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Commit d5059d30 authored by Fang Yuedong's avatar Fang Yuedong
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fixed photometry/bad column issue, modify detector effects order

parent ef8c4609
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ObservationSim/Config/ChipOutput.py
View file @ d5059d30
......@@ -37,8 +37,10 @@ class ChipOutput(object):
fmt5 = "%10s %8.4f %8.4f %8.4f\n"
self.hdr = hdr1 + hdr2 + hdr3 + hdr4 + hdr5
self.fmt = fmt1 + fmt2 + fmt3 + fmt4 + fmt5
print("pointing_type = %s\n"%(pointing_type))
if pointing_type == 'MS':
self.cat = open(os.path.join(self.subdir, self.cat_name), "w")
print("Creating catalog file %s ...\n"%(os.path.join(self.subdir, self.cat_name)))
self.cat.write(self.hdr)
def updateHDR(self, hdr):
......
ObservationSim/Instrument/Chip/Chip.py
View file @ d5059d30
......@@ -30,16 +30,13 @@ class Chip(FocalPlane):
self.read_noise = 5.0 # e/pix
self.dark_noise = 0.02 # e/pix/s
self.pix_scale = 0.074 # pixel scale
# self.gain = 1.0
# self.bias_level = 1000 # e-/pix
self.gain = float(config["gain"])
self.bias_level = float(config["bias_level"])
self.overscan = 1000
self.exptime = 150 # second
# self.dark_exptime = 300 # second
# self.flat_exptime = 150 # second
self.exptime = float(config["exp_time"]) # second
self.dark_exptime = float(config['dark_exptime'])
self.flat_exptime = float(config['flat_exptime'])
self.readout_time = float(config['readout_time'])
# A chip ID must be assigned
self.chipID = int(chipID)
......@@ -234,60 +231,6 @@ class Chip(FocalPlane):
noise = self.dark_noise * exptime + self.read_noise**2
return noise
def addNoise_phot(self, img, exptime=150.0, sky_noise=0., seed=31415):
rng = galsim.BaseDeviate(seed)
dark_noise = galsim.DeviateNoise(galsim.PoissonDeviate(rng, self.dark_noise*exptime))
img.addNoise(dark_noise)
ccd_noise = galsim.CCDNoise(rng, sky_level=sky_noise, gain=self.gain, read_noise=self.read_noise)
img.addNoise(ccd_noise)
return img
def addNoise_spec(self, config, tel, img, sky_map, exptime=150.0, seed=31415):
if img.array.shape != sky_map.shape:
raise ValueError("The shape img and sky_map must be equal.")
# n_img_arrar = (img.array + sky_map) * tel.pupil_area * exptime
# Should be the following?
rng = galsim.BaseDeviate(seed)
noise_img = galsim.Image(img, copy=True)
dark_noise = galsim.DeviateNoise(galsim.PoissonDeviate(rng, self.dark_noise*exptime))
img.addNoise(dark_noise)
if config["abs_back"].lower() == "y":
img += (sky_map * tel.pupil_area * exptime)
ccd_noise = galsim.CCDNoise(rng, gain=self.gain, read_noise=self.read_noise)
img.addNoise(ccd_noise)
return img
# pNoise = self.dark_noise * exptime
# noise_img = galsim.Image(n_img_arrar, copy=True)
# dNose = galsim.PoissonNoise(rng=rng, sky_level=pNoise)
# noise_img.addNoise(dNose)
# rdNoise = galsim.GaussianNoise(rng=rng, sigma=self.read_noise)
# noise_img.addNoise(rdNoise)
# return noise_img
def addNoise(self, config, tel, filt, img, sky_map=None, exptime=150.0):
if self.survey_type == "photometric":
sky_level = filt.getSkyNoise(exptime=exptime, gain=self.gain)
img = self.addNoise_phot(
