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Commit 36189a3e authored by JX's avatar JX 😵
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Merge remote-tracking branch 'origin/develop'

parents dd26d370 27646bc4
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tests/test_darknoise_func.py
View file @ 36189a3e
import unittest
import sys,os,math
import sys
import os
import math
from itertools import islice
import numpy as np
import galsim
import yaml
from ObservationSim.Instrument import Chip, Filter, FilterParam, FocalPlane
#from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from observation_sim.instruments import Chip, Filter, FilterParam, FocalPlane
### test FUNCTION --- START ###
def get_base_img(img, chip, read_noise, readout_time, dark_noise, exptime=150., InputDark=None):
if InputDark == None:
# base_level = read_noise**2 + dark_noise*(exptime+0.5*readout_time)
## base_level = dark_noise*(exptime+0.5*readout_time)
# base_level = dark_noise*(exptime+0.5*readout_time)
base_level = dark_noise*(exptime)
base_img1 = base_level * np.ones_like(img.array)
else:
......@@ -25,8 +28,8 @@ def get_base_img(img, chip, read_noise, readout_time, dark_noise, exptime=150.,
arr = np.broadcast_to(arr, (ny, nx))
base_img2 = np.zeros_like(img.array)
base_img2[:ny, :] = arr
base_img2[ny:, :] = arr[::-1,:]
base_img2[:,:] = base_img2[:,:]*(readout_time/ny)*dark_noise
base_img2[ny:, :] = arr[::-1, :]
base_img2[:, :] = base_img2[:, :]*(readout_time/ny)*dark_noise
return base_img1+base_img2
### test FUNCTION --- END ###
......@@ -35,16 +38,18 @@ def defineCCD(iccd, config_file):
with open(config_file, "r") as stream:
try:
config = yaml.safe_load(stream)
#for key, value in config.items():
# for key, value in config.items():
# print (key + " : " + str(value))
except yaml.YAMLError as exc:
print(exc)
chip = Chip(chipID=iccd, config=config)
chip.img = galsim.ImageF(chip.npix_x, chip.npix_y)
focal_plane = FocalPlane(chip_list=[iccd])
chip.img.wcs= focal_plane.getTanWCS(192.8595, 27.1283, -113.4333*galsim.degrees, chip.pix_scale)
chip.img.wcs = focal_plane.getTanWCS(
192.8595, 27.1283, -113.4333*galsim.degrees, chip.pix_scale)
return chip
def defineFilt(chip):
filter_param = FilterParam()
filter_id, filter_type = chip.getChipFilter()
......@@ -60,7 +65,8 @@ def defineFilt(chip):
class detModule_coverage(unittest.TestCase):
def __init__(self, methodName='runTest'):
super(detModule_coverage, self).__init__(methodName)
self.dataPath = os.path.join(os.getenv('UNIT_TEST_DATA_ROOT'), 'csst_msc_sim/csst_fz_msc')
self.dataPath = os.path.join(
os.getenv('UNIT_TEST_DATA_ROOT'), 'csst_msc_sim/csst_fz_msc')
self.iccd = 1
def test_add_dark(self):
......@@ -70,16 +76,19 @@ class detModule_coverage(unittest.TestCase):
print(chip.chipID)
print(chip.cen_pix_x, chip.cen_pix_y)
exptime=150.
base_img = get_base_img(img=chip.img, chip=chip, read_noise=chip.read_noise, readout_time=chip.readout_time, dark_noise=chip.dark_noise, exptime=exptime, InputDark=None)
exptime = 150.
base_img = get_base_img(img=chip.img, chip=chip, read_noise=chip.read_noise,
readout_time=chip.readout_time, dark_noise=chip.dark_noise, exptime=exptime, InputDark=None)
ny = int(chip.npix_y/2)
self.assertTrue( np.abs(np.max(base_img) - (exptime*chip.dark_noise+(ny-1)*(chip.readout_time/ny)*chip.dark_noise )) < 1e-6 )
self.assertTrue( np.min(base_img) == 3 )
self.assertTrue(np.abs(np.max(base_img) - (exptime*chip.dark_noise +
(ny-1)*(chip.readout_time/ny)*chip.dark_noise)) < 1e-6)
self.assertTrue(np.min(base_img) == 3)
base_img = get_base_img(img=chip.img, chip=chip, read_noise=chip.read_noise, readout_time=chip.readout_time, dark_noise=chip.dark_noise, exptime=150., InputDark="testTag")
self.assertTrue( np.abs(np.max(base_img) - ((ny-1)*(chip.readout_time/ny)*chip.dark_noise )) < 1e-6 )
base_img = get_base_img(img=chip.img, chip=chip, read_noise=chip.read_noise,
readout_time=chip.readout_time, dark_noise=chip.dark_noise, exptime=150., InputDark="testTag")
self.assertTrue(np.abs(np.max(base_img) - ((ny-1) *
(chip.readout_time/ny)*chip.dark_noise)) < 1e-6)
if __name__ == '__main__':
......
