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test_noise.py
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test_noise.py
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# Copyright (c) 2012-2018 by the GalSim developers team on GitHub
# https://github.com/GalSim-developers
#
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# https://github.com/GalSim-developers/GalSim
#
# GalSim is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
#
from __future__ import print_function
import numpy as np
import os
import sys
import galsim
from galsim_test_helpers import *
testseed = 1000
precision = 10
# decimal point at which agreement is required for all double precision tests
precisionD = precision
precisionF = 5 # precision=10 does not make sense at single precision
precisionS = 1 # "precision" also a silly concept for ints, but allows all 4 tests to run in one go
precisionI = 1
@timer
def test_deviate_noise():
"""Test basic functionality of the DeviateNoise class
"""
u = galsim.UniformDeviate(testseed)
uResult = np.empty((10,10))
u.generate(uResult)
noise = galsim.DeviateNoise(galsim.UniformDeviate(testseed))
# Test filling an image with random values
testimage = galsim.ImageD(10,10)
testimage.addNoise(noise)
np.testing.assert_array_almost_equal(
testimage.array, uResult, precision,
err_msg='Wrong uniform random number sequence generated when applied to image.')
# Test filling a single-precision image
noise.rng.seed(testseed)
testimage = galsim.ImageF(10,10)
testimage.addNoise(noise)
np.testing.assert_array_almost_equal(
testimage.array, uResult, precisionF,
err_msg='Wrong uniform random number sequence generated when applied to ImageF.')
# Test filling an image with Fortran ordering
noise.rng.seed(testseed)
testimage = galsim.ImageD(np.zeros((10,10)).T)
testimage.addNoise(noise)
np.testing.assert_array_almost_equal(
testimage.array, uResult, precision,
err_msg="Wrong uniform randoms generated for Fortran-ordered Image")
# Check picklability
do_pickle(noise, drawNoise)
do_pickle(noise)
# Check copy, eq and ne
noise2 = galsim.DeviateNoise(noise.rng.duplicate()) # Separate but equivalent rng chain.
noise3 = noise.copy() # Always has exactly the same rng as noise.
noise4 = noise.copy(rng=galsim.BaseDeviate(11)) # Always has a different rng than noise
assert noise == noise2
assert noise == noise3
assert noise != noise4
assert noise.rng() == noise2.rng()
assert noise == noise2 # Still equal because both chains incremented one place.
assert noise == noise3 # Still equal because noise 3's rng is always equal to noise's rng.
noise.rng()
assert noise2 != noise3 # This is no longer equal, since only noise.rng is incremented.
assert noise == noise3
assert_raises(TypeError, galsim.DeviateNoise, 53)
assert_raises(NotImplementedError, galsim.BaseNoise().getVariance)
assert_raises(NotImplementedError, galsim.BaseNoise().withVariance, 23)
assert_raises(NotImplementedError, galsim.BaseNoise().withScaledVariance, 23)
assert_raises(TypeError, noise.applyTo, 23)
assert_raises(NotImplementedError, galsim.BaseNoise().applyTo, testimage)
assert_raises(galsim.GalSimError, noise.getVariance)
assert_raises(galsim.GalSimError, noise.withVariance, 23)
assert_raises(galsim.GalSimError, noise.withScaledVariance, 23)
@timer
def test_gaussian_noise():
"""Test Gaussian random number generator
"""
gSigma = 17.23
g = galsim.GaussianDeviate(testseed, sigma=gSigma)
gResult = np.empty((10,10))
g.generate(gResult)
noise = galsim.DeviateNoise(g)
# Test filling an image
testimage = galsim.ImageD(10,10)
noise.rng.seed(testseed)
testimage.addNoise(noise)
np.testing.assert_array_almost_equal(
testimage.array, gResult, precision,
err_msg='Wrong Gaussian random number sequence generated when applied to image.')
# Test filling a single-precision image
noise.rng.seed(testseed)
testimage = galsim.ImageF(10,10)
testimage.addNoise(noise)
np.testing.assert_array_almost_equal(
testimage.array, gResult, precisionF,
err_msg='Wrong Gaussian random number sequence generated when applied to ImageF.')
