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Commit f1dab347 authored by Fang Yuedong's avatar Fang Yuedong
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Merge branch 'new_sim' of https://csst-tb.bao.ac.cn/code/csst_sim/csst-simulation into new_sim

parents 3321919c 20067476
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ObservationSim/Config/Config.py
View file @ f1dab347
......@@ -20,6 +20,7 @@ def config_dir(config, work_dir=None, data_dir=None):
# PSF data directory
if config["psf_setting"]["psf_model"] == "Interp":
path_dict["psf_dir"] = os.path.join(path_dict["data_dir"], config["psf_setting"]["psf_dir"])
path_dict["psf_sls_dir"] = os.path.join(path_dict["data_dir"], config["psf_setting"]["psf_sls_dir"])
return path_dict
......
ObservationSim/Instrument/Chip/ChipUtils.py
View file @ f1dab347
......@@ -21,6 +21,22 @@ def log_info(msg, logger=None):
else:
print(msg, flush=True)
def getChipSLSGratingID(chipID):
gratingID = ['','']
if chipID == 1: gratingID = ['GI2', 'GI1']
if chipID == 2: gratingID = ['GV4', 'GV3']
if chipID == 3: gratingID = ['GU2', 'GU1']
if chipID == 4: gratingID = ['GU4', 'GU3']
if chipID == 5: gratingID = ['GV2', 'GV1']
if chipID == 10: gratingID = ['GI4', 'GI3']
if chipID == 21: gratingID = ['GI6', 'GI5']
if chipID == 26: gratingID = ['GV8', 'GV7']
if chipID == 27: gratingID = ['GU6', 'GU5']
if chipID == 28: gratingID = ['GU8', 'GU7']
if chipID == 29: gratingID = ['GV6', 'GV5']
if chipID == 30: gratingID = ['GI8', 'GI7']
return gratingID
def getChipSLSConf(chipID):
confFile = ''
if chipID == 1: confFile = ['CSST_GI2.conf', 'CSST_GI1.conf']
......
ObservationSim/MockObject/Galaxy.py
View file @ f1dab347
......@@ -248,10 +248,27 @@ class Galaxy(MockObject):
xOrderSigPlus = {'A':1.3909419820029296,'B':1.4760376591236062,'C':4.035447379743442,'D':5.5684364343742825,'E':16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list), 2])
# print(hasattr(psf_model, 'bandranges'))
if hasattr(psf_model, 'bandranges'):
if psf_model.bandranges is None:
return 2, None
if len(psf_model.bandranges) != len(bandpass_list):
return 2, None
branges = psf_model.bandranges
else:
for i in range(len(bandpass_list)):
bandpass = bandpass_list[i]
branges[i, 0] = bandpass_list[i].blue_limit * 10
branges[i, 1] = bandpass_list[i].red_limit * 10
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
for i in range(len(bandpass_list)):
# bandpass = bandpass_list[i]
brange = branges[i]
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
disk = galsim.Sersic(n=self.disk_sersic_idx, half_light_radius=self.hlr_disk, flux=1.0, gsparams=gsp)
disk_shape = galsim.Shear(g1=self.e1_disk, g2=self.e2_disk)
disk = disk.shear(disk_shape)
......@@ -272,14 +289,14 @@ class Galaxy(MockObject):
g2 += fd_shear.g2
gal_shear = galsim.Shear(g1=g1, g2=g2)
gal = gal.shear(gal_shear)
gal = galsim.Convolve(psf, gal)
# gal = galsim.Convolve(psf, gal)
if not big_galaxy: # Not apply PSF for very big galaxy
gal = galsim.Convolve(psf, gal)
# if fd_shear is not None:
# gal = gal.shear(fd_shear)
# if not big_galaxy: # Not apply PSF for very big galaxy
# gal = galsim.Convolve(psf, gal)
# # if fd_shear is not None:
# # gal = gal.shear(fd_shear)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset,method = 'real_space')
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
......@@ -301,12 +318,16 @@ class Galaxy(MockObject):
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local=[xcenter_p1, ycenter_p1],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
subImg_p2 = starImg.array[:, subSlitPos+1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
......@@ -318,12 +339,16 @@ class Galaxy(MockObject):
sdp_p2 = SpecDisperser(orig_img=star_p2, xcenter=xcenter_p2,
ycenter=ycenter_p2, origin=origin_p2,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp_p1
del sdp_p2
......@@ -331,25 +356,33 @@ class Galaxy(MockObject):
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
elif grating_split_pos_chip>=gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
# print(self.y_nominal, starImg.center.y, starImg.ymin)
del psf
# del psf
return 1, pos_shear
def getGSObj(self, psf, g1=0, g2=0, flux=None, filt=None, tel=None, exptime=150.):
......