tests/test_effect_unit.py
View file @ 36189a3e
import unittest
import numpy as np
from ObservationSim.Instrument.Chip import Effects
from observation_sim.instruments.chip import effects
import galsim
import matplotlib.pyplot as plt
import os,sys,math,copy
import os
import sys
import math
import copy
from numpy.random import Generator, PCG64
import warnings
from astropy.io import fits
......@@ -13,20 +16,20 @@ warnings.filterwarnings("ignore", '.*Numba.*',)
width = 9216
height = 9232
class DetTest(unittest.TestCase):
def __init__(self, methodName='runTest'):
super(DetTest,self).__init__(methodName)
super(DetTest, self).__init__(methodName)
self.filePath('csst_msc_sim/test_sls_and_straylight')
def filePath(self, file_name):
self.datafn = os.path.join(os.getenv('UNIT_TEST_DATA_ROOT'), file_name)
self.outDataFn = os.path.join(self.datafn,'output')
self.outDataFn = os.path.join(self.datafn, 'output')
if os.path.isdir(self.outDataFn):
pass
else:
os.mkdir(self.outDataFn)
def test_prnu(self):
'''
......@@ -35,13 +38,14 @@ class DetTest(unittest.TestCase):
print('PRNU Test:')
sigma = 0.01
seed = 20210911
prnuimg = Effects.PRNU_Img(width, height, sigma=sigma, seed=seed)
prnuimg = effects.PRNU_Img(width, height, sigma=sigma, seed=seed)
meanval, stdval = np.mean(prnuimg.array), np.std(prnuimg.array)
print(' Mean & STDDEV of PRNU image are %6.4f & %6.4f.' % (meanval, stdval))
print(' Mean & STDDEV of PRNU image are %6.4f & %6.4f.' %
(meanval, stdval))
print(' PRNU Image Array:')
print(' ',prnuimg.array)
self.assertTrue(np.abs(meanval-1)<1e-6)
self.assertTrue(np.abs(stdval-sigma)<0.002)
print(' ', prnuimg.array)
self.assertTrue(np.abs(meanval-1) < 1e-6)
self.assertTrue(np.abs(stdval-sigma) < 0.002)
print('\nUnit test for PRNU has been passed.')
del prnuimg
......@@ -50,15 +54,17 @@ class DetTest(unittest.TestCase):
Test add dark current to image. Expected result: an image with dark current 3.4 e- and noise=1.844 e-.
'''
rng_poisson = galsim.BaseDeviate(20210911)
dark_noise = galsim.DeviateNoise(galsim.PoissonDeviate(rng_poisson, 0.02*(150+0.5*40)))
img = galsim.Image(200,200,dtype=np.float32, init_value=0)
print('Initial Mean & STD = %6.3f & %6.3f' % (np.mean(img.array), np.std(img.array)))
dark_noise = galsim.DeviateNoise(
galsim.PoissonDeviate(rng_poisson, 0.02*(150+0.5*40)))
img = galsim.Image(200, 200, dtype=np.float32, init_value=0)
print('Initial Mean & STD = %6.3f & %6.3f' %
(np.mean(img.array), np.std(img.array)))
img.addNoise(dark_noise)
meanval = np.mean(img.array)
stdval = np.std(img.array)
print('Dark added Mean & STD = %6.3f & %6.3f' % (meanval, stdval))
self.assertTrue(np.abs(meanval-3.4)<0.05)
self.assertTrue(np.abs(stdval-1.844)<0.02)
self.assertTrue(np.abs(meanval-3.4) < 0.05)
self.assertTrue(np.abs(stdval-1.844) < 0.02)
print('\nUnit test for dark current has been passed.')
del img
......@@ -66,149 +72,161 @@ class DetTest(unittest.TestCase):
'''
Test saturation and bleeding. Expected result: an image with bleeding effect.
'''
img = galsim.Image(500,500,dtype=np.float32)
star = galsim.Gaussian(flux=60e5,fwhm=3)
img = star.drawImage(image=img,center=(150,200))
img = galsim.Image(500, 500, dtype=np.float32)
star = galsim.Gaussian(flux=60e5, fwhm=3)
img = star.drawImage(image=img, center=(150, 200))
# gal = galsim.Sersic(n=1, half_light_radius=3,flux=50e5)
# img = gal.drawImage(image=img,center=(350,300))
img.addNoise(galsim.GaussianNoise(sigma=7))
# plt.imshow(img.array)
# plt.show()
filename1 = os.path.join(self.outDataFn,'test_satu_initimg.fits')
filename1 = os.path.join(self.outDataFn, 'test_satu_initimg.fits')
img.write(filename1)
newimg = Effects.SaturBloom(img, fullwell=9e4)
newimg = effects.SaturBloom(img, fullwell=9e4)
# plt.imshow(newimg.array)
# plt.show()
filename2 = os.path.join(self.outDataFn,'test_satu_bleedimg.fits')
filename2 = os.path.join(self.outDataFn, 'test_satu_bleedimg.fits')
newimg.write(filename2)
del img,newimg, star
del img, newimg, star
def test_nonlinear(self):
'''
Test non-linear effect. Expected result: an image with non-linearity effect.