# GaussianNoise is equivalent, but no mean allowed.
gn = galsim.GaussianNoise(galsim.BaseDeviate(testseed), sigma=gSigma)
testimage = galsim.ImageD(10,10)
testimage.addNoise(gn)
np.testing.assert_array_almost_equal(
testimage.array, gResult, precision,
err_msg="GaussianNoise applied to Images does not reproduce expected sequence")
# Test filling an image with Fortran ordering
gn.rng.seed(testseed)
testimage = galsim.ImageD(np.zeros((10,10)).T)
testimage.addNoise(gn)
np.testing.assert_array_almost_equal(
testimage.array, gResult, precision,
err_msg="Wrong Gaussian noise generated for Fortran-ordered Image")
# Check GaussianNoise variance:
np.testing.assert_almost_equal(
gn.getVariance(), gSigma**2, precision,
err_msg="GaussianNoise getVariance returns wrong variance")
np.testing.assert_almost_equal(
gn.sigma, gSigma, precision,
err_msg="GaussianNoise sigma returns wrong value")
# Check that the noise model really does produce this variance.
big_im = galsim.Image(2048,2048,dtype=float)
gn.rng.seed(testseed)
big_im.addNoise(gn)
var = np.var(big_im.array)
print('variance = ',var)
print('getVar = ',gn.getVariance())
np.testing.assert_almost_equal(
var, gn.getVariance(), 1,
err_msg='Realized variance for GaussianNoise did not match getVariance()')
# Check that GaussianNoise adds to the image, not overwrites the image.
gal = galsim.Exponential(half_light_radius=2.3, flux=1.e4)
gal.drawImage(image=big_im)
gn.rng.seed(testseed)
big_im.addNoise(gn)
gal.withFlux(-1.e4).drawImage(image=big_im, add_to_image=True)
var = np.var(big_im.array)
np.testing.assert_almost_equal(
var, gn.getVariance(), 1,
err_msg='GaussianNoise wrong when already an object drawn on the image')
# Check that DeviateNoise adds to the image, not overwrites the image.
gal.drawImage(image=big_im)
gn.rng.seed(testseed)
big_im.addNoise(gn)
gal.withFlux(-1.e4).drawImage(image=big_im, add_to_image=True)
var = np.var(big_im.array)
np.testing.assert_almost_equal(
var, gn.getVariance(), 1,
err_msg='DeviateNoise wrong when already an object drawn on the image')
# Check withVariance
gn = gn.withVariance(9.)
np.testing.assert_almost_equal(
gn.getVariance(), 9, precision,
err_msg="GaussianNoise withVariance results in wrong variance")
np.testing.assert_almost_equal(
gn.sigma, 3., precision,
err_msg="GaussianNoise withVariance results in wrong sigma")
# Check withScaledVariance
gn = gn.withScaledVariance(4.)
np.testing.assert_almost_equal(
gn.getVariance(), 36., precision,
err_msg="GaussianNoise withScaledVariance results in wrong variance")
np.testing.assert_almost_equal(
gn.sigma, 6., precision,
err_msg="GaussianNoise withScaledVariance results in wrong sigma")
# Check arithmetic
gn = gn.withVariance(0.5)
gn2 = gn * 3
np.testing.assert_almost_equal(
gn2.getVariance(), 1.5, precision,
err_msg="GaussianNoise gn*3 results in wrong variance")
np.testing.assert_almost_equal(
gn.getVariance(), 0.5, precision,
err_msg="GaussianNoise gn*3 results in wrong variance for original gn")
gn2 = 5 * gn
np.testing.assert_almost_equal(
gn2.getVariance(), 2.5, precision,
err_msg="GaussianNoise 5*gn results in wrong variance")
np.testing.assert_almost_equal(
gn.getVariance(), 0.5, precision,
err_msg="GaussianNoise 5*gn results in wrong variance for original gn")
gn2 = gn/2
np.testing.assert_almost_equal(
gn2.getVariance(), 0.25, precision,
err_msg="GaussianNoise gn/2 results in wrong variance")
np.testing.assert_almost_equal(
gn.getVariance(), 0.5, precision,
err_msg="GaussianNoise 5*gn results in wrong variance for original gn")
gn *= 3
np.testing.assert_almost_equal(
gn.getVariance(), 1.5, precision,
err_msg="GaussianNoise gn*=3 results in wrong variance")
gn /= 2
np.testing.assert_almost_equal(
gn.getVariance(), 0.75, precision,
err_msg="GaussianNoise gn/=2 results in wrong variance")
# Check starting with GaussianNoise()
gn2 = galsim.GaussianNoise()
gn2 = gn2.withVariance(9.)
