def test_ne():
""" Check that inequality works as expected."""
gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)
gal1 = galsim.Gaussian(fwhm=1)
gal2 = galsim.Gaussian(fwhm=2)
# Sum. Params are objs to add and potentially gsparams.
gals = [
galsim.Sum(gal1),
galsim.Sum(gal1, gal2),
galsim.Sum(gal2, gal1), # Not! commutative.
galsim.Sum(galsim.Sum(gal1, gal2), gal2),
galsim.Sum(gal1, galsim.Sum(gal2, gal2)), # Not! associative.
galsim.Sum(gal1, gsparams=gsp)
]
all_obj_diff(gals)
# Convolution. Params are objs to convolve and potentially gsparams.
# The following should test unequal
gals = [
galsim.Convolution(gal1),
galsim.Convolution(gal1, gal2),
galsim.Convolution(gal2, gal1), # Not! commutative.
galsim.Convolution(gal1, gal2, real_space=True),
galsim.Convolution(galsim.Convolution(gal1, gal2), gal2),
galsim.Convolution(gal1,
galsim.Convolution(gal2,
gal2)), # Not! associative.
galsim.Convolution(gal1, gsparams=gsp)
]
all_obj_diff(gals)
# Deconvolution. Only params here are obj to deconvolve and gsparams.
gals = [
galsim.Deconvolution(gal1),
galsim.Deconvolution(gal2),
galsim.Deconvolution(gal1, gsparams=gsp)
]
all_obj_diff(gals)
# AutoConvolution. Only params here are obj to deconvolve and gsparams.
gals = [
galsim.AutoConvolution(gal1),
galsim.AutoConvolution(gal2),
galsim.AutoConvolution(gal1, gsparams=gsp)
]
all_obj_diff(gals)
# AutoCorrelation. Only params here are obj to deconvolve and gsparams.
gals = [
galsim.AutoCorrelation(gal1),
galsim.AutoCorrelation(gal2),
galsim.AutoCorrelation(gal1, gsparams=gsp)
]
all_obj_diff(gals)
def test_ne():
import time
t1 = time.time()
gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)
gal1 = galsim.Gaussian(fwhm=1)
gal2 = galsim.Gaussian(fwhm=2)
# Sum. Params are objs to add and potentially gsparams.
gals = [galsim.Sum(gal1),
galsim.Sum(gal1, gal2),
galsim.Sum(gal2, gal1), # Not! commutative.
galsim.Sum(galsim.Sum(gal1, gal2), gal2),
galsim.Sum(gal1, galsim.Sum(gal2, gal2)), # Not! associative.
galsim.Sum(gal1, gsparams=gsp)]
all_obj_diff(gals)
# Convolution. Params are objs to convolve and potentially gsparams.
# The following should test unequal
gals = [galsim.Convolution(gal1),
galsim.Convolution(gal1, gal2),
galsim.Convolution(gal2, gal1), # Not! commutative.
galsim.Convolution(gal1, gal2, real_space=True),
galsim.Convolution(galsim.Convolution(gal1, gal2), gal2),
galsim.Convolution(gal1, galsim.Convolution(gal2, gal2)), # Not! associative.
galsim.Convolution(gal1, gsparams=gsp)]
all_obj_diff(gals)
# Deconvolution. Only params here are obj to deconvolve and gsparams.
gals = [galsim.Deconvolution(gal1),
galsim.Deconvolution(gal2),
galsim.Deconvolution(gal1, gsparams=gsp)]
all_obj_diff(gals)
# AutoConvolution. Only params here are obj to deconvolve and gsparams.
gals = [galsim.AutoConvolution(gal1),
galsim.AutoConvolution(gal2),
galsim.AutoConvolution(gal1, gsparams=gsp)]
all_obj_diff(gals)
# AutoCorrelation. Only params here are obj to deconvolve and gsparams.
gals = [galsim.AutoCorrelation(gal1),
galsim.AutoCorrelation(gal2),
galsim.AutoCorrelation(gal1, gsparams=gsp)]
all_obj_diff(gals)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def test_convolve_noise():
"""Test that noise propagation works properly for compount objects.
