: YES # Whether to add dead(dark) pixels
bright_fatter: YES # Whether to simulate Brighter-Fatter (also diffusion) effect
add_prescan: NO # Whether to add pre/over-scan
format_output: NO ##1*16 output
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 0.01 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 2000 # bias level [e-/pixel]
# gain: 1. # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
config/config_ooc_c6.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/25
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/home/fangyuedong/20231211/workplace/"
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
run_name: "test20231218_c6_onlyCat"
project_cycle: 6
run_counter: 1
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: YES
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_C6_20221212"
star_cat: "C6_MMW_GGC_Astrometry_healpix.hdf5"
galaxy_cat: "cat2CSSTSim_bundle/"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Catalog_C6_20221212/sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
# Only simulate stars?
star_only: YES
# Only simulate galaxies?
galaxy_only: NO
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
# "CALIBRATION": falt, bias, dark with or without postflash
survey_type: "Photometric"
#"LED": ['LED1','LED2','LED3','LED4','LED5','LED6','LED7','LED8','LED9','LED10','LED11','LED12','LED13','LED14'] or null
#'LED1': '275', 'LED2': '310', 'LED3': '430', 'LED4': '505', 'LED5': '545', 'LED6': '590', 'LED7': '670',
#'LED8': '760', 'LED9': '940', 'LED10': '940', 'LED11': '1050', 'LED12': '1050','LED13': '340', 'LED14': '365'
LED_TYPE: ['LED5']
LED_TIME: [1.]
# unit e- ,flat level
FLAT_LEVEL: 20000
FLAT_LEVEL_FIL: 'g'
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 192.8595
dec_center: 27.1283
# Image rotation [degree]
image_rot: -113.4333
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: "/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: "pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [8]
# Whether to enable astrometric modeling
enable_astrometric_model: False
# Whether to enable straylight model
enable_straylight_model: True
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
mag_sat_margin: -2.5
# mag_sat_margin: -15.
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
psf_sls_dir: "/share/simudata/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "constant"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: YES # Whether to add field distortions
add_back: YES # Whether to add sky background
add_dark: YES # Whether to add dark noise
add_readout: YES # Whether to add read-out (Gaussian) noise
add_bias: YES # Whether to add bias-level to images
bias_16channel: YES # Whether to add different biases for 16 channels
gain_16channel: YES # Whether to make different gains for 16 channels
shutter_effect: YES # Whether to add shutter effect
flat_fielding: YES # Whether to add flat-fielding effect
prnu_effect: YES # Whether to add PRNU effect
non_linear: YES # Whether to add non-linearity
cosmic_ray: YES # Whether to add cosmic-ray
cray_differ: YES # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: YES # Whether to simulate CTE trails, CTI_lgl_v0.3.tar.gz
saturbloom: YES # Whether to simulate Saturation & Blooming
add_badcolumns: YES # Whether to add bad columns
add_hotpixels: YES # Whether to add hot pixels
add_deadpixels: YES # Whether to add dead(dark) pixels
bright_fatter: YES # Whether to simulate Brighter-Fatter (also diffusion) effect
add_prescan: YES # Whether to add pre/over-scan
format_output: YES ##1*16 output
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
config/config_overall.yaml
View file @ dd26d370
......@@ -32,12 +32,10 @@ run_option:
catalog_options:
input_path:
cat_dir: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/"
# star_cat: "starcat/"
star_cat: "starcat_C9/"
galaxy_cat: "qsocat/cat2CSSTSim_bundle-50sqDeg/"
SED_templates_path:
# star_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/SpecLib.hdf5"
star_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/starcat_C9/"
galaxy_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/sedlibs/"
AGN_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/qsocat/qsosed/"
......@@ -59,10 +57,9 @@ obs_setting:
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
# pointing_file: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/pointing50_C9/pointing_50_1_n.dat"
pointing_file: "/public/home/fangyuedong/project/unit_test_data/pointing_for_test.dat"
pointing_file: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/pointing50_C9/pointing_50_1_n.dat"
obs_config_file: "/public/home/fangyuedong/project/csst_msc_sim/config/obs_config_SCI_WIDE_phot.yaml"
obs_config_file: "/public/home/fangyuedong/project/csst_msc_sim/config/obs_config_SCI.yaml"
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
......