np.testing.assert_almost_equal(
gn2.getVariance(), 9, precision,
err_msg="GaussianNoise().withVariance results in wrong variance")
np.testing.assert_almost_equal(
gn2.sigma, 3., precision,
err_msg="GaussianNoise().withVariance results in wrong sigma")
gn2 = galsim.GaussianNoise()
gn2 = gn2.withScaledVariance(4.)
np.testing.assert_almost_equal(
gn2.getVariance(), 4., precision,
err_msg="GaussianNoise().withScaledVariance results in wrong variance")
np.testing.assert_almost_equal(
gn2.sigma, 2., precision,
err_msg="GaussianNoise().withScaledVariance results in wrong sigma")
# Check picklability
do_pickle(gn, lambda x: (x.rng.serialize(), x.sigma))
do_pickle(gn, drawNoise)
do_pickle(gn)
# Check copy, eq and ne
gn = gn.withVariance(gSigma**2)
gn2 = galsim.GaussianNoise(gn.rng.duplicate(), gSigma)
gn3 = gn.copy()
gn4 = gn.copy(rng=galsim.BaseDeviate(11))
gn5 = galsim.GaussianNoise(gn.rng, 2.*gSigma)
assert gn == gn2
assert gn == gn3
assert gn != gn4
assert gn != gn5
assert gn.rng.raw() == gn2.rng.raw()
assert gn == gn2
assert gn == gn3
gn.rng.raw()
assert gn != gn2
assert gn == gn3
@timer
def test_variable_gaussian_noise():
"""Test VariableGaussian random number generator
"""
# Make a checkerboard image with two values for the variance
gSigma1 = 17.23
gSigma2 = 28.55
var_image = galsim.ImageD(galsim.BoundsI(0,9,0,9))
coords = np.ogrid[0:10, 0:10]
var_image.array[ (coords[0] + coords[1]) % 2 == 1 ] = gSigma1**2
var_image.array[ (coords[0] + coords[1]) % 2 == 0 ] = gSigma2**2
print('var_image.array = ',var_image.array)
g = galsim.GaussianDeviate(testseed, sigma=1.)
vgResult = np.empty((10,10))
g.generate(vgResult)
vgResult *= np.sqrt(var_image.array)
# Test filling an image
vgn = galsim.VariableGaussianNoise(galsim.BaseDeviate(testseed), var_image)
testimage = galsim.ImageD(10,10)
testimage.addNoise(vgn)
np.testing.assert_array_almost_equal(
testimage.array, vgResult, precision,
err_msg="VariableGaussianNoise applied to Images does not reproduce expected sequence")
# Test filling an image with Fortran ordering
vgn.rng.seed(testseed)
testimage = galsim.ImageD(np.zeros((10,10)).T)
testimage.addNoise(vgn)
np.testing.assert_array_almost_equal(
testimage.array, vgResult, precision,
err_msg="Wrong VariableGaussian noise generated for Fortran-ordered Image")
# Check var_image property
np.testing.assert_almost_equal(
vgn.var_image.array, var_image.array, precision,
err_msg="VariableGaussianNoise var_image returns wrong var_image")
# Check that the noise model really does produce this variance.
big_var_image = galsim.ImageD(galsim.BoundsI(0,2047,0,2047))
big_coords = np.ogrid[0:2048, 0:2048]
mask1 = (big_coords[0] + big_coords[1]) % 2 == 0
mask2 = (big_coords[0] + big_coords[1]) % 2 == 1
big_var_image.array[mask1] = gSigma1**2
big_var_image.array[mask2] = gSigma2**2
big_vgn = galsim.VariableGaussianNoise(galsim.BaseDeviate(testseed), big_var_image)
big_im = galsim.Image(2048,2048,dtype=float)
big_im.addNoise(big_vgn)
var = np.var(big_im.array)
print('variance = ',var)
print('getVar = ',big_vgn.var_image.array.mean())
np.testing.assert_almost_equal(
var, big_vgn.var_image.array.mean(), 1,
err_msg='Realized variance for VariableGaussianNoise did not match var_image')
# Check realized variance in each mask
print('rms1 = ',np.std(big_im.array[mask1]))
print('rms2 = ',np.std(big_im.array[mask2]))
np.testing.assert_almost_equal(np.std(big_im.array[mask1]), gSigma1, decimal=1)
np.testing.assert_almost_equal(np.std(big_im.array[mask2]), gSigma2, decimal=1)
# Check that VariableGaussianNoise adds to the image, not overwrites the image.
gal = galsim.Exponential(half_light_radius=2.3, flux=1.e4)
gal.drawImage(image=big_im)
big_vgn.rng.seed(testseed)
big_im.addNoise(big_vgn)
gal.withFlux(-1.e4).drawImage(image=big_im, add_to_image=True)
var = np.var(big_im.array)
np.testing.assert_almost_equal(
var, big_vgn.var_image.array.mean(), 1,
err_msg='VariableGaussianNoise wrong when already an object drawn on the image')
# Check picklability
do_pickle(vgn, lambda x: (x.rng.serialize(), x.var_image))
do_pickle(vgn, drawNoise)
do_pickle(vgn)
# Check copy, eq and ne
vgn2 = galsim.VariableGaussianNoise(vgn.rng.duplicate(), var_image)
vgn3 = vgn.copy()
vgn4 = vgn.copy(rng=galsim.BaseDeviate(11))
vgn5 = galsim.VariableGaussianNoise(vgn.rng, 2.*var_image)
assert vgn == vgn2
assert vgn == vgn3
assert vgn != vgn4
assert vgn != vgn5
assert vgn.rng.raw() == vgn2.rng.raw()
assert vgn == vgn2
assert vgn == vgn3
vgn.rng.raw()
assert vgn != vgn2
assert vgn == vgn3
assert_raises(TypeError, vgn.applyTo, 23)
assert_raises(ValueError, vgn.applyTo, galsim.ImageF(3,3))
assert_raises(galsim.GalSimError, vgn.getVariance)
assert_raises(galsim.GalSimError, vgn.withVariance, 23)
assert_raises(galsim.GalSimError, vgn.withScaledVariance, 23)
@timer
def test_poisson_noise():
"""Test Poisson random number generator
"""
pMean = 17
p = galsim.PoissonDeviate(testseed, mean=pMean)
pResult = np.empty((10,10))
p.generate(pResult)
noise = galsim.DeviateNoise(p)
# Test filling an image
noise.rng.seed(testseed)
testimage = galsim.ImageI(10, 10)
testimage.addNoise(galsim.DeviateNoise(p))
np.testing.assert_array_equal(
testimage.array, pResult,
err_msg='Wrong poisson random number sequence generated when applied to image.')
# The PoissonNoise version also subtracts off the mean value
pn = galsim.PoissonNoise(galsim.BaseDeviate(testseed), sky_level=pMean)
testimage.fill(0)
testimage.addNoise(pn)
np.testing.assert_array_equal(
testimage.array, pResult-pMean,
err_msg='Wrong poisson random number sequence generated using PoissonNoise')
# Test filling a single-precision image
pn.rng.seed(testseed)
testimage = galsim.ImageF(10,10)
testimage.addNoise(pn)
np.testing.assert_array_almost_equal(
testimage.array, pResult-pMean, precisionF,
err_msg='Wrong Poisson random number sequence generated when applied to ImageF.')
# Test filling an image with Fortran ordering
pn.rng.seed(testseed)
testimage = galsim.ImageD(10,10)
testimage.addNoise(pn)
np.testing.assert_array_almost_equal(
testimage.array, pResult-pMean,
err_msg="Wrong Poisson noise generated for Fortran-ordered Image")
# Check PoissonNoise variance:
np.testing.assert_almost_equal(
pn.getVariance(), pMean, precision,
err_msg="PoissonNoise getVariance returns wrong variance")
np.testing.assert_almost_equal(
pn.sky_level, pMean, precision,
err_msg="PoissonNoise sky_level returns wrong value")
# Check that the noise model really does produce this variance.
big_im = galsim.Image(2048,2048,dtype=float)
big_im.addNoise(pn)
var = np.var(big_im.array)
print('variance = ',var)
print('getVar = ',pn.getVariance())
np.testing.assert_almost_equal(
var, pn.getVariance(), 1,
err_msg='Realized variance for PoissonNoise did not match getVariance()')
# Check that PoissonNoise adds to the image, not overwrites the image.
gal = galsim.Exponential(half_light_radius=2.3, flux=0.3)
# Note: in this case, flux/size^2 needs to be << sky_level or it will mess up the statistics.
gal.drawImage(image=big_im)
big_im.addNoise(pn)
gal.withFlux(-0.3).drawImage(image=big_im, add_to_image=True)
var = np.var(big_im.array)
np.testing.assert_almost_equal(
var, pn.getVariance(), 1,
err_msg='PoissonNoise wrong when already an object drawn on the image')
# Check withVariance
pn = pn.withVariance(9.)
