Commit 1bb2c55c authored by Martin Reinecke's avatar Martin Reinecke Committed by Lukas Platz
Browse files

Added Martin's 'apply_spherical_convolution' function as template

parent 9e644972
......@@ -147,3 +147,40 @@ class LMSpace(StructuredDomain):
from import HPSpace
if not isinstance(codomain, (GLSpace, HPSpace)):
raise TypeError("codomain must be a GLSpace or HPSpace.")
def apply_spherical_convolution(inp, func):
"""Convolves `inp` with a kernel defined by `func`
which is assumed to be radially symmetric around theta==0."""
import pyHealpix
from .gl_space import GLSpace
from ..operators.harmonic_operators import HarmonicTransformOperator
if len(inp.domain) != 1:
raise ValueError("need exactly one domain")
sph = inp.domain[0]
# define an appropriate harmonic partner space
lm = sph.get_default_codomain()
if not isinstance(lm, LMSpace):
raise TypeError("need a spherical domain")
# define azimuthally symmetric spaces for kernel transform
gl = GLSpace(lm.lmax + 1, 1)
lm0 = gl.get_default_codomain()
theta = pyHealpix.GL_thetas(gl.nlat)
# evaluate the kernel function at the required thetas
kernel_sphere = Field.from_global_data(gl, func(theta))
# normalize the kernel such that the integral over the sphere is 4pi
kernel_sphere = kernel_sphere * (4 * np.pi / kernel_sphere.integrate())
# compute the spherical harmonic coefficients of the kernel
op = HarmonicTransformOperator(lm0, gl)
kernel_lm = op.adjoint_times(kernel_sphere.weight(1)).to_global_data()
# evaluate the k lengths of the harmonic space
k_lengths = lm.get_k_length_array().to_global_data().astype(
op = HarmonicTransformOperator(lm, sph)
# "inverse" transform to harmonic space
inp_lm = op.adjoint_times(inp.weight(1)).to_global_data()
# multiply the kernel to the coefficients and adjust normalization
inp_lm = inp_lm * kernel_lm[k_lengths] * 4 * np.pi
out = Field.from_global_data(lm, inp_lm)
# back to the original space
return op(out)
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