ObservationSim/MockObject/MockObject.py
View file @ f1dab347
......@@ -4,7 +4,7 @@ import astropy.constants as cons
from astropy import wcs
from astropy.table import Table
from ObservationSim.MockObject._util import magToFlux, VC_A, convolveGaussXorders
from ObservationSim.MockObject._util import magToFlux, VC_A, convolveGaussXorders, convolveImg
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, \
getABMAG
from ObservationSim.MockObject.SpecDisperser import SpecDisperser
......@@ -223,9 +223,69 @@ class MockObject(object):
del stamp
del spec_orders
def addSLStoChipImageWithPSF(self, sdp=None, chip=None, pos_img_local = [1,1], psf_model=None, bandNo = 1, grating_split_pos=3685, local_wcs=None, pos_img=None):
spec_orders = sdp.compute_spec_orders()
for k, v in spec_orders.items():
img_s = v[0]
# print(bandNo,k)
try:
psf, pos_shear = psf_model.get_PSF(chip, pos_img_local = pos_img_local, bandNo = bandNo, galsimGSObject=True, g_order = k, grating_split_pos=grating_split_pos)
except:
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img)
psf_img = psf.drawImage(nx=100, ny=100, wcs = local_wcs)
psf_img_m = psf_img.array
#########################################################
# DEBUG
#########################################################
# ids_p = psf_img_m < 0
# psf_img_m[ids_p] = 0
# from astropy.io import fits
# fits.writeto(str(bandNo) + '_' + str(k) + '_psf.fits', psf_img_m)
# print("DEBUG: orig_off is", orig_off)
nan_ids = np.isnan(img_s)
if img_s[nan_ids].shape[0] > 0:
img_s[nan_ids] = 0
print("DEBUG: specImg nan num is", img_s[nan_ids].shape[0])
#########################################################
img_s, orig_off = convolveImg(img_s, psf_img_m)
origin_order_x = v[1] - orig_off[0]
origin_order_y = v[2] - orig_off[1]
specImg = galsim.ImageF(img_s)
# photons = galsim.PhotonArray.makeFromImage(specImg)
# photons.x += origin_order_x
# photons.y += origin_order_y
# xlen_imf = int(specImg.xmax - specImg.xmin + 1)
# ylen_imf = int(specImg.ymax - specImg.ymin + 1)
# stamp = galsim.ImageF(xlen_imf, ylen_imf)
# stamp.wcs = local_wcs
# stamp.setOrigin(origin_order_x, origin_order_y)
specImg.wcs = local_wcs
specImg.setOrigin(origin_order_x, origin_order_y)
bounds = specImg.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() == 0:
continue
chip.img.setOrigin(0, 0)
chip.img[bounds] = chip.img[bounds] + specImg[bounds]
# stamp[bounds] = chip.img[bounds]
# # chip.sensor.accumulate(photons, stamp)
# chip.img[bounds] = stamp[bounds]
chip.img.setOrigin(chip.bound.xmin, chip.bound.ymin)
# del stamp
del spec_orders
return pos_shear
def drawObj_slitless(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., normFilter=None, grating_split_pos=3685, fd_shear=None):
if normFilter is not None:
norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001
sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed,
......@@ -263,22 +323,38 @@ class MockObject(object):
xOrderSigPlus = {'A': 1.3909419820029296, 'B': 1.4760376591236062, 'C': 4.035447379743442,
'D': 5.5684364343742825, 'E': 16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list),2])
if hasattr(psf_model,'bandranges'):
if psf_model.bandranges is None:
return 2, None
if len(psf_model.bandranges) != len(bandpass_list):
return 2, None
branges = psf_model.bandranges
else:
for i in range(len(bandpass_list)):
bandpass = bandpass_list[i]
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass,
folding_threshold=folding_threshold)
branges[i, 0] = bandpass_list[i].blue_limit * 10
branges[i, 1] = bandpass_list[i].red_limit * 10
for i in range(len(bandpass_list)):
# bandpass = bandpass_list[i]
brange = branges[i]
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass,
# folding_threshold=folding_threshold)
star = galsim.DeltaFunction(gsparams=gsp)
star = star.withFlux(tel.pupil_area * exptime)
star = galsim.Convolve(psf, star)
psf_tmp = galsim.Gaussian(sigma=0.002)
star = galsim.Convolve(psf_tmp, star)
starImg = star.drawImage(nx=100, ny=100, wcs=chip_wcs_local, offset=offset)
starImg = star.drawImage(nx=60, ny=60, wcs=chip_wcs_local, offset=offset)
origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0,0)
gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] - 1, origin_star[1] + starImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
## part img disperse
......@@ -293,12 +369,15 @@ class MockObject(object):
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local = [xcenter_p1,ycenter_p1],
psf_model=psf_model, bandNo = i+1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
subImg_p2 = starImg.array[:, subSlitPos + 1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2)
......@@ -310,12 +389,15 @@ class MockObject(object):
sdp_p2 = SpecDisperser(orig_img=star_p2, xcenter=xcenter_p2,
ycenter=ycenter_p2, origin=origin_p2,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp_p2, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp_p1
del sdp_p2
......@@ -323,23 +405,29 @@ class MockObject(object):
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[1],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED,
band_start=bandpass.blue_limit * 10, band_end=bandpass.red_limit * 10,
band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0],
isAlongY=0,
flat_cube=flat_cube)
self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
# self.addSLStoChipImage(sdp=sdp, chip=chip, xOrderSigPlus=xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear=self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img)
del sdp
del psf
# del psf
return 1, pos_shear
def SNRestimate(self, img_obj, flux, noise_level=0.0, seed=31415):
......