np.testing.assert_almost_equal(
pn.getVariance(), 9., precision,
err_msg="PoissonNoise withVariance results in wrong variance")
np.testing.assert_almost_equal(
pn.sky_level, 9., precision,
err_msg="PoissonNoise withVariance results in wrong sky_level")
# Check withScaledVariance
pn = pn.withScaledVariance(4.)
np.testing.assert_almost_equal(
pn.getVariance(), 36, precision,
err_msg="PoissonNoise withScaledVariance results in wrong variance")
np.testing.assert_almost_equal(
pn.sky_level, 36., precision,
err_msg="PoissonNoise withScaledVariance results in wrong sky_level")
# Check arithmetic
pn = pn.withVariance(0.5)
pn2 = pn * 3
np.testing.assert_almost_equal(
pn2.getVariance(), 1.5, precision,
err_msg="PoissonNoise pn*3 results in wrong variance")
np.testing.assert_almost_equal(
pn.getVariance(), 0.5, precision,
err_msg="PoissonNoise pn*3 results in wrong variance for original pn")
pn2 = 5 * pn
np.testing.assert_almost_equal(
pn2.getVariance(), 2.5, precision,
err_msg="PoissonNoise 5*pn results in wrong variance")
np.testing.assert_almost_equal(
pn.getVariance(), 0.5, precision,
err_msg="PoissonNoise 5*pn results in wrong variance for original pn")
pn2 = pn/2
np.testing.assert_almost_equal(
pn2.getVariance(), 0.25, precision,
err_msg="PoissonNoise pn/2 results in wrong variance")
np.testing.assert_almost_equal(
pn.getVariance(), 0.5, precision,
err_msg="PoissonNoise 5*pn results in wrong variance for original pn")
pn *= 3
np.testing.assert_almost_equal(
pn.getVariance(), 1.5, precision,
err_msg="PoissonNoise pn*=3 results in wrong variance")
pn /= 2
np.testing.assert_almost_equal(
pn.getVariance(), 0.75, precision,
err_msg="PoissonNoise pn/=2 results in wrong variance")
# Check starting with PoissonNoise()
pn = galsim.PoissonNoise()
pn = pn.withVariance(9.)
np.testing.assert_almost_equal(
pn.getVariance(), 9., precision,
err_msg="PoissonNoise().withVariance results in wrong variance")
np.testing.assert_almost_equal(
pn.sky_level, 9., precision,
err_msg="PoissonNoise().withVariance results in wrong sky_level")
pn = pn.withScaledVariance(4.)
np.testing.assert_almost_equal(
pn.getVariance(), 36, precision,
err_msg="PoissonNoise().withScaledVariance results in wrong variance")
np.testing.assert_almost_equal(
pn.sky_level, 36., precision,
err_msg="PoissonNoise().withScaledVariance results in wrong sky_level")
# Check picklability
do_pickle(pn, lambda x: (x.rng.serialize(), x.sky_level))
do_pickle(pn, drawNoise)
do_pickle(pn)
# Check copy, eq and ne
pn = pn.withVariance(pMean)
pn2 = galsim.PoissonNoise(pn.rng.duplicate(), pMean)
pn3 = pn.copy()
pn4 = pn.copy(rng=galsim.BaseDeviate(11))
pn5 = galsim.PoissonNoise(pn.rng, 2*pMean)
assert pn == pn2
assert pn == pn3
assert pn != pn4
assert pn != pn5
assert pn.rng.raw() == pn2.rng.raw()
assert pn == pn2
assert pn == pn3
pn.rng.raw()
assert pn != pn2
assert pn == pn3
@timer
def test_ccdnoise():
"""Test CCD Noise generator
"""
# Start with some regression tests where we have known values that we expect to generate:
types = (np.int16, np.int32, np.float32, np.float64)
typestrings = ("S", "I", "F", "D")
testseed = 1000
gain = 3.
read_noise = 5.
sky = 50
# Tabulated results for the above settings and testseed value.
cResultS = np.array([[44, 47], [50, 49]], dtype=np.int16)
cResultI = np.array([[44, 47], [50, 49]], dtype=np.int32)
cResultF = np.array([[44.45332718, 47.79725266], [50.67744064, 49.58272934]], dtype=np.float32)
cResultD = np.array([[44.453328440057618, 47.797254142519577],
[50.677442088335162, 49.582730949808081]],dtype=np.float64)
for i in range(4):
prec = eval("precision"+typestrings[i])
cResult = eval("cResult"+typestrings[i])
rng = galsim.BaseDeviate(testseed)
ccdnoise = galsim.CCDNoise(rng, gain=gain, read_noise=read_noise)
testImage = galsim.Image((np.zeros((2, 2))+sky).astype(types[i]))
ccdnoise.applyTo(testImage)
np.testing.assert_array_almost_equal(
testImage.array, cResult, prec,
err_msg="Wrong CCD noise random sequence generated for Image"+typestrings[i]+".")
