Source code for nifty7.operators.convolution_operators

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# Copyright(C) 2013-2019 Max-Planck-Society
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# 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) if lm[space] != kernel.domain[0]: raise ValueError("Input domain and kernel are incompatible") 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__())))