ObservationSim/MockObject/SpecDisperser/SpecDisperser.py
View file @ f1dab347
......@@ -155,7 +155,7 @@ class SpecDisperser(object):
sensitivity_beam = ysens
len_spec_x = len(dx)
len_spec_y = int(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0]) + 1)
len_spec_y = int(abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)
beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)
modelf = zeros(product(beam_sh), dtype=float)
......
ObservationSim/MockObject/_util.py
View file @ f1dab347
......@@ -571,4 +571,14 @@ def convolveGaussXorders(img=None, sigma = 1):
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
return convImg, offset
def convolveImg(img=None, psf = None):
from astropy.modeling.models import Gaussian2D
from scipy import signal
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
offset_x = int(psf.shape[1]/2. + 0.5) - 1
offset_y = int(psf.shape[0]/2. + 0.5) - 1
offset = [offset_x,offset_y]
return convImg, offset
ObservationSim/ObservationSim.py
View file @ f1dab347
......@@ -15,7 +15,7 @@ from ObservationSim.Config.Header import generatePrimaryHeader, generateExtensio
from ObservationSim.Instrument import Telescope, Filter, FilterParam, FocalPlane, Chip
from ObservationSim.Instrument.Chip import Effects
from ObservationSim.Straylight import calculateSkyMap_split_g
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
from ObservationSim._util import get_shear_field, makeSubDir_PointingList
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
......@@ -63,6 +63,9 @@ class Observation(object):
if self.config["psf_setting"]["psf_model"] == "Gauss":
psf_model = PSFGauss(chip=chip, psfRa=self.config["psf_setting"]["psf_rcont"])
elif self.config["psf_setting"]["psf_model"] == "Interp":
if chip.survey_type == "spectroscopic":
psf_model = PSFInterpSLS(chip, filt,PSF_data_prefix=self.path_dict["psf_sls_dir"])
else:
psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples, PSF_data_file=self.path_dict["psf_dir"])
else:
chip_output.Log_error("unrecognized PSF model type!!", flush=True)
......@@ -198,6 +201,10 @@ class Observation(object):
obj = self.cat.objs[j]
# (DEBUG)
# if obj.getMagFilter(filt)>20:
# continue
# load and convert SED; also caculate object's magnitude in all CSST bands
try:
sed_data = self.cat.load_sed(obj)
......