config/config_overall_newStar.yaml → config/config_overall_fits_stamp.yaml
View file @ dd26d370
......@@ -10,8 +10,8 @@
# Base diretories and naming setup
# can add some of the command-line arguments here as well;
# ok to pass either way or both, as long as they are consistent
work_dir: "/home/zhangxin/CSST_SIM/CSST_sim_scheduler/"
run_name: "QSO_50sqdeg_test"
work_dir: "/public/share/yangxuliu/CSSOSDataProductsSims/outputs_C9Tests/"
run_name: "testRun0"
# Project cycle and run counter are used to name the outputs
project_cycle: 9
......@@ -31,21 +31,26 @@ run_option:
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "/nfsdata/share/CSSOSDataProductsSims/data/Catalog_C6_20221212/"
star_cat: "star_catalog/"
cat_dir: "/public/home/chengliang/CSSOSDataProductsSims/testCats/"
# star_cat: "star_catalog/"
# galaxy_cat: "qsocat/cat2CSSTSim_bundle-50sqDeg/"
stamp_cat: "stampCatsIndex.hdf5"
SED_templates_path:
star_SED: "/nfsdata/share/CSSOSDataProductsSims/data/Catalog_C6_20221212/star_catalog/"
# star_SED:
# galaxy_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/sedlibs/"
# AGN_SED: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/qsocat/qsosed/"
# stamp_SED:
# Only simulate stars?
star_only: YES
star_only: NO
# Only simulate galaxies?
galaxy_only: NO
# With stamp?
stamp_yes: YES
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
......@@ -57,18 +62,18 @@ obs_setting:
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_file: "/nfsdata/share/CSSOSDataProductsSims/data/pointing_50_1_n.dat"
pointing_file: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/pointing50_C9/pointing_50_1_n.dat"
obs_config_file: "/home/zhangxin/CSST_SIM/CSST_sim_scheduler/csst-simulation/config/obs_config_SCI_WIDE_phot.yaml"
obs_config_file: "/public/home/chengliang/CSSOSDataProductsSims/csst_msc_sim/config/obs_config_SCI.yaml"
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [1]
run_pointings: [0]
# Whether to enable astrometric modeling
enable_astrometric_model: NO
enable_astrometric_model: YES
# Cut by saturation magnitude in which band?
cut_in_band: "z"
......@@ -98,9 +103,9 @@ psf_setting:
# path to PSF data
# NOTE: only valid for "Interp" PSF
# PSF models for photometry survey simulation
psf_pho_dir: "/nfsdata/share/CSSOSDataProductsSims/data/psfCube1"
psf_pho_dir: "/public/share/yangxuliu/CSSOSDataProductsSims/psfCube/set1_dynamic/"
# PSF models for slitless spectrum survey simulation
psf_sls_dir: "/nfsdata/share/CSSOSDataProductsSims/data/SLS_PSF_PCA_fp/"
psf_sls_dir: "/public/share/yangxuliu/CSSOSDataProductsSims/data_50sqDeg/SLS_PSF_PCA_fp/"
###############################################
# Shear setting
......@@ -138,4 +143,4 @@ random_seeds:
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
\ No newline at end of file
...
config/config_testCASE.yaml deleted 100644 → 0
View file @ 50fd7c8d
---
###############################################
#
# Configuration file for CSST simulation
# CSST-Sim Group, 2023/04/30
#
###############################################
# Base diretories and naming setup
# Can add some of the command-line arguments here as well;
# OK to pass either way or both, as long as they are consistent
work_dir: "/share/simudata/CSSOSDataProductsSims/data/CSSTSimImage_C6/"
data_dir: "/share/home/weichengliang/CSST_git/csst-simulation/tools/"
run_name: "C6_fits_testRun"
# Whether to use MPI
run_option:
# NOTE: "n_threads" paramters is currently not used in the backend
# simulation codes. It should be implemented later in the web frontend
# in order to config the number of threads to request from NAOC cluster
n_threads: 80
# Output catalog only?
# If yes, no imaging simulation will run
out_cat_only: NO
###############################################
# Catalog setting
###############################################
# Configure your catalog: options to be implemented
# in the corresponding (user defined) 'Catalog' class
catalog_options:
input_path:
cat_dir: "Catalog_test"
star_cat: "testStarCat.h5"
galaxy_cat: "testGalaxyCat.h5"
AGN_cat: "AGN_C6_ross13_rand_pos_rmax-1.3.fits"
stamp_cat: "stampCatsIndex.hdf5"
SED_templates_path:
star_SED: "SpecLib.hdf5"
galaxy_SED: "sedlibs/"
AGN_SED: "quickspeclib_ross13.fits"
AGN_SED_WAVE: "wave_ross13.npy"
#stamp_SED:
# simulate stars?
star_yes: NO
# simulate galaxies?
galaxy_yes: NO
# With stamp?
stamp_yes: YES
# rotate galaxy ellipticity
rotateEll: 0. # [degree]
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Observation setting
###############################################
obs_setting:
# Options for survey types:
# "Photometric": simulate photometric chips only
# "Spectroscopic": simulate slitless spectroscopic chips only
# "FGS": simulate FGS chips only (31-42)
# "All": simulate full focal plane
survey_type: "Photometric"
# Exposure time [seconds]
exp_time: 150.
