def test_radial_profile():
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gauss = Gaussian()
x, y = util.make_grid(11, 1)
flux = gauss.function(x, y, sigma=10, amp=1)
data = util.array2image(flux)
profile_r = image_util.radial_profile(data, center=[5, 5])
profile_r_true = gauss.function(np.linspace(0, stop=7, num=8), 0, sigma=10, amp=1)
npt.assert_almost_equal(profile_r, profile_r_true, decimal=3)
def mge_kernel(kernel, order=5):
"""
azimutal Multi-Gaussian expansion of a pixelized kernel
:param kernel: 2d numpy array
:return:
"""
# radial average
n = len(kernel)
center = (n - 1) / 2.
psf_r = image_util.radial_profile(kernel, center=[center, center])
# MGE of radial average
n_r = len(psf_r)
r_array = np.linspace(start=0., stop=n_r - 1, num=n_r)
amps, sigmas, norm = mge.mge_1d(r_array, psf_r, N=order, linspace=True)
return amps, sigmas, norm
def fwhm_kernel(kernel):
"""
:param kernel:
:return:
"""
n = len(kernel)
center = (n - 1) / 2.
I_r = image_util.radial_profile(kernel, center=[center, center])
if n % 2 == 0:
raise ValueError('only works with odd number of pixels in kernel!')
max_flux = kernel[int((n-1)/2), int((n-1)/2)]
I_2 = max_flux / 2.
r = np.linspace(0, (n - 1) / 2, int((n + 1) / 2)) + 0.33
for i in range(1, len(I_r)):
if I_r[i] < I_2:
fwhm_2 = (I_2 - I_r[i - 1]) / (I_r[i] - I_r[i - 1]) + r[i - 1]
return fwhm_2 * 2
raise ValueError('The kernel did not drop to half the max value - fwhm not determined!')