'''
imgarr = np.arange(1,9e4,4).reshape((150,150))
imgarr = np.arange(1, 9e4, 4).reshape((150, 150))
img = galsim.Image(copy.deepcopy(imgarr))
filename1 = os.path.join(self.outDataFn,'test_nonlinear_initimg.fits')
filename1 = os.path.join(self.outDataFn, 'test_nonlinear_initimg.fits')
img.write(filename1)
newimg = Effects.NonLinearity(img, beta1=5E-7, beta2=0)
filename2 = os.path.join(self.outDataFn,'test_nonlinear_finalimg.fits')
newimg = effects.NonLinearity(img, beta1=5E-7, beta2=0)
filename2 = os.path.join(
self.outDataFn, 'test_nonlinear_finalimg.fits')
newimg.write(filename2)
plt.scatter(imgarr.flatten(), newimg.array.flatten(), s=2, alpha=0.5)
plt.plot([-1e3,9e4],[-1e3,9e4],color='black', lw=1, ls='--')
plt.plot([-1e3, 9e4], [-1e3, 9e4], color='black', lw=1, ls='--')
plt.xlabel('input (e-)')
plt.ylabel('output (e-)')
plt.savefig(os.path.join(self.outDataFn,'test_nonlinearity.png'), dpi=200)
plt.savefig(os.path.join(self.outDataFn,
'test_nonlinearity.png'), dpi=200)
plt.show()
del img,newimg,imgarr
del img, newimg, imgarr
def test_badpixel_HtrDtr(self):
img = galsim.Image(500,500,init_value=1000)
img = galsim.Image(500, 500, init_value=1000)
rgbadpix = Generator(PCG64(20210911))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = Effects.DefectivePixels(img, IfHotPix=True, IfDeadPix=True, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn,'test_badpixel_HtrDtr.fits'))
img = effects.DefectivePixels(
img, IfHotPix=True, IfDeadPix=True, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn, 'test_badpixel_HtrDtr.fits'))
del img
def test_badpixel_HfsDtr(self):
img = galsim.Image(500,500,init_value=1000)
img = galsim.Image(500, 500, init_value=1000)
rgbadpix = Generator(PCG64(20210911))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = Effects.DefectivePixels(img, IfHotPix=False, IfDeadPix=True, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn,'test_badpixel_HfsDtr.fits'))
img = effects.DefectivePixels(
img, IfHotPix=False, IfDeadPix=True, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn, 'test_badpixel_HfsDtr.fits'))
del img
def test_badpixel_HtrDfs(self):
img = galsim.Image(500,500,init_value=1000)
img = galsim.Image(500, 500, init_value=1000)
rgbadpix = Generator(PCG64(20210911))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = Effects.DefectivePixels(img, IfHotPix=True, IfDeadPix=False, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn,'test_badpixel_HtrDfs.fits'))
img = effects.DefectivePixels(
img, IfHotPix=True, IfDeadPix=False, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn, 'test_badpixel_HtrDfs.fits'))
del img
def test_badpixel_HfsDfs(self):
img = galsim.Image(500,500,init_value=1000)
img = galsim.Image(500, 500, init_value=1000)
rgbadpix = Generator(PCG64(20210911))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
img = Effects.DefectivePixels(img, IfHotPix=False, IfDeadPix=False, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn,'test_badpixel_HfsDfs.fits'))
img = effects.DefectivePixels(
img, IfHotPix=False, IfDeadPix=False, fraction=badfraction, seed=20210911, biaslevel=0)
img.write(os.path.join(self.outDataFn, 'test_badpixel_HfsDfs.fits'))
del img
def test_badlines(self):
img = galsim.Image(500,500,init_value=-1000)
img = galsim.Image(500, 500, init_value=-1000)
img.addNoise(galsim.GaussianNoise(sigma=7))
newimg = Effects.BadColumns(copy.deepcopy(img), seed=20210911)
newimg.write(os.path.join(self.outDataFn,'test_badlines.fits'))
del newimg,img
newimg = effects.BadColumns(copy.deepcopy(img), seed=20210911)
newimg.write(os.path.join(self.outDataFn, 'test_badlines.fits'))
del newimg, img
# def test_cte(self):
# img = galsim.Image(200,200,init_value=1000)
# img.array[50,80] = 1e4
# img.array[150,150] = 3e4
# newimgcol = Effects.CTE_Effect(copy.deepcopy(img),direction='column')
# newimgrow = Effects.CTE_Effect(copy.deepcopy(img),direction='row')
# newimgcol = effects.CTE_Effect(copy.deepcopy(img),direction='column')
# newimgrow = effects.CTE_Effect(copy.deepcopy(img),direction='row')
# newimgcol.write(os.path.join(self.outDataFn,'test_ctecol.fits'))
# newimgrow.write(os.path.join(self.outDataFn,'test_cterow.fits'))
# del img,newimgcol,newimgrow
def test_readnoise(self):
img = galsim.Image(200,200,init_value=1000)
img = galsim.Image(200, 200, init_value=1000)
seed = 20210911
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=5)
img.addNoise(readout_noise)
img.write(os.path.join(self.outDataFn,'test_readnoise.fits'))
img.write(os.path.join(self.outDataFn, 'test_readnoise.fits'))
stdval = np.std(img.array)
self.assertTrue(np.abs(stdval-5)<0.01*5)
self.assertTrue(np.abs(stdval-5) < 0.01*5)
print('\nUnit test for readout noise has been passed.')
del img
def test_addbias(self):
img = galsim.Image(200,200,init_value=0)
img = Effects.AddBiasNonUniform16(img,bias_level=500, nsecy = 2, nsecx=8,seed=20210911)
img = galsim.Image(200, 200, init_value=0)
img = effects.AddBiasNonUniform16(
img, bias_level=500, nsecy=2, nsecx=8, seed=20210911)
img.write('./output/test_addbias.fits')
del img
def test_apply16gains(self):
img = galsim.Image(500,500,init_value=100)
img,_ = Effects.ApplyGainNonUniform16(img, gain=1.5, nsecy=2, nsecx=8, seed=202102)
img.write(os.path.join(self.outDataFn,'test_apply16gains.fits'))
img = galsim.Image(500, 500, init_value=100)
img, _ = effects.ApplyGainNonUniform16(
img, gain=1.5, nsecy=2, nsecx=8, seed=202102)
img.write(os.path.join(self.outDataFn, 'test_apply16gains.fits'))
rightedge = int(500/8)*8
print('gain=%6.2f' % 1.5)
meanimg = np.mean(img.array[:,:rightedge])
sigmaimg = np.std(img.array[:,:rightedge])
print('mean, sigma = %6.2f, %6.2f' % (meanimg,sigmaimg))
self.assertTrue(np.abs(meanimg-100/1.5)<1)
self.assertTrue(np.abs(sigmaimg/meanimg-0.01)<0.001)
meanimg = np.mean(img.array[:, :rightedge])
sigmaimg = np.std(img.array[:, :rightedge])
print('mean, sigma = %6.2f, %6.2f' % (meanimg, sigmaimg))
self.assertTrue(np.abs(meanimg-100/1.5) < 1)
self.assertTrue(np.abs(sigmaimg/meanimg-0.01) < 0.001)
print('\nUnit test for applying 16 channel gains has been passed.')