# Observation starting date & time
date_obs: "210525" # [yymmdd]
time_obs: "120000" # [hhmmss]
# Default Pointing [degrees]
# Note: NOT valid when a pointing list file is specified
ra_center: 60.11828178499743 #244.972773
dec_center: -39.75897455697294 #39.895901
# Image rotation [degree]
image_rot: 112.23685624012192 #109.59
# (Optional) a file of point list
# if you just want to run default pointing:
# - pointing_dir: null
# - pointing_file: null
pointing_dir: null #"/share/simudata/CSSOSDataProductsSims/data/"
pointing_file: null #"pointing_radec_246.5_40.dat"
# Number of calibration pointings
np_cal: 0
# Run specific pointing(s):
# - give a list of indexes of pointings: [ip_1, ip_2...]
# - run all pointings: null
# Note: only valid when a pointing list is specified
run_pointings: [0]
# Run specific chip(s):
# - give a list of indexes of chips: [ip_1, ip_2...]
# - run all chips: null
# Note: for all pointings
run_chips: [7, 8, 9]
# Whether to enable astrometric modeling
enable_astrometric_model: False
# Cut by saturation magnitude in which band?
cut_in_band: "z"
# saturation magnitude margin
mag_sat_margin: -2.5
# limiting magnitude margin
mag_lim_margin: +1.0
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
###############################################
# Shear setting
###############################################
shear_setting:
# Options to generate mock shear field:
# "constant": all galaxies are assigned a constant reduced shear
# "catalog": from catalog
shear_type: "catalog"
# For constant shear filed
reduced_g1: 0.
reduced_g2: 0.
###############################################
# Instrumental effects setting
###############################################
ins_effects:
# switches
# Note: bias_16channel, gain_16channel, and shutter_effect
# is currently not applicable to "FGS" observations
field_dist: ON # Whether to add field distortions
add_back: OFF # Whether to add sky background
add_dark: OFF # Whether to add dark noise
add_readout: OFF # Whether to add read-out (Gaussian) noise
add_bias: OFF # Whether to add bias-level to images
bias_16channel: OFF # Whether to add different biases for 16 channels
gain_16channel: OFF # Whether to make different gains for 16 channels
shutter_effect: OFF # Whether to add shutter effect
flat_fielding: OFF # Whether to add flat-fielding effect
prnu_effect: OFF # Whether to add PRNU effect
non_linear: OFF # Whether to add non-linearity
cosmic_ray: OFF # Whether to add cosmic-ray
cray_differ: OFF # Whether to generate different cosmic ray maps CAL and MS output
cte_trail: OFF # Whether to simulate CTE trails
saturbloom: OFF # Whether to simulate Saturation & Blooming
add_badcolumns: OFF # Whether to add bad columns
add_hotpixels: OFF # Whether to add hot pixels
add_deadpixels: OFF # Whether to add dead(dark) pixels
bright_fatter: OFF # Whether to simulate Brighter-Fatter (also diffusion) effect
# Values:
# default values have been defined individually for each chip in:
# ObservationSim/Instrument/data/ccd/chip_definition.json
# Set them here will override the default values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 0.01 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 2000 # bias level [e-/pixel]
# gain: 1. # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Output options (for calibration pointings only)
###############################################
output_setting:
readout16: OFF # Whether to export as 16 channels (subimages) with pre- and over-scan
shutter_output: OFF # Whether to export shutter effect 16-bit image
bias_output: ON # Whether to export bias frames
dark_output: ON # Whether to export the combined dark current files
flat_output: ON # Whether to export the combined flat-fielding files
prnu_output: OFF # Whether to export the PRNU (pixel-to-pixel flat-fielding) files
NBias: 1 # Number of bias frames to be exported for each exposure
NDark: 1 # Number of dark frames to be exported for each exposure
NFlat: 1 # Number of flat frames to be exported for each exposure
###############################################
# Random seeds
###############################################
random_seeds:
seed_poisson: 20210601 # Seed for Poisson noise
seed_CR: 20210317 # Seed for generating random cosmic ray maps
seed_flat: 20210101 # Seed for generating random flat fields
seed_prnu: 20210102 # Seed for photo-response non-uniformity
seed_gainNonUniform: 20210202 # Seed for gain nonuniformity
seed_biasNonUniform: 20210203 # Seed for bias nonuniformity
seed_rnNonUniform: 20210204 # Seed for readout-noise nonuniformity
seed_badcolumns: 20240309 # Seed for bad columns
seed_defective: 20210304 # Seed for defective (bad) pixels
seed_readout: 20210601 # Seed for read-out gaussian noise
...
\ No newline at end of file
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment