Source code for nifty8.operators.convolution_operators
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
from .. import utilities
from ..domain_tuple import DomainTuple
from ..domains.gl_space import GLSpace
from ..domains.hp_space import HPSpace
from ..domains.rg_space import RGSpace
from .diagonal_operator import DiagonalOperator
from .endomorphic_operator import EndomorphicOperator
from .harmonic_operators import HarmonicTransformOperator
from .simple_linear_operators import WeightApplier
[docs]
def FuncConvolutionOperator(domain, func, space=None):
"""Convolves input with a radially symmetric kernel defined by `func`
Parameters
----------
domain: DomainTuple
Domain of the operator.
func: function
This function needs to take exactly one argument, which is
colatitude in radians, and return the kernel amplitude at that
colatitude.
space: int, optional
The index of the subdomain on which the operator should act
If None, it is set to 0 if `domain` contains exactly one space.
`domain[space]` must be of type `RGSpace`, `HPSpace`, or `GLSpace`.
Notes
-----
The operator assumes periodic boundaries in the input domain. This means
for a sufficiently broad function a point source close to the boundary will
blur into the opposite side of the image. Zero padding can be applied to
avoid this behaviour.
"""
domain = DomainTuple.make(domain)
space = utilities.infer_space(domain, space)
if not isinstance(domain[space], (RGSpace, HPSpace, GLSpace)):
raise TypeError("unsupported domain")
codomain = domain[space].get_default_codomain()
kernel = codomain.get_conv_kernel_from_func(func)
return _ConvolutionOperator(domain, kernel, space)
def _ConvolutionOperator(domain, kernel, space=None):
domain = DomainTuple.make(domain)
space = utilities.infer_space(domain, space)
if len(kernel.domain) != 1:
raise ValueError("kernel needs exactly one domain")
if not isinstance(domain[space], (HPSpace, GLSpace, RGSpace)):
raise TypeError("need RGSpace, HPSpace, or GLSpace")
lm = [d for d in domain]
lm[space] = lm[space].get_default_codomain()
lm = DomainTuple.make(lm)
utilities.check_object_identity(lm[space], kernel.domain[0])
HT = HarmonicTransformOperator(lm, domain[space], space)
diag = DiagonalOperator(kernel*domain[space].total_volume, lm, (space,))
wgt = WeightApplier(domain, space, 1)
op = HT(diag(HT.adjoint(wgt)))
return _ApplicationWithoutMeanOperator(op)
class _ApplicationWithoutMeanOperator(EndomorphicOperator):
def __init__(self, op):
self._capability = self.TIMES | self.ADJOINT_TIMES
if op.domain != op.target:
raise TypeError("Operator needs to be endomorphic")
self._domain = op.domain
self._op = op
def apply(self, x, mode):
self._check_input(x, mode)
mean = x.s_mean()
return mean + self._op.apply(x - mean, mode)
def __repr__(self):
from ..utilities import indent
return "\n".join((
"_ApplicationWithoutMeanOperator:",
indent(self._op.__repr__())))