del img
def test_cosmicray(self):
attachedSizes = np.loadtxt(os.path.join(self.datafn,'wfc-cr-attachpixel.dat'))
cr_map,_ = Effects.produceCR_Map(
xLen=500, yLen=500, exTime=150+0.5*40,
cr_pixelRatio=0.003*(1+0.5*40/150),
gain=1, attachedSizes=attachedSizes, seed=20210911)
attachedSizes = np.loadtxt(os.path.join(
self.datafn, 'wfc-cr-attachpixel.dat'))
cr_map, _ = effects.produceCR_Map(
xLen=500, yLen=500, exTime=150+0.5*40,
cr_pixelRatio=0.003*(1+0.5*40/150),
gain=1, attachedSizes=attachedSizes, seed=20210911)
crimg = galsim.Image(cr_map)
crimg.write(os.path.join(self.outDataFn,'test_cosmicray.fits'))
del cr_map,crimg
crimg.write(os.path.join(self.outDataFn, 'test_cosmicray.fits'))
del cr_map, crimg
def test_shutter(self):
img = galsim.Image(5000,5000,init_value=1000)
shuttimg = Effects.ShutterEffectArr(img, t_exp=150, t_shutter=1.3, dist_bearing=735, dt=1E-3) # shutter effect normalized image for this chip
img = galsim.Image(5000, 5000, init_value=1000)
# shutter effect normalized image for this chip
shuttimg = effects.ShutterEffectArr(
img, t_exp=150, t_shutter=1.3, dist_bearing=735, dt=1E-3)
img *= shuttimg
img.write(os.path.join(self.outDataFn,'test_shutter.fits'))
img.write(os.path.join(self.outDataFn, 'test_shutter.fits'))
del img
def test_vignette(self):
img = galsim.Image(2000,2000,init_value=1000)
img = galsim.Image(2000, 2000, init_value=1000)
print(img.bounds)
# # img.bounds = galsim.BoundsI(1, width, 1, height)
img.setOrigin(10000,10000)
flat_img = Effects.MakeFlatSmooth(img.bounds,20210911)
img.setOrigin(10000, 10000)
flat_img = effects.MakeFlatSmooth(img.bounds, 20210911)
flat_normal = flat_img / np.mean(flat_img.array)
flat_normal.write(os.path.join(self.outDataFn,'test_vignette.fits'))
del flat_img,img,flat_normal
flat_normal.write(os.path.join(self.outDataFn, 'test_vignette.fits'))
del flat_img, img, flat_normal
if __name__ == '__main__':
unittest.main()
\ No newline at end of file
unittest.main()
tests/test_focalplane.py
View file @ 36189a3e
import unittest
import os
import galsim
from ObservationSim.Instrument import FocalPlane, Chip
from observation_sim.instruments import FocalPlane, Chip
class TestFocalPlane(unittest.TestCase):
......
tests/test_imaging.py
View file @ 36189a3e
......@@ -15,12 +15,12 @@ import copy
from astropy.cosmology import FlatLambdaCDM
from astropy import constants
from astropy import units as U
from ObservationSim.MockObject._util import getObservedSED
from ObservationSim.MockObject import CatalogBase, Galaxy
from observation_sim.mock_objects._util import getObservedSED
from observation_sim.mock_objects import CatalogBase, Galaxy
from ObservationSim.Instrument import Chip, Filter, FilterParam, FocalPlane
from ObservationSim.PSF.PSFInterp import PSFInterp
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getABMAG
from observation_sim.instruments import Chip, Filter, FilterParam, FocalPlane
from observation_sim.PSF.PSFInterp import PSFInterp
from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getABMAG
class Catalog(CatalogBase):
......
tests/test_prescan_overscan_func.py
View file @ 36189a3e
......@@ -6,8 +6,7 @@ import numpy as np
import galsim
import yaml
from ObservationSim.Instrument import Chip, Filter, FilterParam, FocalPlane
#from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from observation_sim.instruments import Chip, Filter, FilterParam, FocalPlane
### test FUNCTION --- START ###
def AddPreScan(GSImage, pre1=27, pre2=4, over1=71, over2=80, nsecy = 2, nsecx=8):
......
tools/create_chip_json.py
View file @ 36189a3e
......@@ -14,17 +14,19 @@ chip_filename = 'chip_definition.json'
# "npix_y": 7680,
# "x_cen": -273.35, # [mm]
# "y_cen": 211.36, # [mm]
# "rotate_angle": 90. # [deg]
# "rotate_angle": 90. # [deg]
# }
# chip_list[chip_id] = chip_dict
def get_chip_row_col_main_fp(chip_id):
rowID = ((chip_id - 1) % 5) + 1
colID = 6 - ((chip_id - 1) // 5)
return rowID, colID
def get_chip_center_main_fp(chip_id, pixel_size=1e-2):
row, col = get_chip_row_col_main_fp(chip_id)
npix_x = 9216
npix_y = 9232
......@@ -39,16 +41,20 @@ def get_chip_center_main_fp(chip_id, pixel_size=1e-2):
xcen = (npix_x//2 + gx1//2) * xrem - (gx2-gx1)
if chip_id <= 5 or chip_id == 10:
xcen = (npix_x//2 + gx1//2) * xrem + (gx2-gx1)
# ylim of a given CCD chip
yrem = (row - 1) - nchip_y // 2
ycen = (npix_y + gy) * yrem
return xcen * pixel_size, ycen * pixel_size