# Check that reseeding the rng reseeds the internal deviate in CCDNoise
rng.seed(testseed)
testImage.fill(sky)
ccdnoise.applyTo(testImage)
np.testing.assert_array_almost_equal(
testImage.array, cResult, prec,
err_msg="Wrong CCD noise random sequence generated for Image"+typestrings[i]+
" after seed")
# Check using addNoise
rng.seed(testseed)
testImage.fill(sky)
testImage.addNoise(ccdnoise)
np.testing.assert_array_almost_equal(
testImage.array, cResult, prec,
err_msg="Wrong CCD noise random sequence generated for Image"+typestrings[i]+
" using addNoise")
# Test filling an image with Fortran ordering
rng.seed(testseed)
testImageF = galsim.Image(np.zeros((2, 2)).T, dtype=types[i])
testImageF.fill(sky)
testImageF.addNoise(ccdnoise)
np.testing.assert_array_almost_equal(
testImageF.array, cResult, prec,
err_msg="Wrong CCD noise generated for Fortran-ordered Image"+typestrings[i])
# Now include sky_level in ccdnoise
rng.seed(testseed)
ccdnoise = galsim.CCDNoise(rng, sky_level=sky, gain=gain, read_noise=read_noise)
testImage.fill(0)
ccdnoise.applyTo(testImage)
np.testing.assert_array_almost_equal(
testImage.array, cResult-sky, prec,
err_msg="Wrong CCD noise random sequence generated for Image"+typestrings[i]+
" with sky_level included in noise")
rng.seed(testseed)
testImage.fill(0)
testImage.addNoise(ccdnoise)
np.testing.assert_array_almost_equal(
testImage.array, cResult-sky, prec,
err_msg="Wrong CCD noise random sequence generated for Image"+typestrings[i]+
" using addNoise with sky_level included in noise")
# Check CCDNoise variance:
var1 = sky/gain + (read_noise/gain)**2
np.testing.assert_almost_equal(
ccdnoise.getVariance(), var1, precision,
err_msg="CCDNoise getVariance returns wrong variance")
np.testing.assert_almost_equal(
ccdnoise.sky_level, sky, precision,
err_msg="CCDNoise sky_level returns wrong value")
np.testing.assert_almost_equal(
ccdnoise.gain, gain, precision,
err_msg="CCDNoise gain returns wrong value")
np.testing.assert_almost_equal(
ccdnoise.read_noise, read_noise, precision,
err_msg="CCDNoise read_noise returns wrong value")
# Check that the noise model really does produce this variance.
# NB. If default float32 is used here, older versions of numpy will compute the variance
# in single precision, and with 2048^2 values, the final answer comes out significantly
# wrong (19.33 instead of 19.42, which gets compared to the nominal value of 19.44).
big_im = galsim.Image(2048,2048,dtype=float)
big_im.addNoise(ccdnoise)
var = np.var(big_im.array)
print('variance = ',var)
print('getVar = ',ccdnoise.getVariance())
np.testing.assert_almost_equal(
var, ccdnoise.getVariance(), 1,
err_msg='Realized variance for CCDNoise did not match getVariance()')
# Check that CCDNoise adds to the image, not overwrites the image.
gal = galsim.Exponential(half_light_radius=2.3, flux=0.3)
# Note: again, flux/size^2 needs to be << sky_level or it will mess up the statistics.
gal.drawImage(image=big_im)
big_im.addNoise(ccdnoise)
gal.withFlux(-0.3).drawImage(image=big_im, add_to_image=True)
var = np.var(big_im.array)
np.testing.assert_almost_equal(
var, ccdnoise.getVariance(), 1,
err_msg='CCDNoise wrong when already an object drawn on the image')