img=img,
exptime=exptime,
sky_noise=filt.getSkyNoise(exptime=exptime, gain=self.gain),
seed=int(config["seed_skynoise"]))
if config["abs_back"].lower() == "y":
img += sky_level
elif self.survey_type == "spectroscopic":
if sky_map is None:
raise ValueError("Must provide sky_map for spectroscopic chip")
img = self.addNoise_spec(
config=config,
tel=tel,
img=img,
sky_map=sky_map,
exptime=exptime,
seed=int(config["seed_skynoise"]))
return img
def getChipSLSConf(self):
confFile = ''
if self.chipID == 1: confFile = ['CSST_GI2.conf', 'CSST_GI1.conf']
......@@ -351,7 +294,7 @@ class Chip(FocalPlane):
fname = os.path.join(output_dir, h_prim['FILENAME']+'.fits')
hdu1.writeto(fname, output_verify='ignore', overwrite=True)
def addEffects(self, config, img, chip_output, filt, ra_cen, dec_cen, img_rot, exptime=150., pointing_ID=0, timestamp_obs=1621915200, pointing_type='MS'):
def addEffects(self, config, img, chip_output, filt, ra_cen, dec_cen, img_rot, exptime=150., pointing_ID=0, timestamp_obs=1621915200, pointing_type='MS', sky_map=None, tel=None):
SeedGainNonuni=int(config["seed_gainNonUniform"])
SeedBiasNonuni=int(config["seed_biasNonUniform"])
SeedRnNonuni = int(config["seed_rnNonUniform"])
......@@ -368,6 +311,17 @@ class Chip(FocalPlane):
else:
BoolDeadPix = False
# Add sky background
if sky_map == None:
sky_map = filt.getSkyNoise(exptime=self.exptime)
elif img.array.shape != sky_map.shape:
raise ValueError("The shape img and sky_map must be equal.")
elif tel is not None: # If sky_map is given in flux
sky_map = sky_map * tel.pupil_area * self.exptime
if config["add_back"].lower() == "y":
img += sky_map
del sky_map
# Apply flat-field large scale structure for one chip
if config["flat_fielding"].lower() == "y":
print(" Creating and applying Flat-Fielding", flush=True)
......@@ -392,30 +346,19 @@ class Chip(FocalPlane):
del shutt_gsimg
del shuttimg
# Apply PRNU effect and output PRNU flat file:
if config["prnu_effect"].lower() == "y":
print(" Applying PRNU effect", flush=True)
prnu_img = effects.PRNU_Img(
xsize=self.npix_x,
ysize=self.npix_y,
sigma=0.01,
seed=int(config["seed_prnu"]+self.chipID))
img *= prnu_img
if config["prnu_output"].lower() == "y":
prnu_img.write("%s/FlatImg_PRNU_%s.fits" % (chip_output.subdir,self.chipID))
if config["flat_output"].lower() == "n":
del prnu_img
# Add dark current
img += self.dark_noise*exptime
# Add Poisson noise
seed = int(config["seed_poisson"]) + pointing_ID*30 + self.chipID
rng_poisson = galsim.BaseDeviate(seed)
poisson_noise = galsim.PoissonNoise(rng_poisson, sky_level=0.)
img.addNoise(poisson_noise)
# Add cosmic-rays
if config["cosmic_ray"].lower() == "y" and pointing_type=='MS':
print(" Adding Cosmic-Ray", flush=True)
cr_map = effects.produceCR_Map(
xLen=self.npix_x, yLen=self.npix_y,
exTime=exptime,
cr_pixelRatio=0.003,
exTime=self.exptime+0.5*self.readout_time,
cr_pixelRatio=0.003*(1+0.5*self.readout_time/self.exptime),
gain=self.gain,
attachedSizes=self.attachedSizes,
seed=SeedCosmicRay+pointing_ID*30+self.chipID) # seed: obj-imaging:+0; bias:+1; dark:+2; flat:+3;
......@@ -442,15 +385,78 @@ class Chip(FocalPlane):
exptime=150.)
