def test_ne():
""" Check that inequality works as expected."""
gsp = galsim.GSParams(maxk_threshold=5.1e-3, folding_threshold=1.1e-3)
objs = [galsim.Shapelet(1., 2),
galsim.Shapelet(1., 3),
galsim.Shapelet(2., 2),
galsim.Shapelet(1., 2, bvec=[1, 0, 0, 0.2, 0.3, -0.1]),
galsim.Shapelet(1., 2, gsparams=gsp)]
all_obj_diff(objs)
def test_ne():
import time
t1 = time.time()
gsp = galsim.GSParams(maxk_threshold=5.1e-3, folding_threshold=1.1e-3)
objs = [
galsim.Shapelet(1., 2),
galsim.Shapelet(1., 3),
galsim.Shapelet(2., 2),
galsim.Shapelet(1., 2, bvec=[1, 0, 0, 0.2, 0.3, -0.1]),
galsim.Shapelet(1., 2, gsparams=gsp)
]
all_obj_diff(objs)
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_shapelet_gaussian():
"""Test that the simplest Shapelet profile is equivalent to a Gaussian
"""
ftypes = [np.float32, np.float64]
scale = 0.2
test_flux = 23.
# First, a Shapelet with only b_00 = 1 should be identically a Gaussian
im1 = galsim.ImageF(64,64, scale=scale)
im2 = galsim.ImageF(64,64, scale=scale)
for sigma in [1., 0.6, 2.4]:
gauss = galsim.Gaussian(flux=test_flux, sigma=sigma)
gauss.drawImage(im1, method='no_pixel')
for order in [0, 2, 8]:
bvec = np.zeros(galsim.Shapelet.size(order))
bvec[0] = test_flux
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.drawImage(im2, method='no_pixel')
printval(im2,im1)
np.testing.assert_array_almost_equal(
im1.array, im2.array, 5,
err_msg="Shapelet with (only) b00=1 disagrees with Gaussian result"
"for flux=%f, sigma=%f, order=%d"%(test_flux,sigma,order))
np.testing.assert_almost_equal(
gauss.max_sb, shapelet.max_sb, 5,
err_msg="Shapelet max_sb did not match Gaussian max_sb")
np.testing.assert_almost_equal(
gauss.flux, shapelet.flux, 5,
err_msg="Shapelet flux did not match Gaussian flux")
def test_shapelet_gaussian():
"""Test that the simplest Shapelet profile is equivalent to a Gaussian
"""
import time
t1 = time.time()
ftypes = [np.float32, np.float64]
scale = 0.2
test_flux = 23.
# First, a Shapelet with only b_00 = 1 should be identically a Gaussian
im1 = galsim.ImageF(64, 64, scale=scale)
im2 = galsim.ImageF(64, 64, scale=scale)
for sigma in [1., 0.6, 2.4]:
gauss = galsim.Gaussian(flux=test_flux, sigma=sigma)
gauss.draw(im1)
for order in [0, 2, 8]:
bvec = np.zeros(galsim.LVectorSize(order))
bvec[0] = test_flux
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.draw(im2)
printval(im2, im1)
np.testing.assert_array_almost_equal(
im1.array,
im2.array,
5,
err_msg=
"Shapelet with (only) b00=1 disagrees with Gaussian result"
"for flux=%f, sigma=%f, order=%d" % (test_flux, sigma, order))
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def getPSF(self, pos, gsparams=None):
"""Returns the PSF at position pos
@param pos The position in pixel units for which to build the PSF.
@param gsparams (Optional) A GSParams instance to pass to the constructed GSObject.
@returns a galsim.Shapelet instance.
"""
if not self.bounds.includes(pos):
raise IndexError("position in DES_Shapelet.getPSF is out of bounds")
import numpy
Px = self._definePxy(pos.x,self.bounds.xmin,self.bounds.xmax)
Py = self._definePxy(pos.y,self.bounds.ymin,self.bounds.ymax)
order = self.fit_order
P = numpy.array([ Px[n-q] * Py[q] for n in range(order+1) for q in range(n+1) ])
assert len(P) == self.fit_size
# Note: This is equivalent to:
#
# P = numpy.empty(self.fit_size)
# k = 0
# for n in range(self.fit_order+1):
# for q in range(n+1):
# P[k] = Px[n-q] * Py[q]
# k = k+1
b1 = numpy.dot(P,self.interp_matrix)
b = numpy.dot(b1,self.rot_matrix)
assert len(b) == self.psf_size
b += self.ave_psf
ret = galsim.Shapelet(self.sigma, self.psf_order, b, gsparams=gsparams)
return ret
def test_shapelet_properties():
"""Test some specific numbers for a particular Shapelet profile.
"""
# A semi-random particular vector of coefficients.
sigma = 1.8
order = 4
bvec = [
1.3, # n = 0
0.02,
0.03, # n = 1
0.23,
-0.19,
0.08, # n = 2
0.01,
0.02,
0.04,
-0.03, # n = 3
-0.09,
0.07,
-0.11,
-0.08,
0.11
] # n = 4
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
check_basic(shapelet, "Shapelet", approx_maxsb=True)
# Check flux
flux = bvec[0] + bvec[5] + bvec[14]
np.testing.assert_almost_equal(shapelet.getFlux(), flux, 10)
# The maxSB is not very accurate for Shapelet, but in this case it is still ok (matching
# xValue(0,0), which isn't actually the maximum) to 2 digits.
np.testing.assert_almost_equal(
shapelet.xValue(0, 0),
shapelet.maxSB(),
2,
err_msg="Shapelet maxSB did not match maximum pixel value")
# Check centroid
cen = galsim.PositionD(
bvec[1], -bvec[2]) + np.sqrt(2.) * galsim.PositionD(bvec[8], -bvec[9])
cen *= 2. * sigma / flux
np.testing.assert_almost_equal(shapelet.centroid().x, cen.x, 10)
np.testing.assert_almost_equal(shapelet.centroid().y, cen.y, 10)
# Check Fourier properties
np.testing.assert_almost_equal(shapelet.maxK(), 4.61738371186, 10)
np.testing.assert_almost_equal(shapelet.stepK(), 0.195133742529, 10)
# Check image values in real and Fourier space
zero = galsim.PositionD(0., 0.)
np.testing.assert_almost_equal(shapelet.kValue(zero), flux + 0j, 10)
np.testing.assert_almost_equal(shapelet.xValue(zero), 0.0653321217013, 10)
# Check picklability
do_pickle(shapelet)
do_pickle(shapelet.SBProfile)
do_pickle(shapelet.SBProfile.getBVec())
def BuildDES_Shapelet(config, key, base, ignore, gsparams, logger):
"""@brief Build a RealGalaxy type GSObject from user input.