ObservationSim/PSF/PSFInterpSLS.py 0 → 100644
View file @ f1dab347
'''
PSF interpolation for CSST-Sim
NOTE: [iccd, iwave, ipsf] are counted from 1 to n, but [tccd, twave, tpsf] are counted from 0 to n-1
'''
import sys
import time
import copy
import numpy as np
import scipy.spatial as spatial
import galsim
import h5py
from ObservationSim.PSF.PSFModel import PSFModel
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
import os
from astropy.io import fits
from astropy.modeling.models import Gaussian2D
from scipy import signal
LOG_DEBUG = False #***#
NPSF = 900 #***# 30*30
PixSizeInMicrons = 5. #***# in microns
###find neighbors-KDtree###
# def findNeighbors(tx, ty, px, py, dr=0.1, dn=1, OnlyDistance=True):
# """
# find nearest neighbors by 2D-KDTree
#
# Parameters:
# tx, ty (float, float): a given position
# px, py (numpy.array, numpy.array): position data for tree
# dr (float-optional): distance
# dn (int-optional): nearest-N
# OnlyDistance (bool-optional): only use distance to find neighbors. Default: True
#
# Returns:
# dataq (numpy.array): index
# """
# datax = px
# datay = py
# tree = spatial.KDTree(list(zip(datax.ravel(), datay.ravel())))
#
# dataq=[]
# rr = dr
# if OnlyDistance == True:
# dataq = tree.query_ball_point([tx, ty], rr)
# if OnlyDistance == False:
# while len(dataq) < dn:
# dataq = tree.query_ball_point([tx, ty], rr)
# rr += dr
# dd = np.hypot(datax[dataq]-tx, datay[dataq]-ty)
# ddSortindx = np.argsort(dd)
# dataq = np.array(dataq)[ddSortindx[0:dn]]
# return dataq
#
# ###find neighbors-hoclist###
# def hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy):
# if np.max(partx) > nhocx*dhocx:
# print('ERROR')
# sys.exit()
# if np.max(party) > nhocy*dhocy:
# print('ERROR')
# sys.exit()
#
# npart = partx.size
# hoclist= np.zeros(npart, dtype=np.int32)-1
# hoc = np.zeros([nhocy, nhocx], dtype=np.int32)-1
# for ipart in range(npart):
# ix = int(partx[ipart]/dhocx)
# iy = int(party[ipart]/dhocy)
# hoclist[ipart] = hoc[iy, ix]
# hoc[iy, ix] = ipart
# return hoc, hoclist
#
# def hocFind(px, py, dhocx, dhocy, hoc, hoclist):
# ix = int(px/dhocx)
# iy = int(py/dhocy)
#
# neigh=[]
# it = hoc[iy, ix]
# while it != -1:
# neigh.append(it)
# it = hoclist[it]
# return neigh
#
# def findNeighbors_hoclist(px, py, tx=None,ty=None, dn=4, hoc=None, hoclist=None):
# nhocy = nhocx = 20
#
# pxMin = np.min(px)
# pxMax = np.max(px)
# pyMin = np.min(py)
# pyMax = np.max(py)
#
# dhocx = (pxMax - pxMin)/(nhocx-1)
# dhocy = (pyMax - pyMin)/(nhocy-1)
# partx = px - pxMin +dhocx/2
# party = py - pyMin +dhocy/2
#
# if hoc is None:
# hoc, hoclist = hocBuild(partx, party, nhocx, nhocy, dhocx, dhocy)
# return hoc, hoclist
#
# if hoc is not None:
# tx = tx - pxMin +dhocx/2
# ty = ty - pyMin +dhocy/2
# itx = int(tx/dhocx)
# ity = int(ty/dhocy)
#
# ps = [-1, 0, 1]
# neigh=[]
# for ii in range(3):
# for jj in range(3):
# ix = itx + ps[ii]
# iy = ity + ps[jj]
# if ix < 0:
# continue
# if iy < 0:
# continue
# if ix > nhocx-1:
# continue
# if iy > nhocy-1:
# continue
#
# #neightt = myUtil.hocFind(ppx, ppy, dhocx, dhocy, hoc, hoclist)
# it = hoc[iy, ix]
# while it != -1:
# neigh.append(it)
# it = hoclist[it]
# #neigh.append(neightt)
# #ll = [i for k in neigh for i in k]
# if dn != -1:
# ptx = np.array(partx[neigh])
# pty = np.array(party[neigh])
# dd = np.hypot(ptx-tx, pty-ty)
# idx = np.argsort(dd)
# neigh= np.array(neigh)[idx[0:dn]]
# return neigh
#
#
# ###PSF-IDW###
# def psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=None, hoclist=None, PSFCentroidWgt=False):
# """