def create_chip_dict_main_fp(chip_id, pixel_size=1e-2):
filter_list = ["GV", "GI", "y", "z", "y", "GI", "GU", "r", "u", "NUV", "i", "GV", "GU", "g", "NUV", "NUV", "g", "GU", "GV", "i", "NUV", "u", "r", "GU", "GI", "y", "z", "y", "GI", "GV"]
chip_label_list = [3,3,3,1,1,1,3,2,2,1,1,1,4,2,3,2,1,1,4,2,4,1,1,2,4,2,2,4,2,2]
chip_id_list = [26, 21, 16, 11, 6, 1, 27, 22, 17, 12, 7, 2, 28, 23, 18, 13, 8, 3, 29, 24, 19, 14, 9, 4, 30, 25, 20, 15, 10, 5]
filter_list = ["GV", "GI", "y", "z", "y", "GI", "GU", "r", "u", "NUV", "i", "GV", "GU", "g",
"NUV", "NUV", "g", "GU", "GV", "i", "NUV", "u", "r", "GU", "GI", "y", "z", "y", "GI", "GV"]
chip_label_list = [3, 3, 3, 1, 1, 1, 3, 2, 2, 1, 1, 1,
4, 2, 3, 2, 1, 1, 4, 2, 4, 1, 1, 2, 4, 2, 2, 4, 2, 2]
chip_id_list = [26, 21, 16, 11, 6, 1, 27, 22, 17, 12, 7, 2, 28,
23, 18, 13, 8, 3, 29, 24, 19, 14, 9, 4, 30, 25, 20, 15, 10, 5]
npix_x = 9216
npix_y = 9232
idx = chip_id_list.index(chip_id)
......@@ -63,10 +69,10 @@ def create_chip_dict_main_fp(chip_id, pixel_size=1e-2):
chip_dict = {
"chip_name": chip_name,
"pix_size": 1e-2, # [mm]
"pix_scale": 0.074, # [arcsec/pix]
"pix_scale": 0.074, # [arcsec/pix]
"npix_x": npix_x,
"npix_y": npix_y,
"x_cen": xcen, # [mm]
"x_cen": xcen, # [mm]
"y_cen": ycen, # [mm]
"rotate_angle": rotate_angle, # [deg]
"n_psf_samples": 900,
......@@ -80,6 +86,7 @@ def create_chip_dict_main_fp(chip_id, pixel_size=1e-2):
}
return chip_dict
def set_fgs_chips(filepath):
with open(filepath, "r") as f:
data = json.load(f)
......@@ -94,7 +101,7 @@ def set_fgs_chips(filepath):
data[chip_id]["full_well"] = 90000
with open(filepath, "w") as f:
json.dump(data, f, indent=4)
def add_main_fp(filepath):
for i in range(30):
......@@ -102,6 +109,7 @@ def add_main_fp(filepath):
chip_dict = create_chip_dict_main_fp(chip_id)
add_dict_to_json(filepath, str(chip_id), chip_dict)
def add_dict_to_json(filepath, key, value):
with open(filepath, 'r') as f:
data = json.load(f)
......@@ -109,8 +117,9 @@ def add_dict_to_json(filepath, key, value):
with open(filepath, "w") as f:
json.dump(data, f, indent=4)
if __name__=="__main__":
src = "../ObservationSim/Instrument/data/ccd/chip_definition.json"
if __name__ == "__main__":
src = "../observation_sim/instruments/data/ccd/chip_definition.json"
shutil.copy(src, chip_filename)
add_main_fp(chip_filename)
set_fgs_chips(chip_filename)
\ No newline at end of file
set_fgs_chips(chip_filename)
tools/getPSF.py → tools/get_PSF.py
View file @ 36189a3e
import os
import numpy as np
import ObservationSim.PSF.PSFInterp as PSFInterp
from ObservationSim.Instrument import Chip, Filter, FilterParam
import observation_sim.PSF.PSFInterp as PSFInterp
from observation_sim.instruments import Chip, Filter, FilterParam
import yaml
import galsim
import astropy.io.fits as fitsio
# Setup PATH
SIMPATH = "/share/simudata/CSSOSDataProductsSims/data/CSSTSimImage_C8/testRun_FGS"
config_filename= SIMPATH+"/config_C6_fits.yaml"
cat_filename = SIMPATH+"/MSC_00000000/MSC_10106100000000_chip_40_filt_FGS.cat"
config_filename = SIMPATH+"/config_C6_fits.yaml"
cat_filename = SIMPATH+"/MSC_00000000/MSC_10106100000000_chip_40_filt_FGS.cat"
# Read cat file
catFn = open(cat_filename,"r")
catFn = open(cat_filename, "r")
line = catFn.readline()
print(cat_filename,'\n',line)
print(cat_filename, '\n', line)
imgPos = []
chipID = -1
for line in catFn:
line = line.strip()
columns = line.split()
if chipID == -1:
chipID = int(columns[1])
else:
......@@ -37,41 +37,46 @@ with open(config_filename, "r") as stream:
try:
config = yaml.safe_load(stream)
for key, value in config.items():
print (key + " : " + str(value))
print(key + " : " + str(value))
except yaml.YAMLError as exc:
print(exc)
# Setup Chip
chip = Chip(chipID=chipID, config=config)
print('chip.bound::', chip.bound.xmin, chip.bound.xmax, chip.bound.ymin, chip.bound.ymax)
print('chip.bound::', chip.bound.xmin, chip.bound.xmax,
chip.bound.ymin, chip.bound.ymax)
for iobj in range(nobj):
print("\nget psf for iobj-", iobj, '\t', 'bandpass:', end=" ", flush=True)
# Setup Position on focalplane
x, y = imgPos[iobj, :] # try get the PSF at some location (1234, 1234) on the chip
# try get the PSF at some location (1234, 1234) on the chip
x, y = imgPos[iobj, :]
x = x+chip.bound.xmin
y = y+chip.bound.ymin
pos_img = galsim.PositionD(x, y)
# Setup sub-bandpass
# (There are 4 sub-bandpasses for each PSF sample)
filter_param = FilterParam()