"""
opt = {'flux': float, 'num': int}
kwargs, safe = galsim.config.GetAllParams(config,
key,
base,
opt=opt,
ignore=ignore)
if 'des_shapelet' not in base:
raise ValueError(
"No DES_Shapelet instance available for building type = DES_Shapelet"
)
num = kwargs.get('num', 0)
if num < 0:
raise ValueError(
"Invalid num < 0 supplied for DES_Shapelet: num = %d" % num)
if num >= len(base['des_shapelet']):
raise ValueError(
"Invalid num supplied for DES_Shapelet (too large): num = %d" %
num)
des_shapelet = base['des_shapelet'][num]
if 'image_pos' not in base:
raise ValueError(
"DES_Shapelet requested, but no image_pos defined in base.")
image_pos = base['image_pos']
# Convert gsparams from a dict to an actual GSParams object
if gsparams: gsparams = galsim.GSParams(**gsparams)
else: gsparams = None
if des_shapelet.getBounds().includes(image_pos):
#psf = des_shapelet.getPSF(image_pos, gsparams)
# Because of serialization issues, the above call doesn't work. So we need to
# repeat the internals of getPSF here.
b = des_shapelet.getB(image_pos)
sigma = des_shapelet.getSigma()
order = des_shapelet.getOrder()
psf = galsim.Shapelet(sigma, order, b, gsparams=gsparams)
else:
message = 'Position ' + str(
image_pos) + ' not in interpolation bounds: '
message += str(des_shapelet.getBounds())
raise galsim.config.gsobject.SkipThisObject(message)
if 'flux' in kwargs:
psf = psf.withFlux(kwargs['flux'])
# The second item here is "safe", a boolean that declares whether the returned value is
# safe to save and use again for later objects. In this case, we wouldn't want to do
# that, since they will be at different positions, so the interpolated PSF will be different.
return psf, False
def getPSF(self, image_pos, gsparams=None):
"""Returns the PSF at position image_pos
@param image_pos The position in pixel units for which to build the PSF.
@param gsparams (Optional) A GSParams instance to pass to the constructed GSObject.
@returns the PSF as a galsim.Shapelet instance
"""
return galsim.Shapelet(self.sigma,
self.psf_order,
self.getB(image_pos),
gsparams=gsparams)
def BuildDES_Shapelet(config, base, ignore, gsparams, logger):
"""Build a GSObject representing the shapelet model at the correct location in the image in a
config-processing context.
This is used as object type ``DES_Shapelet`` in a config file.
It requires the use of the ``des_shapelet`` input field.
"""
des_shapelet = galsim.config.GetInputObj('des_shapelet', config, base,
'DES_Shapelet')
opt = {'flux': float, 'num': int, 'image_pos': galsim.PositionD}
params, safe = galsim.config.GetAllParams(config,
base,
opt=opt,
ignore=ignore)
if 'image_pos' in params:
image_pos = params['image_pos']
elif 'image_pos' in base:
image_pos = base['image_pos']
else:
raise galsim.GalSimConfigError(
"DES_Shapelet requested, but no image_pos defined in base.")
# Convert gsparams from a dict to an actual GSParams object
if gsparams: gsparams = galsim.GSParams(**gsparams)
else: gsparams = None
if des_shapelet.getBounds().includes(image_pos):
#psf = des_shapelet.getPSF(image_pos, gsparams)
# Because of serialization issues, the above call doesn't work. So we need to
# repeat the internals of getPSF here.
b = des_shapelet.getB(image_pos)
sigma = des_shapelet.getSigma()
order = des_shapelet.getOrder()
psf = galsim.Shapelet(sigma, order, b,
gsparams=gsparams).transform(-1, 0, 0, 1)
else:
message = 'Position ' + str(
image_pos) + ' not in interpolation bounds: '
message += str(des_shapelet.getBounds())
raise galsim.config.gsobject.SkipThisObject(message)
if 'flux' in params:
psf = psf.withFlux(params['flux'])
# The second item here is "safe", a boolean that declares whether the returned value is
# safe to save and use again for later objects. In this case, we wouldn't want to do
# that, since they will be at different positions, so the interpolated PSF will be different.
return psf, False
def test_shapelet_properties():
"""Test some specific numbers for a particular Shapelet profile.
"""
import time
t1 = time.time()
# A semi-random particular vector of coefficients.
sigma = 1.8
order = 4
bvec = [
1.3, # n = 0
0.02,
0.03, # n = 1
0.23,
-0.19,
0.08, # n = 2
0.01,
0.02,
0.04,
-0.03, # n = 3
-0.09,
0.07,
-0.11,
-0.08,
0.11
] # n = 4
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
# Check flux
flux = bvec[0] + bvec[5] + bvec[14]
np.testing.assert_almost_equal(shapelet.getFlux(), flux, 10)
# Check centroid
cen = galsim.PositionD(
bvec[1], -bvec[2]) + np.sqrt(2.) * galsim.PositionD(bvec[8], -bvec[9])
cen *= 2. * sigma / flux
np.testing.assert_almost_equal(shapelet.centroid().x, cen.x, 10)
np.testing.assert_almost_equal(shapelet.centroid().y, cen.y, 10)
# Check Fourier properties
np.testing.assert_almost_equal(shapelet.maxK(), 4.61738371186, 10)
np.testing.assert_almost_equal(shapelet.stepK(), 0.195133742529, 10)
# Check image values in real and Fourier space
zero = galsim.PositionD(0., 0.)
np.testing.assert_almost_equal(shapelet.kValue(zero), flux + 0j, 10)
np.testing.assert_almost_equal(shapelet.xValue(zero), 0.0653321217013, 10)
# Check picklability
do_pickle(shapelet)
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def getPSF(self, image_pos, gsparams=None):
"""Returns the PSF at position image_pos
@param image_pos The position in pixel units for which to build the PSF.
