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test_calc.py
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test_calc.py
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# Copyright (c) 2012-2023 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.
#
import numpy as np
import galsim
from galsim_test_helpers import *
@timer
def test_hlr():
"""Test the calculateHLR method.
"""
# Compare the calculation for a simple Gaussian.
g1 = galsim.Gaussian(sigma=5, flux=1.7)
print('g1 native hlr = ',g1.half_light_radius)
print('g1.calculateHLR = ',g1.calculateHLR())
print('nyquist scale = ',g1.nyquist_scale)
# These should be exactly equal.
np.testing.assert_equal(g1.half_light_radius, g1.calculateHLR(),
err_msg="Gaussian.calculateHLR() returned wrong value.")
# Check for a convolution of two Gaussians. Should be equivalent, but now will need to
# do the calculation.
g2 = galsim.Convolve(galsim.Gaussian(sigma=3, flux=1.3), galsim.Gaussian(sigma=4, flux=23))
test_hlr = g2.calculateHLR()
print('g2.calculateHLR = ',test_hlr)
print('ratio - 1 = ',test_hlr/g1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/g1.half_light_radius, 1.0, decimal=1,
err_msg="Gaussian.calculateHLR() is not accurate.")
# The default scale is only accurate to around 1 dp. Using scale = 0.1 is accurate to 3 dp.
# Note: Nyquist scale is about 4.23 for this profile.
test_hlr = g2.calculateHLR(scale=0.1)
print('g2.calculateHLR(scale=0.1) = ',test_hlr)
print('ratio - 1 = ',test_hlr/g1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/g1.half_light_radius, 1.0, decimal=3,
err_msg="Gaussian.calculateHLR(scale=0.1) is not accurate.")
# Finally, we don't expect this to be accurate, but make sure the code can handle having
# more than half the flux in the central pixel.
test_hlr = g2.calculateHLR(scale=15)
print('g2.calculateHLR(scale=15) = ',test_hlr)
print('ratio - 1 = ',test_hlr/g1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/g1.half_light_radius/10, 0.1, decimal=1,
err_msg="Gaussian.calculateHLR(scale=15) is not accurate.")
# Next, use an Exponential profile
e1 = galsim.Exponential(scale_radius=5, flux=1.7)
print('e1 native hlr = ',e1.half_light_radius)
print('e1.calculateHLR = ',e1.calculateHLR())
print('nyquist scale = ',e1.nyquist_scale)
# These should be exactly equal.
np.testing.assert_equal(e1.half_light_radius, e1.calculateHLR(),
err_msg="Exponential.calculateHLR() returned wrong value.")
# Check for a convolution with a delta function. Should be equivalent, but now will need to
# do the calculation.
e2 = galsim.Convolve(galsim.Exponential(scale_radius=5, flux=1.3),
galsim.Gaussian(sigma=1.e-4, flux=23))
test_hlr = e2.calculateHLR()
print('e2.calculateHLR = ',test_hlr)
print('ratio - 1 = ',test_hlr/e1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=1,
err_msg="Exponential.calculateHLR() is not accurate.")
# The default scale is only accurate to around 1 dp. Using scale = 0.1 is accurate to 3 dp.
# Note: Nyquist scale is about 1.57 for this profile.
# We can also decrease the size, which should still be accurate, but maybe a little faster.
# Go a bit more that 2*hlr in units of the pixels.
size = int(2.2 * e1.half_light_radius / 0.1)
test_hlr = e2.calculateHLR(scale=0.1, size=size)
print('e2.calculateHLR(scale=0.1) = ',test_hlr)
print('ratio - 1 = ',test_hlr/e1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=3,
err_msg="Exponential.calculateHLR(scale=0.1) is not accurate.")
# Check that it works if the centroid is not at the origin
e3 = e2.shift(2,3)
test_hlr = e3.calculateHLR(scale=0.1)
print('e3.calculateHLR(scale=0.1) = ',test_hlr)
print('ratio - 1 = ',test_hlr/e1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=3,
err_msg="shifted Exponential HLR is not accurate.")
# Can set a centroid manually. This should be equivalent to the default.
print('e3.centroid = ',e3.centroid)
test_hlr = e3.calculateHLR(scale=0.1, centroid=e3.centroid)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=3,
err_msg="shifted HLR with explicit centroid is not accurate.")