del crmap_gsimg
# Apply PRNU effect and output PRNU flat file:
if config["prnu_effect"].lower() == "y":
print(" Applying PRNU effect", flush=True)
prnu_img = effects.PRNU_Img(
xsize=self.npix_x,
ysize=self.npix_y,
sigma=0.01,
seed=int(config["seed_prnu"]+self.chipID))
img *= prnu_img
if config["prnu_output"].lower() == "y":
prnu_img.write("%s/FlatImg_PRNU_%s.fits" % (chip_output.subdir,self.chipID))
if config["flat_output"].lower() == "n":
del prnu_img
# Add dark current
if config["add_dark"].lower() == "y":
dark_noise = galsim.DeviateNoise(galsim.PoissonDeviate(rng_poisson, self.dark_noise*(self.exptime+0.5*self.readout_time)))
img.addNoise(dark_noise)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = effects.DefectivePixels(img, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=0)
# Apply Bad lines
if config["add_badcolumns"].lower() == "y":
img = effects.BadColumns(img, seed=SeedBadColumns, chipid=self.chipID)
# Add Bias level
if config["add_bias"].lower() == "y":
print(" Adding Bias level and 16-channel non-uniformity")
# img += float(config["bias_level"])
img = effects.AddBiasNonUniform16(img,
bias_level=float(config["bias_level"]),
nsecy = 2, nsecx=8,
seed=SeedBiasNonuni+self.chipID)
# Apply Nonlinearity on the chip image
if config["non_linear"].lower() == "y":
print(" Applying Non-Linearity on the chip image", flush=True)
img = effects.NonLinearity(GSImage=img, beta1=5.e-7, beta2=0)
# Apply CCD Saturation & Blooming
if config["saturbloom"].lower() == "y":
print(" Applying CCD Saturation & Blooming")
img = effects.SaturBloom(GSImage=img, nsect_x=1, nsect_y=1, fullwell=fullwell)
# Apply CTE Effect
if config["cte_trail"].lower() == "y":
print(" Apply CTE Effect")
img = effects.CTE_Effect(GSImage=img, threshold=27)
# Add Read-out Noise
if config["add_readout"].lower() == "y":
seed = int(config["seed_readout"]) + pointing_ID*30 + self.chipID
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=self.read_noise)
img.addNoise(readout_noise)
# Apply Gain & Quantization
print(" Applying Gain (and 16 channel non-uniformity) & Quantization", flush=True)
img = effects.ApplyGainNonUniform16(
img, gain=self.gain,
nsecy = 2, nsecx=8,
seed=SeedGainNonuni+self.chipID)
img.array[img.array > 65535] = 65535
img.replaceNegative(replace_value=0)
img.quantize()
######################################################################################
# Output images for calibration pointing
######################################################################################
# Bias output
if config["bias_output"].lower() == "y" and pointing_type=='CAL':
print(" Output N frame Bias files", flush=True)
......@@ -466,7 +472,7 @@ class Chip(FocalPlane):
cr_map = effects.produceCR_Map(
xLen=self.npix_x, yLen=self.npix_y,
exTime=0.01,
cr_pixelRatio=0.003*0.01/self.exptime,
cr_pixelRatio=0.003*(0.01+0.5*self.readout_time)/(self.exptime+0.5*self.readout_time),
gain=self.gain,
attachedSizes=self.attachedSizes,
seed=SeedCosmicRay+pointing_ID*30+self.chipID+1)
......@@ -477,7 +483,11 @@ class Chip(FocalPlane):
# Non-Linearity for Bias
if config["non_linear"].lower() == "y":
print(" Applying Non-Linearity on the Bias image", flush=True)
BiasCombImg = effects.NonLinearity(GSImage=BiasCombImg, beta1=1.e-7, beta2=1.e-10)
BiasCombImg = effects.NonLinearity(GSImage=BiasCombImg, beta1=5.e-7, beta2=0)
# Apply Bad lines
if config["add_badcolumns"].lower() == "y":
BiasCombImg = effects.BadColumns(BiasCombImg-float(config["bias_level"])+5, seed=SeedBadColumns, chipid=self.chipID) + float(config["bias_level"])-5
BiasCombImg = effects.ApplyGainNonUniform16(BiasCombImg, gain=self.gain,
nsecy = 2, nsecx=8,