# psf interpolation by IDW
#
# Parameters:
# px, py (float, float): position of the target
# PSFMat (numpy.array): image
# cen_col, cen_row (numpy.array, numpy.array): potions of the psf centers
# IDWindex (int-optional): the power index of IDW
# OnlyNeighbors (bool-optional): only neighbors are used for psf interpolation
#
# Returns:
# psfMaker (numpy.array)
# """
#
# minimum_psf_weight = 1e-8
# ref_col = px
# ref_row = py
#
# ngy, ngx = PSFMat[0, :, :].shape
# npsf = PSFMat[:, :, :].shape[0]
# psfWeight = np.zeros([npsf])
#
# if OnlyNeighbors == True:
# if hoc is None:
# neigh = findNeighbors(px, py, cen_col, cen_row, dr=5., dn=4, OnlyDistance=False)
# if hoc is not None:
# neigh = findNeighbors_hoclist(cen_col, cen_row, tx=px,ty=py, dn=4, hoc=hoc, hoclist=hoclist)
#
# neighFlag = np.zeros(npsf)
# neighFlag[neigh] = 1
#
# for ipsf in range(npsf):
# if OnlyNeighbors == True:
# if neighFlag[ipsf] != 1:
# continue
#
# dist = np.sqrt((ref_col - cen_col[ipsf])**2 + (ref_row - cen_row[ipsf])**2)
# if IDWindex == 1:
# psfWeight[ipsf] = dist
# if IDWindex == 2:
# psfWeight[ipsf] = dist**2
# if IDWindex == 3:
# psfWeight[ipsf] = dist**3
# if IDWindex == 4:
# psfWeight[ipsf] = dist**4
# psfWeight[ipsf] = max(psfWeight[ipsf], minimum_psf_weight)
# psfWeight[ipsf] = 1./psfWeight[ipsf]
# psfWeight /= np.sum(psfWeight)
#
# psfMaker = np.zeros([ngy, ngx], dtype=np.float32)
# for ipsf in range(npsf):
# if OnlyNeighbors == True:
# if neighFlag[ipsf] != 1:
# continue
#
# iPSFMat = PSFMat[ipsf, :, :].copy()
# ipsfWeight = psfWeight[ipsf]
#
# psfMaker += iPSFMat * ipsfWeight
# psfMaker /= np.nansum(psfMaker)
#
# return psfMaker
###define PSFInterp###
class PSFInterpSLS(PSFModel):
def __init__(self, chip, filt,PSF_data_prefix="", sigSpin=0, psfRa=0.15, pix_size = 0.005):
if LOG_DEBUG:
print('===================================================')
print('DEBUG: psf module for csstSim ' \
+time.strftime("(%Y-%m-%d %H:%M:%S)", time.localtime()), flush=True)
print('===================================================')
self.sigSpin = sigSpin
self.sigGauss = psfRa
self.grating_ids = chip_utils.getChipSLSGratingID(chip.chipID)
_,self.grating_type = chip.getChipFilter(chipID=chip.chipID)
self.data_folder = PSF_data_prefix
self.getPSFDataFromFile(filt)
self.pixsize = pix_size # um
def getPSFDataFromFile(self, filt):
gratingInwavelist = {'GU':0,'GV':1,'GI':2}
grating_orders = ['0','1']
waveListFn = self.data_folder + '/wavelist.dat'
wavelists = np.loadtxt(waveListFn)
self.waveList = wavelists[:,gratingInwavelist[self.grating_type]]
bandranges = np.zeros([4,2])
midBand = (self.waveList[0:3] + self.waveList[1:4])/2.*10000.
bandranges[0,0] = filt.blue_limit
bandranges[1:4,0] = midBand
bandranges[0:3, 1] = midBand
bandranges[3,1] = filt.red_limit
self.bandranges = bandranges
self.grating1_data = {}
g_folder = self.data_folder + '/' + self.grating_ids[0] + '/'
for g_order in grating_orders:
g_folder_order = g_folder + 'PSF_Order_' + g_order + '/'
grating_order_data = {}
for bandi in [1,2,3,4]:
subBand_data = {}
subBand_data['bandrange'] = bandranges[bandi-1]
final_folder = g_folder_order + str(bandi) + '/'
print(final_folder)
pca_fs = os.listdir(final_folder)
for fname in pca_fs:
if ('_PCs.fits' in fname) and (fname[0] != '.'):
fname_ = final_folder + fname
hdu = fits.open(fname_)
subBand_data['band_data'] = hdu
grating_order_data['band'+str(bandi)] = subBand_data
self.grating1_data['order'+g_order] = grating_order_data
self.grating2_data = {}
g_folder = self.data_folder + '/' + self.grating_ids[1] + '/'
