Commit 64c00169 authored by Fang Yuedong's avatar Fang Yuedong
Browse files

add unittest script for detect effects

parent e0fa93a6
......@@ -41,7 +41,7 @@ class Catalog_example(CatalogBase):
config : dict
configuration dictionary which is parsed from the input YAML file
chip: ObservationSim.Instrument.Chip
a ObservationSim.Instrument.Chip object, can be used to identify the band etc.
a ObservationSim.Instrument.Chip instance, can be used to identify the band etc.
**kwargs : dict
other needed input parameters (in key-value pairs), please modify corresponding
initialization call in "ObservationSim.py" as you need.
......
import unittest
import numpy as np
from ObservationSim.Instrument.Chip import Effects
import galsim
import matplotlib.pyplot as plt
import os,sys,math,copy
from numpy.random import Generator, PCG64
import warnings
warnings.filterwarnings("ignore", '.*Numba.*',)
width = 9216
height = 9232
if os.path.isdir('./output/'):
pass
else:
os.mkdir('./output/')
class DetTest(unittest.TestCase):
def test_prnu(self):
'''
Unit test for PRNU. Expected result: a randomized GS image contains PRNU with sigma=0.01, mean=1.
'''
print('PRNU Test:')
sigma = 0.01
seed = 20210911
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(' PRNU Image Array:')
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
def test_dark(self):
'''
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)))
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)
print('\nUnit test for dark current has been passed.')
del img
def test_satu(self):
'''
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))
# 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('output','test_satu_initimg.fits')
img.write(filename1)
newimg = Effects.SaturBloom(img, fullwell=9e4)
# plt.imshow(newimg.array)
# plt.show()
filename2 = os.path.join('output','test_satu_bleedimg.fits')
newimg.write(filename2)
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))
img = galsim.Image(copy.deepcopy(imgarr))
filename1 = os.path.join('output','test_nonlinear_initimg.fits')
img.write(filename1)
newimg = Effects.NonLinearity(img, beta1=5E-7, beta2=0)
filename2 = os.path.join('output','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.xlabel('input (e-)')
plt.ylabel('output (e-)')
plt.savefig('./output/test_nonlinearity.png', dpi=200)
plt.show()
del img,newimg,imgarr
def test_badpixel_HtrDtr(self):
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('./output/test_badpixel_HtrDtr.fits')
del img
def test_badpixel_HfsDtr(self):
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('./output/test_badpixel_HfsDtr.fits')
del img
def test_badpixel_HtrDfs(self):
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('./output/test_badpixel_HtrDfs.fits')
del img
def test_badpixel_HfsDfs(self):
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('./output/test_badpixel_HfsDfs.fits')
del img
def test_badlines(self):
img = galsim.Image(500,500,init_value=-1000)
img.addNoise(galsim.GaussianNoise(sigma=7))
newimg = Effects.BadColumns(copy.deepcopy(img), seed=20210911)
newimg.write('./output/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.write('./output/test_ctecol.fits')
newimgrow.write('./output/test_cterow.fits')
del img,newimgcol,newimgrow
def test_readnoise(self):
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('./output/test_readnoise.fits')
stdval = np.std(img.array)
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.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("./output/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)
print('\nUnit test for applying 16 channel gains has been passed.')
del img
def test_cosmicray(self):
attachedSizes = np.loadtxt('../ObservationSim/Instrument/Chip/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('./output/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 *= shuttimg
img.write('./output/test_shutter.fits')
del img
def test_vignette(self):
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)
flat_normal = flat_img / np.mean(flat_img.array)
flat_normal.write('./output/test_vignette.fits')
del flat_img,img,flat_normal
if __name__ == '__main__':
unittest.main()
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