......@@ -493,7 +503,7 @@ class Chip(FocalPlane):
datetime_obs = datetime.fromtimestamp(timestamp_obs)
date_obs = datetime_obs.strftime("%y%m%d")
time_obs = datetime_obs.strftime("%H%M%S")
timestamp_obs += 5 * 60
timestamp_obs += 10 * 60
self.outputCal(
img=BiasCombImg,
ra_cen=ra_cen,
......@@ -517,7 +527,7 @@ class Chip(FocalPlane):
elif config["add_bias"].lower() == "n":
biaslevel = 0
overscan = 0
darklevel = self.dark_noise*self.flat_exptime
darklevel = self.dark_noise*(self.flat_exptime+0.5*self.readout_time)
for i in range(NFlat):
FlatSingle = flat_img * prnu_img + darklevel
FlatCombImg, FlatTag = effects.MakeFlatNcomb(
......@@ -526,15 +536,15 @@ class Chip(FocalPlane):
read_noise=self.read_noise,
gain=1,
overscan=overscan,
biaslevel=biaslevel,
biaslevel=0,
seed_bias=SeedDefective+self.chipID
)
if config["cosmic_ray"].lower() == "y":
if config["cray_differ"].lower() == "y":
cr_map = effects.produceCR_Map(
xLen=self.npix_x, yLen=self.npix_y,
exTime=self.flat_exptime,
cr_pixelRatio=0.003*self.flat_exptime/self.exptime,
exTime=self.flat_exptime+0.5*self.readout_time,
cr_pixelRatio=0.003*(self.flat_exptime+0.5*self.readout_time)/(self.exptime+0.5*self.readout_time),
gain=self.gain,
attachedSizes=self.attachedSizes,
seed=SeedCosmicRay+pointing_ID*30+self.chipID+3)
......@@ -544,19 +554,35 @@ class Chip(FocalPlane):
if config["non_linear"].lower() == "y":
print(" Applying Non-Linearity on the Flat image", flush=True)
FlatCombImg = effects.NonLinearity(GSImage=FlatCombImg, beta1=1.e-7, beta2=1.e-10)
FlatCombImg = effects.NonLinearity(GSImage=FlatCombImg, beta1=5.e-7, beta2=0)
if config["cte_trail"].lower() == "y":
FlatCombImg = effects.CTE_Effect(GSImage=FlatCombImg, threshold=3)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
FlatCombImg = effects.DefectivePixels(FlatCombImg, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=0)
# Apply Bad lines
if config["add_badcolumns"].lower() == "y":
FlatCombImg = effects.BadColumns(FlatCombImg, seed=SeedBadColumns, chipid=self.chipID)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
FlatCombImg = effects.DefectivePixels(FlatCombImg, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=self.bias_level)
# Add Bias level
if config["add_bias"].lower() == "y":
print(" Adding Bias level and 16-channel non-uniformity")
# img += float(config["bias_level"])
FlatCombImg = effects.AddBiasNonUniform16(FlatCombImg,
bias_level=biaslevel,
nsecy = 2, nsecx=8,
seed=SeedBiasNonuni+self.chipID)
# Add Read-out Noise
if config["add_readout"].lower() == "y":
seed = int(config["seed_readout"]) + pointing_ID*30 + self.chipID
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=self.read_noise)
FlatCombImg.addNoise(readout_noise)
FlatCombImg = effects.ApplyGainNonUniform16(FlatCombImg, gain=self.gain,
nsecy = 2, nsecx=8,
......@@ -569,7 +595,7 @@ class Chip(FocalPlane):
datetime_obs = datetime.fromtimestamp(timestamp_obs)
date_obs = datetime_obs.strftime("%y%m%d")
time_obs = datetime_obs.strftime("%H%M%S")
timestamp_obs += 5 * 60
timestamp_obs += 10 * 60
self.outputCal(
img=FlatCombImg,
ra_cen=ra_cen,
......@@ -601,15 +627,15 @@ class Chip(FocalPlane):
for i in range(NDark):
DarkCombImg, DarkTag = effects.MakeDarkNcomb(
self.npix_x, self.npix_y,
overscan=overscan, bias_level=biaslevel, darkpsec=0.02, exptime=self.dark_exptime,
overscan=overscan, bias_level=0, darkpsec=0.02, exptime=self.dark_exptime+0.5*self.readout_time,
ncombine=1, read_noise=self.read_noise,
gain=1, seed_bias=SeedBiasNonuni+self.chipID)
if config["cosmic_ray"].lower() == "y":
if config["cray_differ"].lower() == "y":
cr_map = effects.produceCR_Map(