@param gsparams (Optional) A GSParams instance to pass to the constructed GSObject.
@returns the PSF as a galsim.Shapelet instance
"""
psf = galsim.Shapelet(self.sigma, self.psf_order, self.getB(image_pos), gsparams=gsparams)
# The fitpsf files were built with respect to (u,v) = (ra,dec). The GalSim convention is
# to use sky coordinates with u = -ra. So we need to flip the profile across the v axis
# to take u -> -u.
psf = psf.transform(-1,0,0,1)
return psf
def test_shapelet_drawImage():
"""Test some measured properties of a drawn shapelet against the supposed true values
"""
ftypes = [np.float32, np.float64]
scale = 0.2
test_flux = 23.
im = galsim.ImageF(129,129, scale=scale)
for sigma in [1., 0.3, 2.4]:
for order in [0, 2, 8]:
bvec = np.zeros(galsim.Shapelet.size(order))
bvec[0] = 1. # N,m = 0,0
k = 0
for n in range(1,order+1):
k += n+1
if n%2 == 0: # even n
bvec[k] = 0.23/(n*n) # N,m = n,0 or p,q = n/2,n/2
if n >= 2:
bvec[k-2] = 0.14/n # N,m = n,2 real part
bvec[k-1] = -0.08/n # N,m = n,2 imag part
else: # odd n
if n >= 1:
bvec[k-1] = -0.08/n**3.2 # N,m = n,1 real part
bvec[k] = 0.05/n**2.1 # N,m = n,1 imag part
if n >= 3:
bvec[k-3] = 0.31/n**4.2 # N,m = n,3 real part
bvec[k-2] = -0.18/n**3.9 # N,m = n,3 imag part
print('shapelet vector = ',bvec)
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
gsp = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
shapelet2 = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec, gsparams=gsp)
assert shapelet2 != shapelet
assert shapelet2 == shapelet.withGSParams(gsp)
assert shapelet2 == shapelet.withGSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
check_basic(shapelet, "Shapelet", approx_maxsb=True)
# Test normalization (This is normally part of do_shoot. When we eventually
# implement photon shooting, we should go back to the normal do_shoot call,
# and remove this section.)
shapelet = shapelet.withFlux(test_flux)
shapelet.drawImage(im)
flux = im.array.sum()
print('im.sum = ',flux,' cf. ',test_flux)
np.testing.assert_almost_equal(flux / test_flux, 1., 4,
err_msg="Flux normalization for Shapelet disagrees with expected result")
np.testing.assert_allclose(
im.array.max(), shapelet.max_sb * im.scale**2, rtol=0.1,
err_msg="Shapelet max_sb did not match maximum pixel")
# Test centroid
# Note: this only works if the image has odd sizes. If they are even, then
# setCenter doesn't actually set the center to the true center of the image
# (since it falls between pixels).
im.setCenter(0,0)
x,y = np.meshgrid(np.arange(im.array.shape[0]).astype(float) + im.xmin,
np.arange(im.array.shape[1]).astype(float) + im.ymin)
x *= scale
y *= scale
flux = im.array.sum()
mx = (x*im.array).sum() / flux
my = (y*im.array).sum() / flux
conv = galsim.Convolve([shapelet, galsim.Pixel(scale)])
print('centroid = ',mx,my,' cf. ',conv.centroid)
np.testing.assert_almost_equal(mx, shapelet.centroid.x, 3,
err_msg="Measured centroid (x) for Shapelet disagrees with expected result")
np.testing.assert_almost_equal(my, shapelet.centroid.y, 3,
err_msg="Measured centroid (y) for Shapelet disagrees with expected result")
def test_shapelet_adjustments():
"""Test that adjusting the Shapelet profile in various ways does the right thing
"""
import time
t1 = time.time()
ftypes = [np.float32, np.float64]
nx = 128
ny = 128
scale = 0.2
im = galsim.ImageF(nx, ny, scale=scale)
sigma = 1.8
order = 6
bvec = [
1.3, # n = 0
0.02,
0.03, # n = 1
0.23,
-0.19,
0.08, # n = 2
0.01,
0.02,
0.04,
-0.03, # n = 3
-0.09,
0.07,
-0.11,
-0.08,
0.11, # n = 4
-0.03,
-0.02,
-0.08,
0.01,
-0.06,
-0.03, # n = 5
0.06,
-0.02,
0.00,
-0.05,
-0.04,
0.01,
0.09
] # n = 6
ref_shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
ref_im = galsim.ImageF(nx, ny)
ref_shapelet.draw(ref_im, scale=scale)
# Test setSigma
shapelet = galsim.Shapelet(sigma=1., order=order, bvec=bvec)
shapelet.setSigma(sigma)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setSigma disagrees with reference Shapelet")
# Test setBVec
shapelet = galsim.Shapelet(sigma=sigma, order=order)
shapelet.setBVec(bvec)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setBVec disagrees with reference Shapelet")
# Test setOrder
shapelet = galsim.Shapelet(sigma=sigma, order=2)
shapelet.setOrder(order)
shapelet.setBVec(bvec)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setOrder disagrees with reference Shapelet")
# Test that changing the order preserves the values to the extent possible.
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.setOrder(10)
np.testing.assert_array_equal(
shapelet.getBVec()[0:28],
bvec,
err_msg="Shapelet setOrder to larger doesn't preserve existing values."
)
np.testing.assert_array_equal(
shapelet.getBVec()[28:66],
np.zeros(66 - 28),
err_msg="Shapelet setOrder to larger doesn't fill with zeros.")
shapelet.setOrder(6)
np.testing.assert_array_equal(
shapelet.getBVec(),
bvec,
err_msg=
"Shapelet setOrder back to original from larger doesn't preserve existing values."
)
shapelet.setOrder(3)
np.testing.assert_array_equal(
shapelet.getBVec()[0:10],
bvec[0:10],
err_msg="Shapelet setOrder to smaller doesn't preserve existing values."
)
shapelet.setOrder(6)
np.testing.assert_array_equal(
shapelet.getBVec()[0:10],
bvec[0:10],
err_msg=
"Shapelet setOrder back to original from smaller doesn't preserve existing values."
)
shapelet.setOrder(6)
np.testing.assert_array_equal(
shapelet.getBVec()[10:28],
np.zeros(28 - 10),
err_msg=
"Shapelet setOrder back to original from smaller doesn't fill with zeros."