filter_id, filter_type = chip.getChipFilter()
filt = Filter(
filter_id=filter_id,
filter_type=filter_type,
filter_param=filter_param,
filter_id=filter_id,
filter_type=filter_type,
filter_param=filter_param,
ccd_bandpass=chip.effCurve)
bandpass_list = filt.bandpass_sub_list
for i in range(len(bandpass_list)):
print(i, end=" ", flush=True)
bandpass = bandpass_list[i] # say you want to access the PSF for the sub-bandpass at the blue end for that chip
# say you want to access the PSF for the sub-bandpass at the blue end for that chip
bandpass = bandpass_list[i]
# Get corresponding PSF model
psf_model = PSFInterp(chip=chip, npsf=100, PSF_data_file=config["psf_setting"]["psf_dir"])
psf = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, galsimGSObject=False)
psf_model = PSFInterp(chip=chip, npsf=100,
PSF_data_file=config["psf_setting"]["psf_dir"])
psf = psf_model.get_PSF(
chip=chip, pos_img=pos_img, bandpass=bandpass, galsimGSObject=False)
if True:
fn = "psf_{:}.{:}.{:}.fits".format(chipID, iobj, i)
if fn != None:
......@@ -81,6 +86,3 @@ for iobj in range(nobj):
hdu.data = psf
hdu.header.set('pixScale', 5)
hdu.writeto(fn)
tools/indexFits_hdf5.py → tools/index_fits_hdf5.py
View file @ 36189a3e
......@@ -26,7 +26,8 @@ import galsim
def test_fits(nfits=100, dir_cat=None):
for ifits in range(nfits):
gal = galsim.Gaussian(sigma=np.random.uniform(0.2, 0.3)).shear(g1=np.random.uniform(-0.5, 0.5), g2=np.random.uniform(-0.5, 0.5))
gal = galsim.Gaussian(sigma=np.random.uniform(0.2, 0.3)).shear(
g1=np.random.uniform(-0.5, 0.5), g2=np.random.uniform(-0.5, 0.5))
arr = gal.drawImage(nx=64, ny=64, scale=0.074).array
hdu = fitsio.PrimaryHDU()
......@@ -38,41 +39,44 @@ def test_fits(nfits=100, dir_cat=None):
hdu.header.set('mag_g', 22+np.random.uniform(-1, 1))
hdu.header.set('pixScale', 0.074)
fout=dir_cat+"stampCats/testStamp_{:}.fits".format(ifits)
fout = dir_cat+"stampCats/testStamp_{:}.fits".format(ifits)
if os.path.exists(fout):
os.remove(fout)
hdu.writeto(fout)
def write_StampsIndex(dir_cat=None, DEBUG=False):
MAXNUMBERINDEX = 10000
MAXNUMBERINDEX = 10000
NSIDE = 128
fp = h5py.File(dir_cat+'stampCatsIndex.hdf5', 'w')
grp1 = fp.create_group('Stamps')
dataSet_Size = np.zeros(healpy.nside2npix(NSIDE), dtype=np.int64)
fitsList = os.listdir(dir_cat+'stampCats/') #获取fits文件列表
fitsList = os.listdir(dir_cat+'stampCats/') # 获取fits文件列表
for istamp in range(len(fitsList)):
print(istamp, ': ', fitsList[istamp], end='\r')
hdu=fitsio.open(dir_cat+"stampCats/"+fitsList[istamp])
hdu = fitsio.open(dir_cat+"stampCats/"+fitsList[istamp])
tra = hdu[0].header['RA']
tdec= hdu[0].header['DEC']
tdec = hdu[0].header['DEC']
healpixID= healpy.ang2pix(NSIDE, tra, tdec, nest=False, lonlat=True)
healpixID = healpy.ang2pix(NSIDE, tra, tdec, nest=False, lonlat=True)
if not(str(healpixID) in grp1):
if not (str(healpixID) in grp1):
grp2 = grp1.create_group(str(healpixID))
else:
grp2 = grp1[str(healpixID)]
if not('ra' in grp2):
dset_ra = grp2.create_dataset('ra', (0,), dtype='f16' , maxshape=(MAXNUMBERINDEX, ))
dset_dec= grp2.create_dataset('dec', (0,), dtype='f16', maxshape=(MAXNUMBERINDEX, ))
if not ('ra' in grp2):
dset_ra = grp2.create_dataset(
'ra', (0,), dtype='f16', maxshape=(MAXNUMBERINDEX, ))
dset_dec = grp2.create_dataset(
'dec', (0,), dtype='f16', maxshape=(MAXNUMBERINDEX, ))
dt = h5py.special_dtype(vlen=str)
dset_fn = grp2.create_dataset('filename', (0,), dtype=dt, maxshape=(MAXNUMBERINDEX, ))
dset_fn = grp2.create_dataset(
'filename', (0,), dtype=dt, maxshape=(MAXNUMBERINDEX, ))
else:
dset_ra = grp2['ra']
dset_ra = grp2['ra']
dset_dec = grp2['dec']
dset_fn = grp2['filename']
......@@ -82,13 +86,13 @@ def write_StampsIndex(dir_cat=None, DEBUG=False):
grp2['filename'].resize((dataSet_Size[healpixID],))
dset_ra[dataSet_Size[healpixID]-1] = tra
dset_dec[dataSet_Size[healpixID]-1]= tdec
dset_fn[dataSet_Size[healpixID]-1]= fitsList[istamp]
dset_dec[dataSet_Size[healpixID]-1] = tdec
dset_fn[dataSet_Size[healpixID]-1] = fitsList[istamp]
fp.close()
if DEBUG:
print('\n')
ff = h5py.File(dir_cat+"stampCatsIndex.hdf5","r")
ff = h5py.File(dir_cat+"stampCatsIndex.hdf5", "r")
ss = 0
for kk in ff['Stamps'].keys():
print(kk, ff['Stamps'][kk]['ra'].size)
......@@ -98,6 +102,5 @@ def write_StampsIndex(dir_cat=None, DEBUG=False):
if __name__ == '__main__':
dir_temp = "./Catalog_test/"
#test_fits(dir_cat=dir_temp)
# test_fits(dir_cat=dir_temp)
write_StampsIndex(dir_cat=dir_temp)
