def test_Cubic_ref():
"""Test use of Cubic interpolant against some reference values
"""
import time
t1 = time.time()
interp = galsim.Cubic(tol=1.e-4)
testobj = galsim.InterpolatedImage(ref_image.view(),
x_interpolant=interp,
dx=dx,
normalization='sb')
testKvals = np.zeros(len(KXVALS))
# Make test kValues
for i in xrange(len(KXVALS)):
posk = galsim.PositionD(KXVALS[i], KYVALS[i])
testKvals[i] = np.abs(testobj.kValue(posk))
# Compare with saved array
refKvals = np.loadtxt(os.path.join(TESTDIR, "absfKCubic_test.txt"))
print 'ref = ', refKvals
print 'test = ', testKvals
print 'kValue(0) = ', testobj.kValue(galsim.PositionD(0., 0.))
np.testing.assert_array_almost_equal(
refKvals / testKvals,
1.,
5,
err_msg="kValues do not match reference values for Cubic interpolant.")
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_Cubic_spline():
"""Test the spline tabulation of the k space Cubic interpolant.
"""
import time
t1 = time.time()
interp = galsim.InterpolantXY(galsim.Cubic(tol=1.e-4))
testobj = galsim.SBInterpolatedImage(image.view(), interp, dx=dx)
testKvals = np.zeros(len(KXVALS))
# Make test kValues
for i in xrange(len(KXVALS)):
posk = galsim.PositionD(KXVALS[i], KYVALS[i])
testKvals[i] = np.abs(testobj.kValue(posk))
# Compare with saved array
refKvals = np.loadtxt(os.path.join(TESTDIR, "absfKCubic_test.txt"))
np.testing.assert_array_almost_equal(
refKvals,
testKvals,
DECIMAL,
err_msg="Spline-interpolated kValues do not match saved " +
"data for k space Cubic interpolant.")
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
g2 = galsim.Gaussian(sigma = 1.9, flux=3.1)
g2.applyShear(-0.4,0.3)
g2.applyShift(-0.3,0.5)
g3 = galsim.Gaussian(sigma = 4.1, flux=1.6)
g3.applyShear(0.1,-0.1)
g3.applyShift(0.7,-0.2)
final = g1 + g2 + g3
image = galsim.ImageD(128,128)
dx = 0.4
final.draw(image=image, dx=dx)
dir = '../../../tests/interpolant_comparison_files'
# First make a Cubic interpolant
interp = galsim.InterpolantXY(galsim.Cubic(tol=1.e-4))
testobj = galsim.SBInterpolatedImage(image.view(), interp, dx=dx)
for i in xrange(len(kxvals)):
posk = galsim.PositionD(kxvals[i], kyvals[i])
absoutk[i] = np.abs(testobj.kValue(posk))
print absoutk
np.savetxt(os.path.join(dir,'absfKCubic_test.txt'), absoutk)
# Then make a Quintic interpolant
interp = galsim.InterpolantXY(galsim.Quintic(tol=1.e-4))
testobj = galsim.SBInterpolatedImage(image.view(), interp, dx=dx)
for i in xrange(len(kxvals)):
posk = galsim.PositionD(kxvals[i], kyvals[i])
absoutk[i] = np.abs(testobj.kValue(posk))
print absoutk
np.savetxt(os.path.join(dir,'absfKQuintic_test.txt'), absoutk)
The parameters of the Sersic images come from a COSMOS best-fitting Sersic model catalog.