)
# Test that setting a Shapelet with setNM gives the right profile
shapelet = galsim.Shapelet(sigma=sigma, order=order)
i = 0
for n in range(order + 1):
for m in range(n, -1, -2):
if m == 0:
shapelet.setNM(n, m, bvec[i])
i = i + 1
else:
shapelet.setNM(n, m, bvec[i], bvec[i + 1])
i = i + 2
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setNM disagrees with reference Shapelet")
# Test that setting a Shapelet with setPQ gives the right profile
shapelet = galsim.Shapelet(sigma=sigma, order=order)
i = 0
for n in range(order + 1):
for m in range(n, -1, -2):
p = (n + m) / 2
q = (n - m) / 2
if m == 0:
shapelet.setPQ(p, q, bvec[i])
i = i + 1
else:
shapelet.setPQ(p, q, bvec[i], bvec[i + 1])
i = i + 2
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setPQ disagrees with reference Shapelet")
# Test that the Shapelet setFlux does the same thing as the GSObject setFlux
gsref_shapelet = galsim.GSObject(
ref_shapelet) # Make it opaque to the Shapelet versions
gsref_shapelet.setFlux(23.)
gsref_shapelet.draw(ref_im)
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.setFlux(23.)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet setFlux disagrees with GSObject setFlux")
# Test that the Shapelet scaleFlux does the same thing as the GSObject scaleFlux
gsref_shapelet.scaleFlux(0.23)
gsref_shapelet.draw(ref_im)
shapelet.scaleFlux(0.23)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet setFlux disagrees with SObject scaleFlux")
# Test that the Shapelet applyRotation does the same thing as the GSObject applyRotation
gsref_shapelet.applyRotation(23. * galsim.degrees)
gsref_shapelet.draw(ref_im)
shapelet.applyRotation(23. * galsim.degrees)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet applyRotation disagrees with GSObject applyRotation")
# Test that the Shapelet applyDilation does the same thing as the GSObject applyDilation
gsref_shapelet.applyDilation(1.3)
gsref_shapelet.draw(ref_im)
shapelet.applyDilation(1.3)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet applyDilation disagrees with GSObject applyDilation")
# Test that the Shapelet applyMagnification does the same thing as the GSObject
# applyMagnification
gsref_shapelet.applyMagnification(0.8)
gsref_shapelet.draw(ref_im)
shapelet.applyMagnification(0.8)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg=
"Shapelet applyMagnification disagrees with GSObject applyMagnification"
)
# Test that applyLensing works on Shapelet
gsref_shapelet.applyLensing(-0.05, 0.15, 1.1)
gsref_shapelet.draw(ref_im)
shapelet.applyLensing(-0.05, 0.15, 1.1)
shapelet.draw(im)
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet applyLensing disagrees with GSObject applyLensing")
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_shapelet_draw():
"""Test some measured properties of a drawn shapelet against the supposed true values
"""
import time
t1 = time.time()
ftypes = [np.float32, np.float64]
scale = 0.2
test_flux = 23.
pix = galsim.Pixel(scale)
im = galsim.ImageF(129, 129, scale=scale)
for sigma in [1., 0.3, 2.4]:
for order in [0, 2, 8]:
shapelet = galsim.Shapelet(sigma=sigma, order=order)
shapelet.setNM(0, 0, 1.)
for n in range(1, order + 1):
if n % 2 == 0: # even n
#shapelet.setNM(n,0,0.23/(n*n))
shapelet.setPQ(
n / 2, n / 2,
0.23 / (n * n)) # same thing. Just test setPQ syntax.
if n >= 2:
shapelet.setNM(n, 2, 0.14 / n, -0.08 / n)
else: # odd n
if n >= 1:
shapelet.setNM(n, 1, -0.08 / n**3.2, 0.05 / n**2.1)
if n >= 3:
shapelet.setNM(n, 3, 0.31 / n**4.2, -0.18 / n**3.9)
#print 'shapelet vector = ',shapelet.getBVec()
# Test normalization (This is normally part of do_shoot. When we eventually
# implement photon shooting, we should go back to the normal do_shoot call,
# and remove this section.)
shapelet.setFlux(test_flux)
# Need to convolve with a pixel if we want the flux to come out right.
conv = galsim.Convolve([pix, shapelet])
conv.draw(im, normalization="surface brightness")
flux = im.array.sum()
print 'img.sum = ', flux, ' cf. ', test_flux / (scale * scale)
np.testing.assert_almost_equal(
flux * scale * scale / test_flux,
1.,
4,
err_msg="Surface brightness normalization for Shapelet "
"disagrees with expected result")
conv.draw(im, normalization="flux")
flux = im.array.sum()
print 'im.sum = ', flux, ' cf. ', test_flux
np.testing.assert_almost_equal(
flux / test_flux,
1.,
4,
err_msg=
"Flux normalization for Shapelet disagrees with expected result"
)
