# 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.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see .
#
# Copyright(C) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import numpy as np
from .. import utilities
from ..domain_tuple import DomainTuple
from ..field import Field
from .linear_operator import LinearOperator
class ContractionOperator(LinearOperator):
"""A :class:`LinearOperator` which sums up fields into the direction of
subspaces.
This Operator sums up a field with is defined on a :class:`DomainTuple`
to a :class:`DomainTuple` which contains the former as a subset.
Parameters
----------
domain : Domain, tuple of Domain or DomainTuple
spaces : int or tuple of int
The elements of "domain" which are contracted.
weight : int, default=0
If nonzero, the fields defined on self.domain are weighted with the
specified power.
"""
def __init__(self, domain, spaces, weight=0):
self._domain = DomainTuple.make(domain)
self._spaces = utilities.parse_spaces(spaces, len(self._domain))
self._target = [
dom for i, dom in enumerate(self._domain) if i not in self._spaces
]
self._target = DomainTuple.make(self._target)
self._weight = weight
self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
if mode == self.ADJOINT_TIMES:
ldat = x.to_global_data() if 0 in self._spaces else x.local_data
shp = []
for i, dom in enumerate(self._domain):
tmp = dom.shape if i > 0 else dom.local_shape
shp += tmp if i not in self._spaces else (1,)*len(dom.shape)
ldat = np.broadcast_to(ldat.reshape(shp), self._domain.local_shape)
res = Field.from_local_data(self._domain, ldat)
if self._weight != 0:
res = res.weight(self._weight, spaces=self._spaces)
return res
else:
if self._weight != 0:
x = x.weight(self._weight, spaces=self._spaces)
res = x.sum(self._spaces)
return res if isinstance(res, Field) else Field.scalar(res)