# The calculateHLR method can also return other radii like r90, rather than r50 using the
# parameter flux_fraction. This is also analytic for Exponential
r90 = 3.889720170 * e1.scale_radius
test_r90 = e2.calculateHLR(scale=0.1, flux_frac=0.9)
print('r90 = ',r90)
print('e2.calculateHLR(scale=0.1, flux_frac=0.9) = ',test_r90)
print('ratio - 1 = ',test_r90/r90-1)
np.testing.assert_almost_equal(test_r90/r90, 1.0, decimal=3,
err_msg="Exponential r90 calculation is not accurate.")
# Check the image version.
im = e1.drawImage(scale=0.1, nx=2048, ny=2048) # Needs to be large to get enough flux for the
# image to get it to 3 digits of accuracy.
test_hlr = im.calculateHLR()
print('im.calculateHLR() = ',test_hlr)
print('ratio - 1 = ',test_hlr/e1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=3,
err_msg="image.calculateHLR is not accurate.")
# Check that a non-square image works correctly. Also, not centered anywhere in particular.
bounds = galsim.BoundsI(-1234, -1234+2048, 8234, 8234+2099)
offset = galsim.PositionD(29,1)
im = e1.drawImage(scale=0.1, bounds=bounds, offset=offset)
test_hlr = im.calculateHLR(center=im.true_center+offset)
print('im.calculateHLR() = ',test_hlr)
print('ratio - 1 = ',test_hlr/e1.half_light_radius-1)
np.testing.assert_almost_equal(test_hlr/e1.half_light_radius, 1.0, decimal=3,
err_msg="non-square image.calculateHLR is not accurate.")
@timer
def test_sigma():
"""Test the calculateMomentRadius method.
"""
# Compare the calculation for a simple Gaussian.
g1 = galsim.Gaussian(sigma=5, flux=1.7)
print('g1 native sigma = ',g1.sigma)
print('g1.calculateMomentRadius = ',g1.calculateMomentRadius())
# These should be exactly equal.
np.testing.assert_equal(
g1.sigma, g1.calculateMomentRadius(),
err_msg="Gaussian.calculateMomentRadius() returned wrong value.")
np.testing.assert_equal(
g1.sigma, g1.calculateMomentRadius(rtype='trace'),
err_msg="Gaussian.calculateMomentRadius(trace) returned wrong value.")
np.testing.assert_equal(
g1.sigma, g1.calculateMomentRadius(rtype='det'),
err_msg="Gaussian.calculateMomentRadius(det) returned wrong value.")
np.testing.assert_equal(
(g1.sigma, g1.sigma), g1.calculateMomentRadius(rtype='both'),
err_msg="Gaussian.calculateMomentRadius(both) returned wrong value.")
with assert_raises(galsim.GalSimValueError):
g1.calculateMomentRadius(rtype='invalid')
# Check for a convolution of two Gaussians. Should be equivalent, but now will need to
# do the calculation.
g2 = galsim.Convolve(galsim.Gaussian(sigma=3, flux=1.3), galsim.Gaussian(sigma=4, flux=23))
test_sigma = g2.calculateMomentRadius()
print('g2.calculateMomentRadius = ',test_sigma)
print('ratio - 1 = ',test_sigma/g1.sigma-1)
np.testing.assert_almost_equal(
test_sigma/g1.sigma, 1.0, decimal=1,
err_msg="Gaussian.calculateMomentRadius() is not accurate.")
# The default scale and size is only accurate to around 1 dp. Using scale = 0.1 is accurate
# to 4 dp.
test_sigma = g2.calculateMomentRadius(scale=0.1)
print('g2.calculateMomentRadius(scale=0.1) = ',test_sigma)
print('ratio - 1 = ',test_sigma/g1.sigma-1)
np.testing.assert_almost_equal(
test_sigma/g1.sigma, 1.0, decimal=4,
err_msg="Gaussian.calculateMomentRadius(scale=0.1) is not accurate.")
# In this case, the different calculations are eqivalent:
np.testing.assert_almost_equal(
test_sigma, g2.calculateMomentRadius(scale=0.1, rtype='trace'),
err_msg="Gaussian.calculateMomentRadius(trace) is not accurate.")
np.testing.assert_almost_equal(
test_sigma, g2.calculateMomentRadius(scale=0.1, rtype='det'),
err_msg="Gaussian.calculateMomentRadius(trace) is not accurate.")
np.testing.assert_almost_equal(
(test_sigma, test_sigma), g2.calculateMomentRadius(scale=0.1, rtype='both'),
err_msg="Gaussian.calculateMomentRadius(trace) is not accurate.")