tools/TargetLocationCheck.py → tools/target_location_check.py
View file @ 36189a3e
# NOTE: This is a stand-alone function, meaning that you do not need
# to install the entire CSST image simulation pipeline.
# NOTE: This is a stand-alone function, meaning that you do not need
# to install the entire CSST image simulation pipeline.
# For a given object's coordinate (Ra, Dec), the function will predict
# the object's image position and corresponding filter in the focal plane
# For a given object's coordinate (Ra, Dec), the function will predict
# the object's image position and corresponding filter in the focal plane
# under a specified CSST pointing centered at (rap, decp).
import galsim
import numpy as np
import argparse
import matplotlib.pyplot as plt
import os, sys
import os
import sys
def focalPlaneInf(ra_target, dec_target, ra_point, dec_point, image_rot=-113.4333, figout="zTargetOnCCD.pdf"):
"""
......@@ -37,60 +39,71 @@ def focalPlaneInf(ra_target, dec_target, ra_point, dec_point, image_rot=-113.433
or type >> python TargetLocationCheck.py ra_target dec_target ra_point dec_point -image_rot=floatNum
or type >> python TargetLocationCheck.py ra_target dec_target ra_point dec_point -image_rot=floatNum -figout=FigureName
"""
print("^_^ Input target coordinate: [Ra, Dec] = [%10.6f, %10.6f]"%(ra_target,dec_target))
print("^_^ Input telescope pointing center: [Ra, Dec] = [%10.6f, %10.6f]"%(ra_point,dec_point))
print("^_^ Input camera orientation: %12.6f degree(s)"%image_rot)
print("^_^ Input target coordinate: [Ra, Dec] = [%10.6f, %10.6f]" % (
ra_target, dec_target))
print("^_^ Input telescope pointing center: [Ra, Dec] = [%10.6f, %10.6f]" % (
ra_point, dec_point))
print("^_^ Input camera orientation: %12.6f degree(s)" % image_rot)
print(" ")
# load ccd parameters
xsize, ysize, xchip, ychip, xgap, ygap, xnchip, ynchip = ccdParam()
print("^_^ Pixel range of focal plane: x = [%5d, %5d], y = [%5d, %5d]"%(-xsize/2,xsize/2,-ysize/2,ysize/2))
# wcs
wcs = getTanWCS(ra_point, dec_point, image_rot, pix_scale=0.074)
skyObj = galsim.CelestialCoord(ra=ra_target*galsim.degrees,dec=dec_target*galsim.degrees)
pixObj = wcs.toImage(skyObj)
print("^_^ Pixel range of focal plane: x = [%5d, %5d], y = [%5d, %5d]" % (
-xsize/2, xsize/2, -ysize/2, ysize/2))
# wcs
wcs = getTanWCS(ra_point, dec_point, image_rot, pix_scale=0.074)
skyObj = galsim.CelestialCoord(
ra=ra_target*galsim.degrees, dec=dec_target*galsim.degrees)
pixObj = wcs.toImage(skyObj)
xpixObj = pixObj.x
ypixObj = pixObj.y
print("^_^ Image position of target: [xImage, yImage] = [%9.3f, %9.3f]"%(xpixObj,ypixObj))
print("^_^ Image position of target: [xImage, yImage] = [%9.3f, %9.3f]" % (
xpixObj, ypixObj))
# first determine if the target is in the focal plane
xin = (xpixObj+xsize/2)*(xpixObj-xsize/2)
yin = (ypixObj+ysize/2)*(ypixObj-ysize/2)
if xin>0 or yin>0: raise ValueError("!!! Input target is out of the focal plane")
if xin > 0 or yin > 0:
raise ValueError("!!! Input target is out of the focal plane")
# second determine the location of the target
trigger = False
for i in range(30):
ichip = i+1
ischip = str("0%d"%ichip)[-2:]
fId, fType = getChipFilter(ichip)
ichip = i+1
ischip = str("0%d" % ichip)[-2:]
fId, fType = getChipFilter(ichip)
ix0, ix1, iy0, iy1 = getChipLim(ichip)
ixin = (xpixObj-ix0)*(xpixObj-ix1)
iyin = (ypixObj-iy0)*(ypixObj-iy1)
if ixin<=0 and iyin<=0:
ixin = (xpixObj-ix0)*(xpixObj-ix1)
iyin = (ypixObj-iy0)*(ypixObj-iy1)
if ixin <= 0 and iyin <= 0:
trigger = True
idx = xpixObj - ix0
idy = ypixObj - iy0
idx = xpixObj - ix0
idy = ypixObj - iy0
print(" ---------------------------------------------")
print(" ** Target locates in CHIP#%s with filter %s **"%(ischip,fType))
print(" ** Target position in the chip: [x, y] = [%7.2f, %7.2f]"%(idx, idy))
print(" ** Target locates in CHIP#%s with filter %s **" %
(ischip, fType))
print(
" ** Target position in the chip: [x, y] = [%7.2f, %7.2f]" % (idx, idy))
print(" ---------------------------------------------")
break
if not trigger: print("^|^ Target locates in CCD gap")
if not trigger:
print("^|^ Target locates in CCD gap")
# show the figure
print(" Target on CCD layout is saved into %s"%figout)
print(" Target on CCD layout is saved into %s" % figout)
ccdLayout(xpixObj, ypixObj, figout=figout)
return
def ccdParam():
xt, yt = 59516, 49752
x0, y0 = 9216, 9232
xgap, ygap = (534,1309), 898
xgap, ygap = (534, 1309), 898
xnchip, ynchip = 6, 5
ccdSize = xt, yt, x0, y0, xgap, ygap, xnchip, ynchip
return ccdSize
def getTanWCS(ra, dec, img_rot, pix_scale=0.074):
"""
Get the WCS of the image mosaic using Gnomonic/TAN projection
......@@ -105,40 +118,53 @@ def getTanWCS(ra, dec, img_rot, pix_scale=0.074):
WCS of the focal plane
"""
xcen, ycen = 0, 0
img_rot = img_rot * galsim.degrees
dudx = -np.cos(img_rot.rad) * pix_scale
dudy = -np.sin(img_rot.rad) * pix_scale
dvdx = -np.sin(img_rot.rad) * pix_scale
dvdy = +np.cos(img_rot.rad) * pix_scale
moscen = galsim.PositionD(x=xcen, y=ycen)
sky_center = galsim.CelestialCoord(ra=ra*galsim.degrees, dec=dec*galsim.degrees)
affine = galsim.AffineTransform(dudx, dudy, dvdx, dvdy, origin=moscen)
WCS = galsim.TanWCS(affine, sky_center, units=galsim.arcsec)
img_rot = img_rot * galsim.degrees
dudx = -np.cos(img_rot.rad) * pix_scale
dudy = -np.sin(img_rot.rad) * pix_scale
dvdx = -np.sin(img_rot.rad) * pix_scale
dvdy = +np.cos(img_rot.rad) * pix_scale
moscen = galsim.PositionD(x=xcen, y=ycen)
sky_center = galsim.CelestialCoord(
ra=ra*galsim.degrees, dec=dec*galsim.degrees)
affine = galsim.AffineTransform(dudx, dudy, dvdx, dvdy, origin=moscen)
WCS = galsim.TanWCS(affine, sky_center, units=galsim.arcsec)
return WCS
def getChipFilter(chipID):
"""
Return the filter index and type for a given chip #(chipID)
"""
filter_type_list = ["nuv","u", "g", "r", "i","z","y","GU", "GV", "GI"]
filter_type_list = ["nuv", "u", "g", "r", "i", "z", "y", "GU", "GV", "GI"]