# Check using a non-integer sky level which does some slightly different calculations.
rng.seed(testseed)
big_im_int = galsim.Image(2048,2048,dtype=int)
ccdnoise = galsim.CCDNoise(rng, sky_level=34.42, gain=1.6, read_noise=11.2)
big_im_int.fill(0)
big_im_int.addNoise(ccdnoise)
var = np.var(big_im_int.array)
np.testing.assert_almost_equal(var/ccdnoise.getVariance(), 1., decimal=2,
err_msg='CCDNoise wrong when sky_level is not an integer')
# Using gain=0 means the read_noise is in ADU, not e-
rng.seed(testseed)
ccdnoise = galsim.CCDNoise(rng, gain=0., read_noise=read_noise)
var2 = read_noise**2
np.testing.assert_almost_equal(
ccdnoise.getVariance(), var2, precision,
err_msg="CCDNoise getVariance returns wrong variance with gain=0")
np.testing.assert_almost_equal(
ccdnoise.sky_level, 0., precision,
err_msg="CCDNoise sky_level returns wrong value with gain=0")
np.testing.assert_almost_equal(
ccdnoise.gain, 0., precision,
err_msg="CCDNoise gain returns wrong value with gain=0")
np.testing.assert_almost_equal(
ccdnoise.read_noise, read_noise, precision,
err_msg="CCDNoise read_noise returns wrong value with gain=0")
big_im.fill(0)
big_im.addNoise(ccdnoise)
var = np.var(big_im.array)
np.testing.assert_almost_equal(var, ccdnoise.getVariance(), 1,
err_msg='CCDNoise wrong when gain=0')
# Check withVariance
ccdnoise = galsim.CCDNoise(rng, sky_level=sky, gain=gain, read_noise=read_noise)
ccdnoise = ccdnoise.withVariance(9.)
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 9., precision,
err_msg="CCDNoise withVariance results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.sky_level, (9./var1)*sky, precision,
err_msg="CCDNoise withVariance results in wrong sky_level")
np.testing.assert_almost_equal(
ccdnoise.gain, gain, precision,
err_msg="CCDNoise withVariance results in wrong gain")
np.testing.assert_almost_equal(
ccdnoise.read_noise, np.sqrt(9./var1) * read_noise, precision,
err_msg="CCDNoise withVariance results in wrong ReadNoise")
# Check withScaledVariance
ccdnoise = ccdnoise.withScaledVariance(4.)
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 36., precision,
err_msg="CCDNoise withVariance results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.sky_level, (36./var1)*sky, precision,
err_msg="CCDNoise withVariance results in wrong sky_level")
np.testing.assert_almost_equal(
ccdnoise.gain, gain, precision,
err_msg="CCDNoise withVariance results in wrong gain")
np.testing.assert_almost_equal(
ccdnoise.read_noise, np.sqrt(36./var1) * read_noise, precision,
err_msg="CCDNoise withVariance results in wrong ReadNoise")
# Check arithmetic
ccdnoise = ccdnoise.withVariance(0.5)
ccdnoise2 = ccdnoise * 3
np.testing.assert_almost_equal(
ccdnoise2.getVariance(), 1.5, precision,
err_msg="CCDNoise ccdnoise*3 results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 0.5, precision,
err_msg="CCDNoise ccdnoise*3 results in wrong variance for original ccdnoise")
ccdnoise2 = 5 * ccdnoise
np.testing.assert_almost_equal(
ccdnoise2.getVariance(), 2.5, precision,
err_msg="CCDNoise 5*ccdnoise results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 0.5, precision,
err_msg="CCDNoise 5*ccdnoise results in wrong variance for original ccdnoise")
ccdnoise2 = ccdnoise/2
np.testing.assert_almost_equal(
ccdnoise2.getVariance(), 0.25, precision,
err_msg="CCDNoise ccdnoise/2 results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 0.5, precision,
err_msg="CCDNoise 5*ccdnoise results in wrong variance for original ccdnoise")
ccdnoise *= 3
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 1.5, precision,
err_msg="CCDNoise ccdnoise*=3 results in wrong variance")
ccdnoise /= 2
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 0.75, precision,
err_msg="CCDNoise ccdnoise/=2 results in wrong variance")
# Check starting with CCDNoise()
ccdnoise = galsim.CCDNoise()
ccdnoise = ccdnoise.withVariance(9.)