# Test centroid
# Note: this only works if the image has odd sizes. If they are even, then
# setCenter doesn't actually set the center to the true center of the image
# (since it falls between pixels).
im.setCenter(0, 0)
x, y = np.meshgrid(
np.arange(im.array.shape[0]).astype(float) + im.getXMin(),
np.arange(im.array.shape[1]).astype(float) + im.getYMin())
x *= scale
y *= scale
flux = im.array.sum()
mx = (x * im.array).sum() / flux
my = (y * im.array).sum() / flux
print 'centroid = ', mx, my, ' cf. ', conv.centroid()
np.testing.assert_almost_equal(
mx,
shapelet.centroid().x,
3,
err_msg=
"Measured centroid (x) for Shapelet disagrees with expected result"
)
np.testing.assert_almost_equal(
my,
shapelet.centroid().y,
3,
err_msg=
"Measured centroid (y) for Shapelet disagrees with expected result"
)
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_shapelet_adjustments():
"""Test that adjusting the Shapelet profile in various ways does the right thing
"""
ftypes = [np.float32, np.float64]
nx = 128
ny = 128
scale = 0.2
im = galsim.ImageF(nx,ny, scale=scale)
sigma = 1.8
order = 6
bvec = [1.3, # n = 0
0.02, 0.03, # n = 1
0.23, -0.19, 0.08, # n = 2
0.01, 0.02, 0.04, -0.03, # n = 3
-0.09, 0.07, -0.11, -0.08, 0.11, # n = 4
-0.03, -0.02, -0.08, 0.01, -0.06, -0.03, # n = 5
0.06, -0.02, 0.00, -0.05, -0.04, 0.01, 0.09 ] # n = 6
ref_shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
ref_im = galsim.ImageF(nx,ny)
ref_shapelet.drawImage(ref_im, scale=scale)
# Test PQ and NM access
np.testing.assert_equal(ref_shapelet.getPQ(0,0), (bvec[0],0))
np.testing.assert_equal(ref_shapelet.getPQ(1,1), (bvec[5],0))
np.testing.assert_equal(ref_shapelet.getPQ(1,2), (bvec[8],-bvec[9]))
np.testing.assert_equal(ref_shapelet.getPQ(3,2), (bvec[19],bvec[20]))
np.testing.assert_equal(ref_shapelet.getNM(0,0), (bvec[0],0))
np.testing.assert_equal(ref_shapelet.getNM(2,0), (bvec[5],0))
np.testing.assert_equal(ref_shapelet.getNM(3,-1), (bvec[8],-bvec[9]))
np.testing.assert_equal(ref_shapelet.getNM(5,1), (bvec[19],bvec[20]))
# Test that the Shapelet withFlux does the same thing as the GSObject withFlux
# Make it opaque to the Shapelet versions
alt_shapelet = ref_shapelet + 0. * galsim.Gaussian(sigma=1)
alt_shapelet.withFlux(23.).drawImage(ref_im, method='no_pixel')
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.withFlux(23.).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet withFlux disagrees with GSObject withFlux")
# Test that scaling the Shapelet flux does the same thing as the GSObject scaling
(alt_shapelet * 0.23).drawImage(ref_im, method='no_pixel')
(shapelet * 0.23).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet *= 0.23 disagrees with GSObject *= 0.23")
# Test that the Shapelet rotate does the same thing as the GSObject rotate
alt_shapelet.rotate(23. * galsim.degrees).drawImage(ref_im, method='no_pixel')
shapelet.rotate(23. * galsim.degrees).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet rotate disagrees with GSObject rotate")
# Test that the Shapelet dilate does the same thing as the GSObject dilate
alt_shapelet.dilate(1.3).drawImage(ref_im, method='no_pixel')
shapelet.dilate(1.3).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet dilate disagrees with GSObject dilate")
# Test that the Shapelet expand does the same thing as the GSObject expand
alt_shapelet.expand(1.7).drawImage(ref_im, method='no_pixel')
shapelet.expand(1.7).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet expand disagrees with GSObject expand")
# Test that the Shapelet magnify does the same thing as the GSObject magnify
alt_shapelet.magnify(0.8).drawImage(ref_im, method='no_pixel')
shapelet.magnify(0.8).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet magnify disagrees with GSObject magnify")
# Test that lens works on Shapelet
alt_shapelet.lens(-0.05, 0.15, 1.1).drawImage(ref_im, method='no_pixel')
shapelet.lens(-0.05, 0.15, 1.1).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet lens disagrees with GSObject lens")
def test_flip():
"""Test several ways to flip a profile
"""
# The Shapelet profile has the advantage of being fast and not circularly symmetric, so
# it is a good test of the actual code for doing the flips (in SBTransform).
# But since the bug Rachel reported in #645 was actually in SBInterpolatedImage
# (one calculation implicitly assumed dx > 0), it seems worthwhile to run through all the
# classes to make sure we hit everything with negative steps for dx and dy.
prof_list = [
galsim.Shapelet(sigma=0.17, order=2,
bvec=[1.7, 0.01,0.03, 0.29, 0.33, -0.18]),
]
if __name__ == "__main__":
image_dir = './real_comparison_images'
catalog_file = 'test_catalog.fits'
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
# Some of these are slow, so only do the Shapelet test as part of the normal unit tests.
prof_list += [
galsim.Airy(lam_over_diam=0.17, flux=1.7),
galsim.Airy(lam_over_diam=0.17, obscuration=0.2, flux=1.7),
# Box gets rendered with real-space convolution. The default accuracy isn't quite
# enough to get the flip to match at 6 decimal places.
galsim.Box(0.17, 0.23, flux=1.7,
gsparams=galsim.GSParams(realspace_relerr=1.e-6)),
# Without being convolved by anything with a reasonable k cutoff, this needs
# a very large fft.
galsim.DeVaucouleurs(half_light_radius=0.17, flux=1.7),
# I don't really understand why this needs a lower maxk_threshold to work, but
# without it, the k-space tests fail.
galsim.Exponential(scale_radius=0.17, flux=1.7,
gsparams=galsim.GSParams(maxk_threshold=1.e-4)),
galsim.Gaussian(sigma=0.17, flux=1.7),
galsim.Kolmogorov(fwhm=0.17, flux=1.7),
galsim.Moffat(beta=2.5, fwhm=0.17, flux=1.7),
galsim.Moffat(beta=2.5, fwhm=0.17, flux=1.7, trunc=0.82),
galsim.OpticalPSF(lam_over_diam=0.17, obscuration=0.2, nstruts=6,
coma1=0.2, coma2=0.5, defocus=-0.1, flux=1.7),
# Like with Box, we need to increase the real-space convolution accuracy.
# This time lowering both relerr and abserr.
galsim.Pixel(0.23, flux=1.7,
gsparams=galsim.GSParams(realspace_relerr=1.e-6,
realspace_abserr=1.e-8)),
# Note: RealGalaxy should not be rendered directly because of the deconvolution.
# Here we convolve it by a Gaussian that is slightly larger than the original PSF.
galsim.Convolve([ galsim.RealGalaxy(rgc, index=0, flux=1.7), # "Real" RealGalaxy
galsim.Gaussian(sigma=0.08) ]),
galsim.Convolve([ galsim.RealGalaxy(rgc, index=1, flux=1.7), # "Fake" RealGalaxy
galsim.Gaussian(sigma=0.08) ]), # (cf. test_real.py)