xLen=self.npix_x, yLen=self.npix_y,
exTime=self.dark_exptime,
cr_pixelRatio=0.003*self.dark_exptime/self.exptime,
exTime=self.dark_exptime+0.5*self.readout_time,
cr_pixelRatio=0.003*(self.dark_exptime+0.5*self.readout_time)/(self.exptime+0.5*self.readout_time),
gain=self.gain,
attachedSizes=self.attachedSizes,
seed=SeedCosmicRay+pointing_ID*30+self.chipID+2)
......@@ -638,19 +664,35 @@ class Chip(FocalPlane):
# Non-Linearity for Dark
if config["non_linear"].lower() == "y":
print(" Applying Non-Linearity on the Dark image", flush=True)
DarkCombImg = effects.NonLinearity(GSImage=DarkCombImg, beta1=1.e-7, beta2=1.e-10)
DarkCombImg = effects.NonLinearity(GSImage=DarkCombImg, beta1=5.e-7, beta2=0)
if config["cte_trail"].lower() == "y":
DarkCombImg = effects.CTE_Effect(GSImage=DarkCombImg, threshold=3)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
DarkCombImg = effects.DefectivePixels(DarkCombImg, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=0)
# Apply Bad lines
if config["add_badcolumns"].lower() == "y":
DarkCombImg = effects.BadColumns(DarkCombImg, seed=SeedBadColumns, chipid=self.chipID)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
DarkCombImg = effects.DefectivePixels(DarkCombImg, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=self.bias_level)
# Add Bias level
if config["add_bias"].lower() == "y":
print(" Adding Bias level and 16-channel non-uniformity")
# img += float(config["bias_level"])
DarkCombImg = effects.AddBiasNonUniform16(DarkCombImg,
bias_level=biaslevel,
nsecy = 2, nsecx=8,
seed=SeedBiasNonuni+self.chipID)
# Add Read-out Noise
if config["add_readout"].lower() == "y":
seed = int(config["seed_readout"]) + pointing_ID*30 + self.chipID
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=self.read_noise)
DarkCombImg.addNoise(readout_noise)
DarkCombImg = effects.ApplyGainNonUniform16(
DarkCombImg, gain=self.gain,
......@@ -667,7 +709,7 @@ class Chip(FocalPlane):
datetime_obs = datetime.fromtimestamp(timestamp_obs)
date_obs = datetime_obs.strftime("%y%m%d")
time_obs = datetime_obs.strftime("%H%M%S")
timestamp_obs += 5 * 60
timestamp_obs += 10 * 60
self.outputCal(
img=DarkCombImg,
ra_cen=ra_cen,
......@@ -680,41 +722,6 @@ class Chip(FocalPlane):
output_dir=chip_output.subdir,
exptime=self.dark_exptime)
del DarkCombImg
# Apply Nonlinearity on the chip image
if config["non_linear"].lower() == "y":
print(" Applying Non-Linearity on the chip image", flush=True)
img = effects.NonLinearity(GSImage=img, beta1=1.e-7, beta2=1.e-10)
# Apply CCD Saturation & Blooming
if config["saturbloom"].lower() == "y":
print(" Applying CCD Saturation & Blooming")
img = effects.SaturBloom(GSImage=img, nsect_x=1, nsect_y=1, fullwell=fullwell)
# Apply CTE Effect
if config["cte_trail"].lower() == "y":
print(" Apply CTE Effect")
img = effects.CTE_Effect(GSImage=img, threshold=27)
# Apply Bad lines
if config["add_badcolumns"].lower() == "y":
img = effects.BadColumns(img, seed=SeedBadColumns, chipid=self.chipID)
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(SeedDefective+self.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = effects.DefectivePixels(img, IfHotPix=BoolHotPix, IfDeadPix=BoolDeadPix, fraction=badfraction, seed=SeedDefective+self.chipID, biaslevel=self.bias_level)
# Apply Gain & Quantization
print(" Applying Gain (and 16 channel non-uniformity) & Quantization", flush=True)
img = effects.ApplyGainNonUniform16(
img, gain=self.gain,
nsecy = 2, nsecx=8,
seed=SeedGainNonuni+self.chipID)
img.array[img.array > 65535] = 65535
img.replaceNegative(replace_value=0)
img.quantize()
# img = galsim.ImageUS(img)
# # 16 output channel, with each a single image file
......