for g_order in grating_orders:
g_folder_order = g_folder + 'PSF_Order_' + g_order + '/'
grating_order_data = {}
for bandi in [1, 2, 3, 4]:
subBand_data = {}
subBand_data['bandrange'] = bandranges[bandi - 1]
final_folder = g_folder_order + str(bandi) + '/'
print(final_folder)
pca_fs = os.listdir(final_folder)
for fname in pca_fs:
if ('_PCs.fits' in fname) and (fname[0] != '.'):
fname_ = final_folder + fname
hdu = fits.open(fname_)
subBand_data['band_data'] = hdu
grating_order_data['band' + str(bandi)] = subBand_data
self.grating2_data['order' + g_order] = grating_order_data
#
#
#
# def _getPSFwave(self, iccd, PSF_data_file, PSF_data_prefix):
# # fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_ccd{:}.h5'.format(iccd), 'r')
# fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_{:}.h5'.format(iccd), 'r')
# nwave = len(fq.keys())
# fq.close()
# return nwave
#
#
# def _loadPSF(self, iccd, PSF_data_file, PSF_data_prefix):
# psfSet = []
# # fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_ccd{:}.h5'.format(iccd), 'r')
# fq = h5py.File(PSF_data_file+'/' +PSF_data_prefix +'psfCube_{:}.h5'.format(iccd), 'r')
# for ii in range(self.nwave):
# iwave = ii+1
# psfWave = []
#
# fq_iwave = fq['w_{:}'.format(iwave)]
# for jj in range(self.npsf):
# ipsf = jj+1
# psfInfo = {}
# psfInfo['wavelength']= fq_iwave['wavelength'][()]
#
# fq_iwave_ipsf = fq_iwave['psf_{:}'.format(ipsf)]
# psfInfo['pixsize'] = PixSizeInMicrons
# psfInfo['field_x'] = fq_iwave_ipsf['field_x'][()]
# psfInfo['field_y'] = fq_iwave_ipsf['field_y'][()]
# psfInfo['image_x'] = fq_iwave_ipsf['image_x'][()]
# psfInfo['image_y'] = fq_iwave_ipsf['image_y'][()]
# psfInfo['centroid_x']= fq_iwave_ipsf['cx'][()]
# psfInfo['centroid_y']= fq_iwave_ipsf['cy'][()]
# psfInfo['psfMat'] = fq_iwave_ipsf['psfMat'][()]
#
# psfWave.append(psfInfo)
# psfSet.append(psfWave)
# fq.close()
#
# if LOG_DEBUG:
# print('psfSet has been loaded:', flush=True)
# print('psfSet[iwave][ipsf][keys]:', psfSet[0][0].keys(), flush=True)
# return psfSet
#
#
# def _findWave(self, bandpass):
# if isinstance(bandpass,int):
# twave = bandpass
# return twave
#
# for twave in range(self.nwave):
# bandwave = self.PSF_data[twave][0]['wavelength']
# if bandpass.blue_limit < bandwave and bandwave < bandpass.red_limit:
# return twave
# return -1
#
#
def convolveWithGauss(self, img=None, sigma=1):
offset = int(np.ceil(sigma * 3))
g_size = 2 * offset + 1
m_cen = int(g_size / 2)
print('-----',g_size)
g_PSF_ = Gaussian2D(1, m_cen, m_cen, sigma, sigma)
yp, xp = np.mgrid[0:g_size, 0:g_size]
g_PSF = g_PSF_(xp, yp)
psf = g_PSF / g_PSF.sum()
convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
convImg = convImg/np.sum(convImg)
return convImg
def get_PSF(self, chip, pos_img_local = [1000,1000], bandNo = 1, galsimGSObject=True, folding_threshold=5.e-3, g_order = 'A', grating_split_pos=3685):
"""
Get the PSF at a given image position
Parameters:
chip: A 'Chip' object representing the chip we want to extract PSF from.
pos_img: A 'galsim.Position' object representing the image position.
bandpass: A 'galsim.Bandpass' object representing the wavelength range.
pixSize: The pixels size of psf matrix
findNeighMode: 'treeFind' or 'hoclistFind'
Returns:
PSF: A 'galsim.GSObject'.
"""
order_IDs = {'A': '1', 'B': '0' ,'C': '0', 'D': '0', 'E': '0'}
contam_order_sigma = {'C':0.28032344707964174,'D':0.39900182912061344,'E':1.1988309797685412} #arcsec
x_start = chip.x_cen/chip.pix_size - chip.npix_x / 2.
y_start = chip.y_cen/chip.pix_size - chip.npix_y / 2.