galsim.Spergel(nu=-0.19, half_light_radius=0.17, flux=1.7),
galsim.Spergel(nu=0., half_light_radius=0.17, flux=1.7),
galsim.Spergel(nu=0.8, half_light_radius=0.17, flux=1.7),
galsim.Sersic(n=2.3, half_light_radius=0.17, flux=1.7),
galsim.Sersic(n=2.3, half_light_radius=0.17, flux=1.7, trunc=0.82),
# The shifts here caught a bug in how SBTransform handled the recentering.
# Two of the shifts (0.125 and 0.375) lead back to 0.0 happening on an integer
# index, which now works correctly.
galsim.Sum([ galsim.Gaussian(sigma=0.17, flux=1.7).shift(-0.2,0.125),
galsim.Exponential(scale_radius=0.23, flux=3.1).shift(0.375,0.23)]),
galsim.TopHat(0.23, flux=1.7),
# Box and Pixel use real-space convolution. Convolve with a Gaussian to get fft.
galsim.Convolve([ galsim.Box(0.17, 0.23, flux=1.7).shift(-0.2,0.1),
galsim.Gaussian(sigma=0.09) ]),
galsim.Convolve([ galsim.TopHat(0.17, flux=1.7).shift(-0.275,0.125),
galsim.Gaussian(sigma=0.09) ]),
# Test something really crazy with several layers worth of transformations
galsim.Convolve([
galsim.Sum([
galsim.Gaussian(sigma=0.17, flux=1.7).shear(g1=0.1,g2=0.2).shift(2,3),
galsim.Kolmogorov(fwhm=0.33, flux=3.9).transform(0.31,0.19,-0.23,0.33) * 88.,
galsim.Box(0.11, 0.44, flux=4).rotate(33 * galsim.degrees) / 1.9
]).shift(-0.3,1),
galsim.AutoConvolve(galsim.TopHat(0.5).shear(g1=0.3,g2=0)).rotate(3*galsim.degrees),
(galsim.AutoCorrelate(galsim.Box(0.2, 0.3)) * 11).shift(3,2).shift(2,-3) * 0.31
]).shift(0,0).transform(0,-1,-1,0).shift(-1,1)
]
s = galsim.Shear(g1=0.11, g2=-0.21)
s1 = galsim.Shear(g1=0.11, g2=0.21) # Appropriate for the flips around x and y axes
s2 = galsim.Shear(g1=-0.11, g2=-0.21) # Appropriate for the flip around x=y
# Also use shears with just a g1 to get dx != dy, but dxy, dyx = 0.
q = galsim.Shear(g1=0.11, g2=0.)
q1 = galsim.Shear(g1=0.11, g2=0.) # Appropriate for the flips around x and y axes
q2 = galsim.Shear(g1=-0.11, g2=0.) # Appropriate for the flip around x=y
decimal=6 # Oddly, these aren't as precise as I would have expected.
# Even when we only go to this many digits of accuracy, the Exponential needed
# a lower than default value for maxk_threshold.
im = galsim.ImageD(16,16, scale=0.05)
for prof in prof_list:
print('prof = ',prof)
# Not all profiles are expected to have a max_sb value close to the maximum pixel value,
# so mark the ones where we don't want to require this to be true.
close_maxsb = True
name = str(prof)
if ('DeVauc' in name or 'Sersic' in name or 'Spergel' in name or
'Optical' in name or 'shift' in name):
close_maxsb = False
# Make sure we hit all 4 fill functions.
# image_x uses fillXValue with izero, jzero
# image_x1 uses fillXValue with izero, jzero, and unequal dx,dy
# image_x2 uses fillXValue with dxy, dyx
# image_k uses fillKValue with izero, jzero
# image_k1 uses fillKValue with izero, jzero, and unequal dx,dy
# image_k2 uses fillKValue with dxy, dyx
image_x = prof.drawImage(image=im.copy(), method='no_pixel')
image_x1 = prof.shear(q).drawImage(image=im.copy(), method='no_pixel')
image_x2 = prof.shear(s).drawImage(image=im.copy(), method='no_pixel')
image_k = prof.drawImage(image=im.copy())
image_k1 = prof.shear(q).drawImage(image=im.copy())
image_k2 = prof.shear(s).drawImage(image=im.copy())
if close_maxsb:
np.testing.assert_allclose(
image_x.array.max(), prof.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image_x1.array.max(), prof.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image_x2.array.max(), prof.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around y axis (i.e. x -> -x)
flip1 = prof.transform(-1, 0, 0, 1)
image2_x = flip1.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, image2_x.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x test")
image2_x1 = flip1.shear(q1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, image2_x1.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x1 test")
image2_x2 = flip1.shear(s1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, image2_x2.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x2 test")
image2_k = flip1.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, image2_k.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k test")
image2_k1 = flip1.shear(q1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, image2_k1.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k1 test")
image2_k2 = flip1.shear(s1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, image2_k2.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip1.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip1.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip1.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around x axis (i.e. y -> -y)
flip2 = prof.transform(1, 0, 0, -1)
image2_x = flip2.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, image2_x.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x test")
image2_x1 = flip2.shear(q1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, image2_x1.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x1 test")
image2_x2 = flip2.shear(s1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, image2_x2.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x2 test")
image2_k = flip2.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, image2_k.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k test")
image2_k1 = flip2.shear(q1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, image2_k1.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k1 test")
image2_k2 = flip2.shear(s1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, image2_k2.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip2.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip2.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip2.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around x=y (i.e. y -> x, x -> y)
flip3 = prof.transform(0, 1, 1, 0)
image2_x = flip3.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, np.transpose(image2_x.array), decimal=decimal,
err_msg="Flipping image around x=y failed x test")
image2_x1 = flip3.shear(q2).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, np.transpose(image2_x1.array), decimal=decimal,
err_msg="Flipping image around x=y failed x1 test")
image2_x2 = flip3.shear(s2).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, np.transpose(image2_x2.array), decimal=decimal,