ObservationSim/Instrument/Chip/Effects.py
View file @ d5059d30
......@@ -5,6 +5,7 @@ from numpy.core.fromnumeric import mean, size
from numpy.random import Generator, PCG64
import math
from numba import jit
from astropy import stats
def AddOverscan(GSImage, overscan=1000, gain=1, widthl=27, widthr=27, widtht=8, widthb=8, read_noise=5):
......@@ -31,7 +32,7 @@ def AddOverscan(GSImage, overscan=1000, gain=1, widthl=27, widthr=27, widtht=8,
return newimg
def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=20210304, biaslevel=500):
def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=20210304, biaslevel=0):
# Also called bad pixels, including hot pixels and dead pixels
# Hot Pixel > 20e-/s
# Dead Pixel < 70%*Mean
......@@ -68,10 +69,11 @@ def DefectivePixels(GSImage, IfHotPix=True, IfDeadPix=True, fraction=1E-4, seed=
return GSImage
def BadColumns(GSImage, seed=20210309, chipid=1):
def BadColumns(GSImage, seed=20240309, chipid=1):
# Set bad column values
ysize,xsize = GSImage.array.shape
subarr = GSImage.array[int(ysize*0.1):int(ysize*0.12), int(xsize*0.1):int(xsize*0.12)]
subarr = stats.sigma_clip(subarr, sigma=4, cenfunc='median', maxiters=3, masked=False)
meanimg = np.median(subarr)
stdimg = np.std(subarr)
seed += chipid
......@@ -83,12 +85,16 @@ def BadColumns(GSImage, seed=20210309, chipid=1):
nbadsecA,nbadsecD = rgn.integers(low=1, high=5, size=2)
collen = rgcollen.integers(low=int(ysize*0.1), high=int(ysize*0.7), size=(nbadsecA+nbadsecD))
xposit = rgxpos.integers(low=int(xsize*0.05), high=int(xsize*0.95), size=(nbadsecA+nbadsecD))
signs = 2*rgdn.integers(0,2,size=(nbadsecA+nbadsecD))-1
dn = rgdn.integers(low=meanimg*0.4, high=meanimg*0.8, size=(nbadsecA+nbadsecD))*signs
print(xposit+1)
# signs = 2*rgdn.integers(0,2,size=(nbadsecA+nbadsecD))-1
if meanimg>0:
dn = rgdn.integers(low=meanimg*1.3+50, high=meanimg*2+150, size=(nbadsecA+nbadsecD)) #*signs
elif meanimg<0:
dn = rgdn.integers(low=meanimg*2-150, high=meanimg*1.3-50, size=(nbadsecA+nbadsecD)) #*signs
for badcoli in range(nbadsecA):
GSImage.array[(ysize-collen[badcoli]):ysize,xposit[badcoli]:(xposit[badcoli]+1)] += np.random.random((collen[badcoli],1))*stdimg*3+dn[badcoli]
GSImage.array[(ysize-collen[badcoli]):ysize,xposit[badcoli]:(xposit[badcoli]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli],1)))+dn[badcoli])
for badcoli in range(nbadsecD):
GSImage.array[0:collen[badcoli+nbadsecA],xposit[badcoli+nbadsecA]:(xposit[badcoli+nbadsecA]+1)] += np.random.random((collen[badcoli+nbadsecA],1))*stdimg*3+dn[badcoli+nbadsecA]
GSImage.array[0:collen[badcoli+nbadsecA],xposit[badcoli+nbadsecA]:(xposit[badcoli+nbadsecA]+1)] = (np.abs(np.random.normal(0, stdimg*2, (collen[badcoli+nbadsecA],1)))+dn[badcoli+nbadsecA])
return GSImage
......@@ -223,7 +229,7 @@ def PRNU_Img(xsize, ysize, sigma=0.01, seed=202101):
return prnuimg
def NonLinearity(GSImage, beta1=1E-7, beta2=1E-10):
def NonLinearity(GSImage, beta1=5E-7, beta2=0):
NonLinear_f = lambda x, beta_1, beta_2: x - beta_1*x*x + beta_2*x*x*x
GSImage.applyNonlinearity(NonLinear_f, beta1, beta2)
return GSImage
......
ObservationSim/Instrument/Filter.py
View file @ d5059d30
......@@ -73,5 +73,5 @@ class Filter(object):
def getPhotonE(self):
return photonEnergy(self.effective_wavelength)
def getSkyNoise(self, exptime, gain):
def getSkyNoise(self, exptime, gain=1.):
return self.sky_background * exptime / gain
ObservationSim/ObservationSim.py
View file @ d5059d30
......@@ -97,6 +97,7 @@ class Observation(object):
# Load SED
if obj.type == 'star':
normF = chip.normF_star
#continue
try:
obj.load_SED(
survey_type=chip.survey_type,
......@@ -108,6 +109,7 @@ class Observation(object):
continue
elif obj.type == 'galaxy': # or obj.type == quasar
normF = chip.normF_galaxy
# continue
obj.load_SED(
sed_path=sed_dir,
survey_type=chip.survey_type,
......@@ -116,6 +118,7 @@ class Observation(object):
target_filt=filt)
elif obj.type == 'quasar':
normF = chip.normF_galaxy
# continue
obj.load_SED(
sed_path=sed_dir,
survey_type=chip.survey_type,
......@@ -200,7 +203,7 @@ class Observation(object):
# Detector Effects
# ===========================================================
chip.img = chip.addNoise(config=self.config, tel=self.tel, filt=filt, img=chip.img, sky_map=sky_map)
# chip.img = chip.addNoise(config=self.config, tel=self.tel, filt=filt, img=chip.img, sky_map=sky_map)
# chip.img = chip.addEffects(config=self.config, img=chip.img, chip_output=chip_output, filt=filt, pointing_ID=pointing_ID)