# However, when we shear it, the default (det) measure stays equal to the original sigma, but
# the trace measure increases by a factor of (1-e^2)^0.25
g3 = g2.shear(e1=0.4, e2=0.3)
esq = 0.4**2 + 0.3**2
sheared_sigma = g3.calculateMomentRadius(scale=0.1)
print('g3.calculateMomentRadius(scale=0.1) = ',sheared_sigma)
print('ratio - 1 = ',sheared_sigma/g1.sigma-1)
sheared_sigma2 = g3.calculateMomentRadius(scale=0.1, rtype='trace')
print('g3.calculateMomentRadius(scale=0.1,trace) = ',sheared_sigma2)
print('ratio = ',sheared_sigma2 / g1.sigma)
print('(1-e^2)^-0.25 = ',(1-esq)**-0.25)
print('ratio - 1 = ',sheared_sigma2/(g1.sigma*(1.-esq)**-0.25)-1)
np.testing.assert_almost_equal(
sheared_sigma/g1.sigma, 1.0, decimal=4,
err_msg="sheared Gaussian.calculateMomentRadius(scale=0.1) is not accurate.")
np.testing.assert_almost_equal(
sheared_sigma2/(g1.sigma*(1.-esq)**-0.25), 1.0, decimal=4,
err_msg="sheared Gaussian.calculateMomentRadius(scale=0.1,trace) is not accurate.")
# Next, use an Exponential profile
e1 = galsim.Exponential(scale_radius=5, flux=1.7)
# The true "sigma" for this is analytic, but not an attribute.
e1_sigma = np.sqrt(3.0) * e1.scale_radius
print('true e1 sigma = sqrt(3) * e1.scale_radius = ',e1_sigma)
# Test with the default scale and size.
test_sigma = e1.calculateMomentRadius()
print('e1.calculateMomentRadius = ',test_sigma)
print('ratio - 1 = ',test_sigma/e1_sigma-1)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=1,
err_msg="Exponential.calculateMomentRadius() is not accurate.")
# The default scale and size is only accurate to around 1 dp. This time we have to both
# decrease the scale and also increase the size to get 4 dp of precision.
test_sigma = e1.calculateMomentRadius(scale=0.1, size=2000)
print('e1.calculateMomentRadius(scale=0.1) = ',test_sigma)
print('ratio - 1 = ',test_sigma/e1_sigma-1)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=4,
err_msg="Exponential.calculateMomentRadius(scale=0.1) is not accurate.")
# Check that it works if the centroid is not at the origin
e3 = e1.shift(2,3)
test_sigma = e3.calculateMomentRadius(scale=0.1, size=2000)
print('e1.calculateMomentRadius(scale=0.1) = ',test_sigma)
print('ratio - 1 = ',test_sigma/e1_sigma-1)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=4,
err_msg="shifted Exponential MomentRadius is not accurate.")
# Can set a centroid manually. This should be equivalent to the default.
print('e3.centroid = ',e3.centroid)
test_sigma = e3.calculateMomentRadius(scale=0.1, size=2000, centroid=e3.centroid)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=4,
err_msg="shifted MomentRadius with explicit centroid is not accurate.")
# Check the image version.
size = 2000
im = e1.drawImage(scale=0.1, nx=size, ny=size)
test_sigma = im.calculateMomentRadius()
print('im.calculateMomentRadius() = ',test_sigma)
print('ratio - 1 = ',test_sigma/e1_sigma-1)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=4,
err_msg="image.calculateMomentRadius is not accurate.")
with assert_raises(galsim.GalSimValueError):
im.calculateMomentRadius(rtype='invalid')
# Check that a non-square image works correctly. Also, not centered anywhere in particular.
bounds = galsim.BoundsI(-1234, -1234+size*2, 8234, 8234+size)
offset = galsim.PositionD(29,1)
im = e1.drawImage(scale=0.1, bounds=bounds, offset=offset)
test_hlr = im.calculateMomentRadius(center=im.true_center+offset)
print('im.calculateMomentRadius() = ',test_sigma)
print('ratio - 1 = ',test_sigma/e1_sigma-1)
np.testing.assert_almost_equal(
test_sigma/e1_sigma, 1.0, decimal=4,
err_msg="non-square image.calculateMomentRadius is not accurate.")
@timer
def test_fwhm():
"""Test the calculateFWHM method.