err_msg="Flipping image around x=y failed x2 test")
image2_k = flip3.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, np.transpose(image2_k.array), decimal=decimal,
err_msg="Flipping image around x=y failed k test")
image2_k1 = flip3.shear(q2).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, np.transpose(image2_k1.array), decimal=decimal,
err_msg="Flipping image around x=y failed k1 test")
image2_k2 = flip3.shear(s2).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, np.transpose(image2_k2.array), decimal=decimal,
err_msg="Flipping image around x=y failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip3.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip3.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip3.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
do_pickle(prof, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip1, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip2, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip3, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(prof)
do_pickle(flip1)
do_pickle(flip2)
do_pickle(flip3)
def test_dep_shapelet():
"""Test the deprecated methods in galsim/deprecated/shapelet.py
"""
np.testing.assert_almost_equal(check_dep(galsim.LVectorSize, 12),
galsim.ShapeletSize(12))
# The next bit is from the old test_shapelet_adjustments() test
ftypes = [np.float32, np.float64]
nx = 128
ny = 128
scale = 0.2
im = galsim.ImageF(nx, ny, scale=scale)
sigma = 1.8
order = 6
bvec = [
1.3, # n = 0
0.02,
0.03, # n = 1
0.23,
-0.19,
0.08, # n = 2
0.01,
0.02,
0.04,
-0.03, # n = 3
-0.09,
0.07,
-0.11,
-0.08,
0.11, # n = 4
-0.03,
-0.02,
-0.08,
0.01,
-0.06,
-0.03, # n = 5
0.06,
-0.02,
0.00,
-0.05,
-0.04,
0.01,
0.09
] # n = 6
ref_shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
ref_im = galsim.ImageF(nx, ny)
ref_shapelet.drawImage(ref_im, scale=scale, method='no_pixel')
# test setsigma
shapelet = galsim.Shapelet(sigma=1., order=order, bvec=bvec)
check_dep(shapelet.setSigma, sigma)
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setSigma disagrees with reference Shapelet")
# Test setBVec
shapelet = galsim.Shapelet(sigma=sigma, order=order)
check_dep(shapelet.setBVec, bvec)
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setBVec disagrees with reference Shapelet")
# Test setOrder
shapelet = galsim.Shapelet(sigma=sigma, order=2)
check_dep(shapelet.setOrder, order)
check_dep(shapelet.setBVec, bvec)
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setOrder disagrees with reference Shapelet")
# Test that changing the order preserves the values to the extent possible.
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
check_dep(shapelet.setOrder, 10)
np.testing.assert_array_equal(
shapelet.getBVec()[0:28],
bvec,
err_msg="Shapelet setOrder to larger doesn't preserve existing values."
)
np.testing.assert_array_equal(
shapelet.getBVec()[28:66],
np.zeros(66 - 28),
err_msg="Shapelet setOrder to larger doesn't fill with zeros.")
check_dep(shapelet.setOrder, 6)
np.testing.assert_array_equal(
shapelet.getBVec(),
bvec,
err_msg=
"Shapelet setOrder back to original from larger doesn't preserve existing values."
)
check_dep(shapelet.setOrder, 3)
np.testing.assert_array_equal(
shapelet.getBVec()[0:10],
bvec[0:10],
err_msg="Shapelet setOrder to smaller doesn't preserve existing values."
)
check_dep(shapelet.setOrder, 6)
np.testing.assert_array_equal(
shapelet.getBVec()[0:10],
bvec[0:10],
err_msg=
"Shapelet setOrder back to original from smaller doesn't preserve existing values."
)
check_dep(shapelet.setOrder, 6)
np.testing.assert_array_equal(
shapelet.getBVec()[10:28],
np.zeros(28 - 10),
err_msg=
"Shapelet setOrder back to original from smaller doesn't fill with zeros."
)
# Test that setting a Shapelet with setNM gives the right profile
shapelet = galsim.Shapelet(sigma=sigma, order=order)
i = 0
for n in range(order + 1):
for m in range(n, -1, -2):
if m == 0:
check_dep(shapelet.setNM, n, m, bvec[i])
i = i + 1
else:
check_dep(shapelet.setNM, n, m, bvec[i], bvec[i + 1])
i = i + 2
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setNM disagrees with reference Shapelet")
# Test that setting a Shapelet with setPQ gives the right profile
shapelet = galsim.Shapelet(sigma=sigma, order=order)
i = 0
for n in range(order + 1):
for m in range(n, -1, -2):
p = (n + m) // 2
q = (n - m) // 2
if m == 0:
check_dep(shapelet.setPQ, p, q, bvec[i])
i = i + 1
else:
check_dep(shapelet.setPQ, p, q, bvec[i], bvec[i + 1])
i = i + 2
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array,
ref_im.array,
6,
err_msg="Shapelet set with setPQ disagrees with reference Shapelet")
# Check fitImage
s1 = galsim.Shapelet(sigma=sigma, order=10)
check_dep(s1.fitImage, image=im)
s2 = galsim.FitShapelet(sigma=sigma, order=10, image=im)
np.testing.assert_array_almost_equal(s1.getBVec(), s2.getBVec())
def test_shapelet_fit():
"""Test fitting a Shapelet decomposition of an image
"""
import time
t1 = time.time()
for norm in ['f', 'sb']:
# We fit a shapelet approximation of a distorted Moffat profile:
flux = 20
psf = galsim.Moffat(beta=3.4, half_light_radius=1.2, flux=flux)
psf.applyShear(g1=0.11, g2=0.07)
psf.applyShift(0.03, 0.04)
scale = 0.2
pixel = galsim.Pixel(scale)
conv = galsim.Convolve([psf, pixel])
im1 = conv.draw(scale=scale, normalization=norm)
sigma = 1.2 # Match half-light-radius as a decent first approximation.
shapelet = galsim.Shapelet(sigma=sigma, order=10)
shapelet.fitImage(im1, normalization=norm)
#print 'fitted shapelet coefficients = ',shapelet.getBVec()
# Check flux
print 'flux = ', shapelet.getFlux(), ' cf. ', flux
np.testing.assert_almost_equal(
shapelet.getFlux() / flux,
1.,
1,
err_msg="Fitted shapelet has the wrong flux")
# Test centroid
print 'centroid = ', shapelet.centroid(), ' cf. ', conv.centroid()
np.testing.assert_almost_equal(
shapelet.centroid().x,
conv.centroid().x,
2,
err_msg="Fitted shapelet has the wrong centroid (x)")
np.testing.assert_almost_equal(
shapelet.centroid().y,
conv.centroid().y,
2,
err_msg="Fitted shapelet has the wrong centroid (y)")
# Test drawing image from shapelet
im2 = im1.copy()
shapelet.draw(im2, normalization=norm)
# Check that images are close to the same:
print 'norm(diff) = ', np.sum((im1.array - im2.array)**2)
print 'norm(im) = ', np.sum(im1.array**2)
assert np.sum(
(im1.array - im2.array)**2) < 1.e-3 * np.sum(im1.array**2)