# TODO: maybe a more elegent way other than hard coded?
# e.g. use something like a nested dict:
if chipID in [6, 15, 16, 25]: filter_type = "y"
if chipID in [11, 20]: filter_type = "z"
if chipID in [7, 24]: filter_type = "i"
if chipID in [14, 17]: filter_type = "u"
if chipID in [9, 22]: filter_type = "r"
if chipID in [12, 13, 18, 19]: filter_type = "nuv"
if chipID in [8, 23]: filter_type = "g"
if chipID in [1, 10, 21, 30]: filter_type = "GI"
if chipID in [2, 5, 26, 29]: filter_type = "GV"
if chipID in [3, 4, 27, 28]: filter_type = "GU"
if chipID in [6, 15, 16, 25]:
filter_type = "y"
if chipID in [11, 20]:
filter_type = "z"
if chipID in [7, 24]:
filter_type = "i"
if chipID in [14, 17]:
filter_type = "u"
if chipID in [9, 22]:
filter_type = "r"
if chipID in [12, 13, 18, 19]:
filter_type = "nuv"
if chipID in [8, 23]:
filter_type = "g"
if chipID in [1, 10, 21, 30]:
filter_type = "GI"
if chipID in [2, 5, 26, 29]:
filter_type = "GV"
if chipID in [3, 4, 27, 28]:
filter_type = "GU"
filter_id = filter_type_list.index(filter_type)
return filter_id, filter_type
def getChipLim(chipID):
"""
Calculate the edges in pixel for a given CCD chip on the focal plane
......@@ -173,20 +199,22 @@ def getChipLim(chipID):
return nx0-1, nx1-1, ny0-1, ny1-1
def ccdLayout(xpixTar, ypixTar, figout="ccdLayout.pdf"):
fig = plt.figure(figsize=(10.0,8.0))
ax = fig.add_axes([0.1,0.1,0.80,0.80])
fig = plt.figure(figsize=(10.0, 8.0))
ax = fig.add_axes([0.1, 0.1, 0.80, 0.80])
# plot the layout of the ccd distribution
for i in range(30):
ichip = i+1
fId, fType = getChipFilter(ichip)
ischip = str("0%d"%ichip)[-2:]
ischip = str("0%d" % ichip)[-2:]
ix0, ix1, iy0, iy1 = getChipLim(ichip)
ax.plot([ix0,ix1],[iy0,iy0],"k-", linewidth=2.5)
ax.plot([ix0,ix1],[iy1,iy1],"k-", linewidth=2.5)
ax.plot([ix0,ix0],[iy0,iy1],"k-", linewidth=2.5)
ax.plot([ix1,ix1],[iy0,iy1],"k-", linewidth=2.5)
ax.text(ix0+500,iy0+1500,"%s#%s"%(fType, ischip), fontsize=12, color="grey")
ax.plot([ix0, ix1], [iy0, iy0], "k-", linewidth=2.5)
ax.plot([ix0, ix1], [iy1, iy1], "k-", linewidth=2.5)
ax.plot([ix0, ix0], [iy0, iy1], "k-", linewidth=2.5)
ax.plot([ix1, ix1], [iy0, iy1], "k-", linewidth=2.5)
ax.text(ix0+500, iy0+1500, "%s#%s" %
(fType, ischip), fontsize=12, color="grey")
ax.plot(xpixTar, ypixTar, "r*", ms=12)
ax.set_xlabel("$X\,[\mathrm{pixels}]$", fontsize=20)
ax.set_ylabel("$Y\,[\mathrm{pixels}]$", fontsize=20)
......@@ -194,6 +222,7 @@ def ccdLayout(xpixTar, ypixTar, figout="ccdLayout.pdf"):
ax.axis('off')
plt.savefig(figout)
def parseArguments():
# Create argument parser
parser = argparse.ArgumentParser()
......@@ -203,7 +232,7 @@ def parseArguments():
parser.add_argument("dec_target", type=float)
parser.add_argument("ra_point", type=float)
parser.add_argument("dec_point", type=float)
# Optional arguments
parser.add_argument("-image_rot", type=float, default=-113.4333)
parser.add_argument("-figout", type=str, default="zTargetOnCCD.pdf")
......@@ -213,10 +242,11 @@ def parseArguments():
return args
if __name__ == "__main__":
# Parse the arguments
args = parseArguments()
# Run function
focalPlaneInf(args.ra_target, args.dec_target, args.ra_point, args.dec_point, args.image_rot, args.figout)
focalPlaneInf(args.ra_target, args.dec_target, args.ra_point,
args.dec_point, args.image_rot, args.figout)
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