"""
# Compare the calculation for a simple Gaussian.
g1 = galsim.Gaussian(sigma=5, flux=1.7)
print('g1 native fwhm = ',g1.fwhm)
print('g1.calculateFWHM = ',g1.calculateFWHM())
# These should be exactly equal.
np.testing.assert_equal(g1.fwhm, g1.calculateFWHM(),
err_msg="Gaussian.calculateFWHM() returned wrong value.")
# Check for a convolution of two Gaussians. Should be equivalent, but now will need to
# do the calculation.
g2 = galsim.Convolve(galsim.Gaussian(sigma=3, flux=1.3), galsim.Gaussian(sigma=4, flux=23))
test_fwhm = g2.calculateFWHM()
print('g2.calculateFWHM = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/g1.fwhm-1)
np.testing.assert_almost_equal(test_fwhm/g1.fwhm, 1.0, decimal=3,
err_msg="Gaussian.calculateFWHM() is not accurate.")
# The default scale already accurate to around 3 dp. Using scale = 0.1 is accurate to 8 dp.
test_fwhm = g2.calculateFWHM(scale=0.1)
print('g2.calculateFWHM(scale=0.1) = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/g1.fwhm-1)
np.testing.assert_almost_equal(test_fwhm/g1.fwhm, 1.0, decimal=8,
err_msg="Gaussian.calculateFWHM(scale=0.1) is not accurate.")
# Finally, we don't expect this to be accurate, but make sure the code can handle having
# only the central pixel higher than half-maximum.
test_fwhm = g2.calculateFWHM(scale=20)
print('g2.calculateFWHM(scale=20) = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/g1.fwhm-1)
np.testing.assert_almost_equal(test_fwhm/g1.fwhm/10, 0.1, decimal=1,
err_msg="Gaussian.calculateFWHM(scale=20) is not accurate.")
# Next, use an Exponential profile
e1 = galsim.Exponential(scale_radius=5, flux=1.7)
# The true fwhm for this is analytic, but not an attribute.
e1_fwhm = 2. * np.log(2.0) * e1.scale_radius
print('true e1 fwhm = ',e1_fwhm)
# Test with the default scale and size.
test_fwhm = e1.calculateFWHM()
print('e1.calculateFWHM = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/e1_fwhm-1)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=3,
err_msg="Exponential.calculateFWHM() is not accurate.")
# The default scale already accurate to around 3 dp. Using scale = 0.1 is accurate to 7 dp.
# We can also decrease the size, which should still be accurate, but maybe a little faster.
# Go a bit more that fwhm in units of the pixels.
size = int(1.2 * e1_fwhm / 0.1)
test_fwhm = e1.calculateFWHM(scale=0.1, size=size)
print('e1.calculateFWHM(scale=0.1) = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/e1_fwhm-1)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=7,
err_msg="Exponential.calculateFWHM(scale=0.1) is not accurate.")
# Check that it works if the centroid is not at the origin
e3 = e1.shift(2,3)
test_fwhm = e3.calculateFWHM(scale=0.1)
print('e3.calculateFWHM(scale=0.1) = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/e1_fwhm-1)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=6,
err_msg="shifted Exponential FWHM is not accurate.")
# Can set a centroid manually. This should be equivalent to the default.
print('e3.centroid = ',e3.centroid)
test_fwhm = e3.calculateFWHM(scale=0.1, centroid=e3.centroid)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=6,
err_msg="shifted FWHM with explicit centroid is not accurate.")
# Check the image version.
im = e1.drawImage(scale=0.1, method='sb')
test_fwhm = im.calculateFWHM(Imax=e1.xValue(0,0))
print('im.calculateFWHM() = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/e1_fwhm-1)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=6,
err_msg="image.calculateFWHM is not accurate.")
# Check that a non-square image works correctly. Also, not centered anywhere in particular.
bounds = galsim.BoundsI(-1234, -1234+size*2, 8234, 8234+size)
offset = galsim.PositionD(29,1)
im = e1.drawImage(scale=0.1, bounds=bounds, offset=offset, method='sb')
test_fwhm = im.calculateFWHM(Imax=e1.xValue(0,0), center=im.true_center+offset)
print('im.calculateFWHM() = ',test_fwhm)
print('ratio - 1 = ',test_fwhm/e1_fwhm-1)
np.testing.assert_almost_equal(test_fwhm/e1_fwhm, 1.0, decimal=6,
err_msg="non-square image.calculateFWHM is not accurate.")
if __name__ == "__main__":
testfns = [v for k, v in vars().items() if k[:5] == 'test_' and callable(v)]
for testfn in testfns:
testfn()