# Remeasure -- should now be very close to the same.
shapelet2 = shapelet.copy()
shapelet2.fitImage(im2, normalization=norm)
np.testing.assert_equal(
shapelet.getSigma(),
shapelet2.getSigma(),
err_msg="Second fitted shapelet has the wrong sigma")
np.testing.assert_equal(
shapelet.getOrder(),
shapelet2.getOrder(),
err_msg="Second fitted shapelet has the wrong order")
np.testing.assert_almost_equal(
shapelet.getBVec(),
shapelet2.getBVec(),
6,
err_msg="Second fitted shapelet coefficients do not match original"
)
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_shapelet_drawImage():
"""Test some measured properties of a drawn shapelet against the supposed true values
"""
import time
t1 = time.time()
ftypes = [np.float32, np.float64]
scale = 0.2
test_flux = 23.
im = galsim.ImageF(129,129, scale=scale)
for sigma in [1., 0.3, 2.4]:
for order in [0, 2, 8]:
bvec = np.zeros(galsim.ShapeletSize(order))
bvec[0] = 1. # N,m = 0,0
k = 0
for n in range(1,order+1):
k += n+1
if n%2 == 0: # even n
bvec[k] = 0.23/(n*n) # N,m = n,0 or p,q = n/2,n/2
if n >= 2:
bvec[k-2] = 0.14/n # N,m = n,2 real part
bvec[k-1] = -0.08/n # N,m = n,2 imag part
else: # odd n
if n >= 1:
bvec[k-1] = -0.08/n**3.2 # N,m = n,1 real part
bvec[k] = 0.05/n**2.1 # N,m = n,1 imag part
if n >= 3:
bvec[k-3] = 0.31/n**4.2 # N,m = n,3 real part
bvec[k-2] = -0.18/n**3.9 # N,m = n,3 imag part
print 'shapelet vector = ',bvec
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
# Test normalization (This is normally part of do_shoot. When we eventually
# implement photon shooting, we should go back to the normal do_shoot call,
# and remove this section.)
shapelet = shapelet.withFlux(test_flux)
shapelet.drawImage(im)
flux = im.array.sum()
print 'im.sum = ',flux,' cf. ',test_flux
np.testing.assert_almost_equal(flux / test_flux, 1., 4,
err_msg="Flux normalization for Shapelet disagrees with expected result")
# Test centroid
# Note: this only works if the image has odd sizes. If they are even, then
# setCenter doesn't actually set the center to the true center of the image
# (since it falls between pixels).
im.setCenter(0,0)
x,y = np.meshgrid(np.arange(im.array.shape[0]).astype(float) + im.getXMin(),
np.arange(im.array.shape[1]).astype(float) + im.getYMin())
x *= scale
y *= scale
flux = im.array.sum()
mx = (x*im.array).sum() / flux
my = (y*im.array).sum() / flux
conv = galsim.Convolve([shapelet, galsim.Pixel(scale)])
print 'centroid = ',mx,my,' cf. ',conv.centroid()
np.testing.assert_almost_equal(mx, shapelet.centroid().x, 3,
err_msg="Measured centroid (x) for Shapelet disagrees with expected result")
np.testing.assert_almost_equal(my, shapelet.centroid().y, 3,
err_msg="Measured centroid (y) for Shapelet disagrees with expected result")
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def test_shapelet_adjustments():
"""Test that adjusting the Shapelet profile in various ways does the right thing
"""
import time
t1 = time.time()
ftypes = [np.float32, np.float64]
nx = 128
ny = 128
scale = 0.2
im = galsim.ImageF(nx,ny, scale=scale)
sigma = 1.8
order = 6
bvec = [1.3, # n = 0
0.02, 0.03, # n = 1
0.23, -0.19, 0.08, # n = 2
0.01, 0.02, 0.04, -0.03, # n = 3
-0.09, 0.07, -0.11, -0.08, 0.11, # n = 4
-0.03, -0.02, -0.08, 0.01, -0.06, -0.03, # n = 5
0.06, -0.02, 0.00, -0.05, -0.04, 0.01, 0.09 ] # n = 6
ref_shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
ref_im = galsim.ImageF(nx,ny)
ref_shapelet.drawImage(ref_im, scale=scale)
# Test that the Shapelet withFlux does the same thing as the GSObject withFlux
gsref_shapelet = galsim.GSObject(ref_shapelet) # Make it opaque to the Shapelet versions
gsref_shapelet.withFlux(23.).drawImage(ref_im, method='no_pixel')
shapelet = galsim.Shapelet(sigma=sigma, order=order, bvec=bvec)
shapelet.withFlux(23.).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet withFlux disagrees with GSObject withFlux")
# Test that scaling the Shapelet flux does the same thing as the GSObject scaling
gsref_shapelet *= 0.23
gsref_shapelet.drawImage(ref_im, method='no_pixel')
shapelet *= 0.23
shapelet.drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet *= 0.23 disagrees with GSObject *= 0.23")
# Test that the Shapelet rotate does the same thing as the GSObject rotate
gsref_shapelet.rotate(23. * galsim.degrees).drawImage(ref_im, method='no_pixel')
shapelet.rotate(23. * galsim.degrees).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet rotate disagrees with GSObject rotate")
# Test that the Shapelet dilate does the same thing as the GSObject dilate
gsref_shapelet.dilate(1.3).drawImage(ref_im, method='no_pixel')
shapelet.dilate(1.3).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet dilate disagrees with GSObject dilate")
# Test that the Shapelet magnify does the same thing as the GSObject magnify
gsref_shapelet.magnify(0.8).drawImage(ref_im, method='no_pixel')
shapelet.magnify(0.8).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet magnify disagrees with GSObject magnify")
# Test that lens works on Shapelet
gsref_shapelet.lens(-0.05, 0.15, 1.1).drawImage(ref_im, method='no_pixel')
shapelet.lens(-0.05, 0.15, 1.1).drawImage(im, method='no_pixel')
np.testing.assert_array_almost_equal(
im.array, ref_im.array, 6,
err_msg="Shapelet lens disagrees with GSObject lens")
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