"""
import cPickle
import numpy as np
import galsim
import test_interpolants
SERSIC_IMAGE_SIZE = 512 # For initial image of the Sersic at Hubble resolution, make nice and large
TEST_IMAGE_SIZE = SERSIC_IMAGE_SIZE # For speed could make this smaller
# Dictionary for parsing the test_interpolants.interpolant_list into galsim Interpolants
INTERPOLANT_DICT = {
"nearest": galsim.Nearest(),
"sinc": galsim.SincInterpolant(),
"linear": galsim.Linear(),
"cubic": galsim.Cubic(),
"quintic": galsim.Quintic(),
"lanczos3": galsim.Lanczos(3),
"lanczos4": galsim.Lanczos(4),
"lanczos5": galsim.Lanczos(5),
"lanczos7": galsim.Lanczos(7)
}
# Output filenames
DELTA_FILENAME = 'interpolant_test_parametric_output_delta.dat'
ORIGINAL_FILENAME = 'interpolant_test_parametric_output_original.dat'
NITEMS = 30 # For now, look at a few only
LAM_OVER_DIAM_COSMOS = 814.e-9 / 2.4 # All the original images in Melanie's tests were from COSMOS
# F814W, so this is a crude approximation to the PSF scale in
def test_interpolated_image():
"""Test various ways to build an InterpolatedImage
"""
imgdir = 'SBProfile_comparison_images'
file_name = os.path.join(imgdir,'gauss_smallshear.fits')
imgdir2 = 'fits_files'
file_name2 = os.path.join(imgdir2,'interpim_hdu_test.fits')
config = {
'gal1' : { 'type' : 'InterpolatedImage', 'image' : file_name },
'gal2' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'x_interpolant' : 'linear' },
'gal3' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'x_interpolant' : 'cubic', 'normalization' : 'sb', 'flux' : 1.e4
},
'gal4' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'x_interpolant' : 'lanczos5', 'scale' : 0.7, 'flux' : 1.e5
},
'gal5' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'noise_pad' : 0.001
},
'gal6' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'noise_pad' : 'fits_files/blankimg.fits'
},
'gal7' : { 'type' : 'InterpolatedImage', 'image' : file_name,
'pad_image' : 'fits_files/blankimg.fits'
},
'galmulti' : { 'type' : 'InterpolatedImage', 'image' : file_name2,
'hdu' : 2 }
}
rng = galsim.UniformDeviate(1234)
config['rng'] = galsim.UniformDeviate(1234) # A second copy starting with the same seed.
gal1a = galsim.config.BuildGSObject(config, 'gal1')[0]
im = galsim.fits.read(file_name)
gal1b = galsim.InterpolatedImage(im)
gsobject_compare(gal1a, gal1b)
gal2a = galsim.config.BuildGSObject(config, 'gal2')[0]
gal2b = galsim.InterpolatedImage(im, x_interpolant=galsim.Linear())
gsobject_compare(gal2a, gal2b)
gal3a = galsim.config.BuildGSObject(config, 'gal3')[0]
gal3b = galsim.InterpolatedImage(im, x_interpolant=galsim.Cubic(), normalization='sb',
flux=1.e4)
gsobject_compare(gal3a, gal3b)
gal4a = galsim.config.BuildGSObject(config, 'gal4')[0]
interp = galsim.Lanczos(n=5, conserve_dc=True)
gal4b = galsim.InterpolatedImage(im, x_interpolant=interp, scale=0.7, flux=1.e5)
gsobject_compare(gal4a, gal4b)
gal5a = galsim.config.BuildGSObject(config, 'gal5')[0]
gal5b = galsim.InterpolatedImage(im, rng=rng, noise_pad=0.001)
gsobject_compare(gal5a, gal5b)
gal6a = galsim.config.BuildGSObject(config, 'gal6')[0]
gal6b = galsim.InterpolatedImage(im, rng=rng, noise_pad='fits_files/blankimg.fits')
gsobject_compare(gal6a, gal6b)
gal7a = galsim.config.BuildGSObject(config, 'gal7')[0]
gal7b = galsim.InterpolatedImage(im, pad_image = 'fits_files/blankimg.fits')
gsobject_compare(gal7a, gal7b)
# Now test the reading from some particular HDU
galmulti = galsim.config.BuildGSObject(config, 'galmulti')[0]
im = galmulti.drawImage(scale=0.2, method='no_pixel')
test_g2 = im.FindAdaptiveMom().observed_shape.g2
np.testing.assert_almost_equal(
test_g2, 0.7, decimal=3,
err_msg='Did not get right shape image after reading InterpolatedImage from HDU')