def testNoise(self):
"""
Test that ExampleCCDNoise puts the expected counts on an image
by generating a flat image, adding noise and background to it,
and calculating the variance of counts in the image.
"""
lsstDefaults = LSSTdefaults()
gain = 2.5
readnoise = 6.0
photParams = PhotometricParameters(gain=gain, readnoise=readnoise)
img = galsim.Image(100, 100)
noise = ExampleCCDNoise(seed=42)
m5 = 24.5
bandpass = Bandpass()
bandpass.readThroughput(
os.path.join(getPackageDir('throughputs'), 'baseline',
'total_r.dat'))
background = calcSkyCountsPerPixelForM5(
m5,
bandpass,
FWHMeff=lsstDefaults.FWHMeff('r'),
photParams=photParams)
noisyImage = noise.addNoiseAndBackground(
img,
bandpass,
m5=m5,
FWHMeff=lsstDefaults.FWHMeff('r'),
photParams=photParams)
mean = 0.0
var = 0.0
for ix in range(1, 101):
for iy in range(1, 101):
mean += noisyImage(ix, iy)
mean = mean / 10000.0
for ix in range(1, 101):
for iy in range(1, 101):
var += (noisyImage(ix, iy) - mean) * (noisyImage(ix, iy) -
mean)
var = var / 9999.0
varElectrons = background * gain + readnoise
varADU = varElectrons / (gain * gain)
msg = 'background %e mean %e ' % (background, mean)
self.assertLess(np.abs(background / mean - 1.0), 0.05, msg=msg)
msg = 'var %e varADU %e ; ratio %e ; background %e' % (
var, varADU, var / varADU, background)
self.assertLess(np.abs(var / varADU - 1.0), 0.05, msg=msg)
def testNoise(self):
"""
Test that ExampleCCDNoise puts the expected counts on an image
by generating a flat image, adding noise and background to it,
and calculating the variance of counts in the image.
"""
lsstDefaults = LSSTdefaults()
gain = 2.5
readnoise = 6.0
photParams=PhotometricParameters(gain=gain, readnoise=readnoise)
img = galsim.Image(100,100)
noise = ExampleCCDNoise(seed=42)
m5 = 24.5
bandpass = Bandpass()
bandpass.readThroughput(os.path.join(lsst.utils.getPackageDir('throughputs'),'baseline','total_r.dat'))
background = calcSkyCountsPerPixelForM5(m5, bandpass, seeing=lsstDefaults.seeing('r'),
photParams=photParams)
noisyImage = noise.addNoiseAndBackground(img, bandpass, m5=m5,
seeing=lsstDefaults.seeing('r'),
photParams=photParams)
mean = 0.0
var = 0.0
for ix in range(1,101):
for iy in range(1,101):
mean += noisyImage(ix, iy)
mean = mean/10000.0
for ix in range(1,101):
for iy in range(1,101):
var += (noisyImage(ix, iy) - mean)*(noisyImage(ix, iy) - mean)
var = var/9999.0
varElectrons = background*gain + readnoise
varADU = varElectrons/(gain*gain)
msg = 'background %e mean %e ' % (background, mean)
self.assertTrue(numpy.abs(background/mean - 1.0) < 0.05, msg=msg)
msg = 'var %e varADU %e ; ratio %e ; background %e' % (var, varADU, var/varADU, background)
self.assertTrue(numpy.abs(var/varADU - 1.0) < 0.05, msg=msg)
class testGalSimGalaxiesNoisy(testGalSimGalaxiesNoiseless):
#defined in galSimInterface/galSimUtilities.py
noise_and_background = ExampleCCDNoise(99)
class testGalSimStarsWithNoise(testGalSimStarsNoiseless):
#defined in galSimInterface/galSimUtilities.py
noise_and_background = ExampleCCDNoise(seed=99)
class noisyCatalog(testGalaxyCatalog):
"""
Adds a noise and sky background wrapper to testGalaxyCatalog
"""
PSF = SNRdocumentPSF()
noise_and_background = ExampleCCDNoise(seed=42)
class backgroundCatalog(testGalaxyCatalog):
"""
Add sky background but no noise to testGalaxyCatalog
"""
PSF = SNRdocumentPSF()
noise_and_background = ExampleCCDNoise(addNoise=False, seed=42)