# whether to output zero, dark, flat calibration images.
......@@ -214,7 +217,8 @@ class Observation(object):
img_rot=img_rot,
pointing_ID=pointing_ID,
timestamp_obs=timestamp_obs,
pointing_type=pointing_type)
pointing_type=pointing_type,
sky_map=sky_map)
if pointing_type == 'MS':
datetime_obs = datetime.fromtimestamp(timestamp_obs)
......@@ -309,13 +313,20 @@ class Observation(object):
sed_dir=self.path_dict["SED_dir"])
print("finished running chip#%d..."%(chip.chipID), flush=True)
def runExposure_MPI_PointingList(self, ra_cen=None, dec_cen=None, pRange=None, timestamp_obs=[1621915200], pointing_type=['MS'], img_rot=None, exptime=150., input_cat_name=None, shear_cat_file=None):
def runExposure_MPI_PointingList(self, ra_cen=None, dec_cen=None, pRange=None, timestamp_obs=np.array([1621915200]), pointing_type=np.array(['MS']), img_rot=None, exptime=150., input_cat_name=None, shear_cat_file=None):
comm = MPI.COMM_WORLD
ind_thread = comm.Get_rank()
num_thread = comm.Get_size()
nchips_per_fp = len(self.chip_list)
# TEMP
if len(timestamp_obs) == 1:
timestamp_obs = np.tile(timestamp_obs, len(ra_cen))
pointing_type = np.tile(pointing_type, len(ra_cen))
timestamp_obs = timestamp_obs[pRange]
pointing_type = pointing_type[pRange]
ra_cen = ra_cen[pRange]
dec_cen = dec_cen[pRange]
......@@ -325,11 +336,6 @@ class Observation(object):
else:
pStart = 0
# TEMP
if len(timestamp_obs) == 1:
timestamp_obs *= len(pRange)
pointing_type *= len(pRange)
for ipoint in range(len(ra_cen)):
for ichip in range(nchips_per_fp):
i = ipoint*nchips_per_fp + ichip
......
ObservationSim/PSF/PSFInterp/PSFInterp.py
View file @ d5059d30
......@@ -171,8 +171,8 @@ class PSFInterp(PSFModel):
img = galsim.ImageF(imPSF, scale=pixSize)
gsp = galsim.GSParams(folding_threshold=folding_threshold)
self.psf = galsim.InterpolatedImage(img, gsparams=gsp)
gaussPSF = galsim.Gaussian(sigma=0.04, gsparams=gsp)
self.psf = galsim.Convolve(gaussPSF, self.psf)
# gaussPSF = galsim.Gaussian(sigma=0.04, gsparams=gsp)
# self.psf = galsim.Convolve(gaussPSF, self.psf)
return self.PSFspin(x=px/0.01, y=py/0.01)
return imPSF
......
