add unittest script for detect effects

Catalog/Catalog_example.py

@@ -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.

tests/det_effect_unit_test.py

+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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