# print(pos_img.x - x_start)
pos_img_x = pos_img_local[0] + x_start
pos_img_y = pos_img_local[1] + y_start
pos_img = galsim.PositionD(pos_img_x, pos_img_y)
if pos_img_local[0] < grating_split_pos:
psf_data = self.grating1_data
else:
psf_data = self.grating2_data
grating_order = order_IDs[g_order]
# if grating_order in ['-2','-1','2']:
# grating_order = '1'
# if grating_order in ['0', '1']:
psf_order = psf_data['order'+grating_order]
psf_order_b = psf_order['band'+str(bandNo)]
psf_b_dat = psf_order_b['band_data']
pos_p = psf_b_dat[1].data
pc_coeff = psf_b_dat[2].data
pcs = psf_b_dat[0].data
# print(max(pos_p[:,0]), min(pos_p[:,0]),max(pos_p[:,1]), min(pos_p[:,1]))
# print(chip.x_cen, chip.y_cen)
# print(pos_p)
px = pos_img.x*chip.pix_size
py = pos_img.y*chip.pix_size
dist2=(pos_p[:,1] - px)*(pos_p[:,1] - px) + (pos_p[:,0] - py)*(pos_p[:,0] - py)
temp_sort_dist = np.zeros([dist2.shape[0],2])
temp_sort_dist[:, 0] = np.arange(0, dist2.shape[0],1)
temp_sort_dist[:, 1] = dist2
# print(temp_sort_dist)
dits2_sortlist = sorted(temp_sort_dist, key=lambda x:x[1])
# print(dits2_sortlist)
nearest4p = np.zeros([4,2])
pc_coeff_4p = np.zeros([pc_coeff.data.shape[0],4])
for i in np.arange(4):
smaller_ids = int(dits2_sortlist[i][0])
nearest4p[i, 0] = pos_p[smaller_ids, 1]
nearest4p[i, 1] = pos_p[smaller_ids, 0]
pc_coeff_4p[:,i] = pc_coeff[:,smaller_ids]
idw_dist = 1/(np.sqrt((px-nearest4p[:,0]) * (px-nearest4p[:,0]) + (py-nearest4p[:,1]) * (py-nearest4p[:,1])))
coeff_int = np.zeros(pc_coeff.data.shape[0])
for i in np.arange(4):
coeff_int = coeff_int + pc_coeff_4p[:,i]*idw_dist[i]
coeff_int = coeff_int / np.sum(coeff_int)
npc = 10
m_size = int(pcs.shape[0]**0.5)
PSF_int = np.dot(pcs[:,0:npc],coeff_int[0:npc]).reshape(m_size,m_size)
# PSF_int = PSF_int/np.sum(PSF_int)
PSF_int_trans = np.flipud(np.fliplr(PSF_int))
PSF_int_trans = np.fliplr(PSF_int_trans.T)
# PSF_int_trans = np.abs(PSF_int_trans)
# ids_szero = PSF_int_trans<0
# PSF_int_trans[ids_szero] = 0
# print(PSF_int_trans[ids_szero].shape[0],PSF_int_trans.shape)
PSF_int_trans = PSF_int_trans/np.sum(PSF_int_trans)
# from astropy.io import fits
# fits.writeto(str(bandNo) + '_' + g_order+ '_psf_o.fits', PSF_int_trans)
# if g_order in ['C','D','E']:
# g_simgma = contam_order_sigma[g_order]/pixel_size_arc
# PSF_int_trans = self.convolveWithGauss(PSF_int_trans,g_simgma)
# n_m_size = int(m_size/2)
#
# n_PSF_int = np.zeros([n_m_size, n_m_size])
#
# for i in np.arange(n_m_size):
# for j in np.arange(n_m_size):
# n_PSF_int[i,j] = np.sum(PSF_int[2*i:2*i+2, 2*j:2*j+2])
#
# n_PSF_int = n_PSF_int/np.sum(n_PSF_int)
# chip.img = galsim.ImageF(chip.npix_x, chip.npix_y)
# chip.img.wcs = galsim.wcs.AffineTransform
if galsimGSObject:
# imPSFt = np.zeros([257,257])
# imPSFt[0:256, 0:256] = imPSF
# # imPSFt[120:130, 0:256] = 1.
pixel_size_arc = np.rad2deg(self.pixsize * 1e-3 / 28) * 3600
img = galsim.ImageF(PSF_int_trans, scale=pixel_size_arc)
gsp = galsim.GSParams(folding_threshold=folding_threshold)
############TEST: START
# Use sheared PSF to test the PSF orientation
# self.psf = galsim.InterpolatedImage(img, gsparams=gsp).shear(g1=0.8, g2=0.)
############TEST: END
self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
# if g_order in ['C','D','E']:
# add_psf = galsim.Gaussian(sigma=contam_order_sigma[g_order], flux=1.0)
# self.psf = galsim.Convolve(self.psf, add_psf)
wcs = chip.img.wcs.local(pos_img)
scale = galsim.PixelScale(0.074)
self.psf = wcs.toWorld(scale.toImage(self.psf), image_pos=(pos_img))
# return self.PSFspin(x=px/0.01, y=py/0.01)
return self.psf, galsim.Shear(e=0., beta=(np.pi/2)*galsim.radians)