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 9., precision,
err_msg="CCDNoise().withVariance results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.sky_level, 9., precision,
err_msg="CCDNoise().withVariance results in wrong sky_level")
np.testing.assert_almost_equal(
ccdnoise.gain, 1., precision,
err_msg="CCDNoise().withVariance results in wrong gain")
np.testing.assert_almost_equal(
ccdnoise.read_noise, 0., precision,
err_msg="CCDNoise().withVariance results in wrong ReadNoise")
ccdnoise = ccdnoise.withScaledVariance(4.)
np.testing.assert_almost_equal(
ccdnoise.getVariance(), 36., precision,
err_msg="CCDNoise().withScaledVariance results in wrong variance")
np.testing.assert_almost_equal(
ccdnoise.sky_level, 36., precision,
err_msg="CCDNoise().withScaledVariance results in wrong sky_level")
np.testing.assert_almost_equal(
ccdnoise.gain, 1., precision,
err_msg="CCDNoise().withScaledVariance results in wrong gain")
np.testing.assert_almost_equal(
ccdnoise.read_noise, 0., precision,
err_msg="CCDNoise().withScaledVariance results in wrong ReadNoise")
# Check picklability
do_pickle(ccdnoise, lambda x: (x.rng.serialize(), x.sky_level, x.gain, x.read_noise))
do_pickle(ccdnoise, drawNoise)
do_pickle(ccdnoise)
# Check copy, eq and ne
ccdnoise = galsim.CCDNoise(rng, sky, gain, read_noise)
ccdnoise2 = galsim.CCDNoise(ccdnoise.rng.duplicate(), gain=gain, read_noise=read_noise,
sky_level=sky)
ccdnoise3 = ccdnoise.copy()
ccdnoise4 = ccdnoise.copy(rng=galsim.BaseDeviate(11))
ccdnoise5 = galsim.CCDNoise(ccdnoise.rng, gain=2*gain, read_noise=read_noise, sky_level=sky)
ccdnoise6 = galsim.CCDNoise(ccdnoise.rng, gain=gain, read_noise=2*read_noise, sky_level=sky)
ccdnoise7 = galsim.CCDNoise(ccdnoise.rng, gain=gain, read_noise=read_noise, sky_level=2*sky)
assert ccdnoise == ccdnoise2
assert ccdnoise == ccdnoise3
assert ccdnoise != ccdnoise4
assert ccdnoise != ccdnoise5
assert ccdnoise != ccdnoise6
assert ccdnoise != ccdnoise7
assert ccdnoise.rng.raw() == ccdnoise2.rng.raw()
assert ccdnoise == ccdnoise2
assert ccdnoise == ccdnoise3
ccdnoise.rng.raw()
assert ccdnoise != ccdnoise2
assert ccdnoise == ccdnoise3
@timer
def test_addnoisesnr():
"""Test that addNoiseSNR is behaving sensibly.
"""
# Rather than reproducing the S/N calculation in addNoiseSNR(), we'll just check for
# self-consistency of the behavior with / without flux preservation.
# Begin by making some object that we draw into an Image.
gal_sigma = 3.7
pix_scale = 0.6
test_snr = 73.
gauss = galsim.Gaussian(sigma=gal_sigma)
im = gauss.drawImage(scale=pix_scale, dtype=np.float64)
# Now make the noise object to use.
# Use a default-constructed rng (i.e. rng=None) since we had initially had trouble
# with that. And use the duplicate feature to get a second copy of this rng.
gn = galsim.GaussianNoise()
rng2 = gn.rng.duplicate()
# Try addNoiseSNR with preserve_flux=True, so the RNG needs a different variance.
# Check what variance was added for this SNR, and that the RNG still has its original variance
# after this call.
var_out = im.addNoiseSNR(gn, test_snr, preserve_flux=True)
assert gn.getVariance()==1.0
max_val = im.array.max()
# Now apply addNoiseSNR to another (clean) image with preserve_flux=False, so we use the noise
# variance in the original RNG, i.e., 1. Check that the returned variance is 1, and that the
# value of the maximum pixel (presumably the peak of the galaxy light profile) is scaled as we
# expect for this SNR.
im2 = gauss.drawImage(scale=pix_scale, dtype=np.float64)
gn2 = galsim.GaussianNoise(rng=rng2)
var_out2 = im2.addNoiseSNR(gn2, test_snr, preserve_flux=False)
assert var_out2==1.0
expect_max_val2 = max_val*np.sqrt(var_out2/var_out)
np.testing.assert_almost_equal(
im2.array.max(), expect_max_val2, decimal=8,
err_msg='addNoiseSNR with preserve_flux = True and False give inconsistent results')
if __name__ == "__main__":
test_deviate_noise()
test_gaussian_noise()
test_variable_gaussian_noise()
test_poisson_noise()
test_ccdnoise()
test_addnoisesnr()