ObservationSim/Pointing.py
View file @ d5059d30
......@@ -9,6 +9,8 @@ data_dir = "/data/simudata/CSSOSDataProductsSims/data/"
config_file = os.path.join(work_dir, "ObservationSim.cfg")
config = ReadConfig(config_file)
exp_time = float(config['exp_time'])
# Read Pointing list
pointing_file = os.path.join(data_dir, "pointing10_20210202.dat")
f = open(pointing_file, 'r')
......@@ -37,12 +39,16 @@ pointing_type = ['CAL']*ncal + pointing_type
t0 = datetime(2021, 5, 25, 12, 0, 0)
t = datetime.timestamp(t0)
timestamp_obs = []
delta_t = 10 # Time elapsed between exposures (minutes)
for i in range(len(pointing_type)):
timestamp_obs.append(t)
if pointing_type[i] == 'CAL':
t += 3 * 5 * 60 # 3 calibration exposure
t += 3 * delta_t * 60 # 3 calibration exposure
elif pointing_type[i] == 'MS':
t += 5 * 60
t += delta_t * 60
timestamp_obs = np.array(timestamp_obs)
pointing_type = np.array(pointing_type)
# Define the range of pointing list
pRange = range(0, 2)
\ No newline at end of file
ObservationSim/run.pbs
View file @ d5059d30
......@@ -7,7 +7,7 @@
##mpdboot -n 10 -f ./mpd.hosts
##PBS -j oe
#PBS -l nodes=comput110:ppn=80
#PBS -l nodes=comput108:ppn=80
#####PBS -q longq
#PBS -q batch
#PBS -u fangyuedong
......
ObservationSim/runExposure.py
View file @ d5059d30
......@@ -20,4 +20,4 @@ gc.enable()
# Testing run pointing list (using MPI)
obs = Observation(work_dir=work_dir, data_dir=data_dir)
# obs.runExposure_MPI_PointingList(ra_cen=pRA, dec_cen=pDEC, pRange=pRange, timestamp_obs=timestamp_obs, pointing_type=pointing_type)
obs.runExposure_MPI_PointingList(ra_cen=pRA, dec_cen=pDEC, pRange=pRange, timestamp_obs=timestamp_obs, pointing_type=pointing_type, exptime=150.)
obs.runExposure_MPI_PointingList(ra_cen=pRA, dec_cen=pDEC, pRange=pRange, timestamp_obs=timestamp_obs, pointing_type=pointing_type, exptime=exp_time)
test/ObservationSim.cfg
View file @ d5059d30
......@@ -7,6 +7,7 @@
# Observation setting
date_obs 210525 # Observation date [yymmdd]
time_obs 120000 # Observation time [hhmmss]
exp_time 150. # Exposure time [seconds]
ra_center 60.0 # Telesscope pointing center [degree] (Default)
dec_center -40.0
psf_rcont 0.15,0.8 # Radius of the 80% flux concentration (for a Gaussian PSF)
......@@ -18,7 +19,7 @@ survey_type Photometric # Survey simulation option:
# 'Photometric': only run sims for photometric chips
# 'Spectroscopic': only run sims for spectroscopic chps
# 'All': run sims for all chips
psf_model Interp # which PSF model to use:
psf_model Interp # Which PSF model to use:
# 'Gauss': simple gaussin profile
# 'Interp': Interpolated PSF from measured data
......@@ -26,8 +27,9 @@ psf_model Interp # which PSF model to use:
# Instrument effects
#===========================================================
field_dist y # Y/N: Whether to add field distortion
abs_back y # Y: add sky background + sky noise & dark + dark noise
# N: only add sky noise and dark noise
add_back y # Y/N: Whether to add sky background
add_dark y # Y/N: Whether to add dark noise
add_readout y # Y/N: Whether to add read-out (Gaussian) noise
add_bias y # Y/N: Whether to add bias-level to image
shutter_effect y # Y/N: Whether to apply shutter effect
shutter_output n # Y/N: Whether to export shutter effect 16-bit image (<=65535)
......@@ -56,6 +58,7 @@ NDark 1 # Number of frames to be exported
NFlat 1 # Number of frames to be exported
dark_exptime 300 # Exposure time for dark current frame
flat_exptime 150 # Exposure time for flat field frame
readout_time 40 # Time for read-out by each channel
#===========================================================
# Shear method
......@@ -85,6 +88,7 @@ seed_mock 12345678 # Seed for generating random values of ra,
seed_star 121212121 # Seed for generating random redshift, sed_type for stars
seed_gal 212121212 # Seed for generating random redshift, sed_type for galaxies
seed_Av 121212 # Seed for generating random intrinsic extinction
seed_poisson 20210601 # Seed for Poisson noise
seed_CR 20210317 # Seed for generating random cosmic ray map
seed_flat 20210101 # Seed for generating random flat field
seed_prnu 20210102 # Seed for photo-response non-uniformity
......@@ -94,3 +98,4 @@ seed_biasNonUniform 20210203 # Seed for Bias nonuniformity
seed_rnNonUniform 20210204 # Seed for ReadNoise nonuniformity
seed_badcolumns 20240309 # Initial 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
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