"""
obj1 = galsim.Gaussian(sigma=1.7)
obj2 = galsim.Gaussian(sigma=2.3)
obj1.noise = galsim.UncorrelatedNoise(variance=0.3, scale=0.2)
obj2.noise = galsim.UncorrelatedNoise(variance=0.5, scale=0.2)
obj3 = galsim.Gaussian(sigma=2.9)
# Convolve convolves the noise from a single component
conv2 = galsim.Convolution([obj1, obj3])
noise = galsim.Convolve([obj1.noise._profile, obj3, obj3])
# xValue is too slow here. Use drawImage to get variance. (Just as CorrelatedNoise does.)
variance = noise.drawImage(nx=1, ny=1, scale=1., method='sb')(1, 1)
np.testing.assert_almost_equal(
conv2.noise.getVariance(),
variance,
err_msg=
"Convolution of two objects did not correctly propagate noise varinace"
)
conv2 = galsim.Convolution([obj2, obj3])
noise = galsim.Convolve([obj2.noise._profile, obj3, obj3])
variance = noise.drawImage(nx=1, ny=1, scale=1., method='sb')(1, 1)
np.testing.assert_almost_equal(
conv2.noise.getVariance(),
variance,
err_msg=
"Convolution of two objects did not correctly propagate noise varinace"
)
# Convolution of multiple objects with noise attributes raises a warning and fails
# to propagate noise properly. (It takes the input noise from the first one.)
conv2 = galsim.Convolution(obj1, obj2)
conv3 = galsim.Convolution(obj1, obj2, obj3)
with assert_warns(galsim.GalSimWarning):
assert conv2.noise == obj1.noise.convolvedWith(obj2)
with assert_warns(galsim.GalSimWarning):
assert conv3.noise == obj1.noise.convolvedWith(
galsim.Convolve(obj2, obj3))
# Convolution with only one object uses that object's noise
conv1 = galsim.Convolution(obj1)
assert conv1.noise == obj1.noise
# Other types don't propagate noise and give a warning about it.
deconv = galsim.Deconvolution(obj2)
autoconv = galsim.AutoConvolution(obj2)
autocorr = galsim.AutoCorrelation(obj2)
four = galsim.FourierSqrt(obj2)
with assert_warns(galsim.GalSimWarning):
assert deconv.noise is None
with assert_warns(galsim.GalSimWarning):
assert autoconv.noise is None
with assert_warns(galsim.GalSimWarning):
assert autocorr.noise is None
with assert_warns(galsim.GalSimWarning):
assert four.noise is None
obj2.noise = None # Remove obj2 noise for the rest.
conv2 = galsim.Convolution(obj1, obj2)
noise = galsim.Convolve([obj1.noise._profile, obj2, obj2])
variance = noise.drawImage(nx=1, ny=1, scale=1., method='sb')(1, 1)
np.testing.assert_almost_equal(
conv2.noise.getVariance(),
variance,
err_msg=
"Convolution of two objects did not correctly propagate noise varinace"
)
conv3 = galsim.Convolution(obj1, obj2, obj3)
noise = galsim.Convolve([obj1.noise._profile, obj2, obj2, obj3, obj3])
variance = noise.drawImage(nx=1, ny=1, scale=1., method='sb')(1, 1)
np.testing.assert_almost_equal(
conv3.noise.getVariance(),
variance,
err_msg=
"Convolution of three objects did not correctly propagate noise varinace"
)
deconv = galsim.Deconvolution(obj2)
autoconv = galsim.AutoConvolution(obj2)
autocorr = galsim.AutoCorrelation(obj2)
four = galsim.FourierSqrt(obj2)
assert deconv.noise is None
assert autoconv.noise is None
assert autocorr.noise is None
assert four.noise is None