return PSF_int_trans, PSF_int
# pixSize = np.rad2deg(self.pixsize*1e-3/28)*3600 #set psf pixsize
#
# # assert self.iccd == int(chip.getChipLabel(chipID=chip.chipID)), 'ERROR: self.iccd != chip.chipID'
# twave = self._findWave(bandpass)
# if twave == -1:
# print("!!!PSF bandpass does not match.")
# exit()
# PSFMat = self.psfMat[twave]
# cen_col= self.cen_col[twave]
# cen_row= self.cen_row[twave]
#
# px = (pos_img.x - chip.cen_pix_x)*0.01
# py = (pos_img.y - chip.cen_pix_y)*0.01
# if findNeighMode == 'treeFind':
# imPSF = psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, PSFCentroidWgt=True)
# if findNeighMode == 'hoclistFind':
# assert(self.hoc != 0), 'hoclist should be built correctly!'
# imPSF = psfMaker_IDW(px, py, PSFMat, cen_col, cen_row, IDWindex=2, OnlyNeighbors=True, hoc=self.hoc[twave], hoclist=self.hoclist[twave], PSFCentroidWgt=True)
#
# ############TEST: START
# TestGaussian = False
# if TestGaussian:
# gsx = galsim.Gaussian(sigma=0.04)
# #pointing_pa = -23.433333
# imPSF= gsx.shear(g1=0.8, g2=0.).rotate(0.*galsim.degrees).drawImage(nx = 256, ny=256, scale=pixSize).array
# ############TEST: END
#
# if galsimGSObject:
# imPSFt = np.zeros([257,257])
# imPSFt[0:256, 0:256] = imPSF
# # imPSFt[120:130, 0:256] = 1.
#
# img = galsim.ImageF(imPSFt, scale=pixSize)
# gsp = galsim.GSParams(folding_threshold=folding_threshold)
# ############TEST: START
# # Use sheared PSF to test the PSF orientation
# # self.psf = galsim.InterpolatedImage(img, gsparams=gsp).shear(g1=0.8, g2=0.)
# ############TEST: END
# self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
# wcs = chip.img.wcs.local(pos_img)
# scale = galsim.PixelScale(0.074)
# self.psf = wcs.toWorld(scale.toImage(self.psf), image_pos=(pos_img))
#
# # return self.PSFspin(x=px/0.01, y=py/0.01)
# return self.psf, galsim.Shear(e=0., beta=(np.pi/2)*galsim.radians)
# return imPSF
#
# def PSFspin(self, x, y):
# """
# The PSF profile at a given image position relative to the axis center
#
# Parameters:
# theta : spin angles in a given exposure in unit of [arcsecond]
# dx, dy: relative position to the axis center in unit of [pixels]
#
# Return:
# Spinned PSF: g1, g2 and axis ratio 'a/b'
# """
# a2Rad = np.pi/(60.0*60.0*180.0)
#
# ff = self.sigGauss * 0.107 * (1000.0/10.0) # in unit of [pixels]
# rc = np.sqrt(x*x + y*y)
# cpix = rc*(self.sigSpin*a2Rad)
#
# beta = (np.arctan2(y,x) + np.pi/2)
# ell = cpix**2/(2.0*ff**2+cpix**2)
# qr = np.sqrt((1.0+ell)/(1.0-ell))
# PSFshear = galsim.Shear(e=ell, beta=beta*galsim.radians)
# return self.psf.shear(PSFshear), PSFshear
from ObservationSim.Instrument import Filter, FilterParam, Chip
import yaml
if __name__ == '__main__':
configfn = '/Users/zhangxin/Work/SlitlessSim/CSST_SIM/CSST_new_sim/csst-simulation/config/config_C6_dev.yaml'
with open(configfn, "r") as stream:
try:
config = yaml.safe_load(stream)
for key, value in config.items():
print (key + " : " + str(value))
except yaml.YAMLError as exc:
print(exc)
chip = Chip(chipID=1,config=config)
filter_id, filter_type = chip.getChipFilter()
filt = Filter(filter_id=filter_id,
filter_type=filter_type,
filter_param=FilterParam())
psf_i = PSFInterpSLS(chip, filt,PSF_data_prefix="/Volumes/EAGET/CSST_PSF_data/SLS_PSF_PCA_fp/")
pos_img = galsim.PositionD(x=25155, y=-22060)
psf_im = psf_i.get_PSF(chip, pos_img = pos_img, g_order = '1')
ObservationSim/PSF/__init__.py
View file @ f1dab347
......@@ -2,4 +2,5 @@ from .PSFModel import PSFModel
from .PSFGauss import PSFGauss
# from .PSFInterp.PSFInterp import PSFInterp
from .PSFInterp import PSFInterp
from .PSFInterpSLS import PSFInterpSLS
from .FieldDistortion import FieldDistortion
\ No newline at end of file
config/config_C6_dev.yaml 0 → 100644
View file @ f1dab347
---
###############################################
#
# 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
<<<<<<< HEAD
work_dir: "/share/home/zhangxin/CSST_SIM/CSST_new_sim/csst-simulation/"
=======
work_dir: "/share/home/fangyuedong/new_sim/workplace/"
# work_dir: "/share/C6_new_sim_2sq"
>>>>>>> new_sim
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:
use_mpi: NO
# 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: "Spectroscopic"
# 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: [10]
# 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
...
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