block_diagonal_operator.py

# 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 <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

from __future__ import absolute_import, division, print_function

from ..compat import *
from .endomorphic_operator import EndomorphicOperator
from ..multi_domain import MultiDomain
from ..multi_field import MultiField


class BlockDiagonalOperator(EndomorphicOperator):
    def __init__(self, domain, operators):
        """
        Parameters
        ----------
        operators : dict
            dictionary with operators domain names as keys and
            LinearOperators as items
        """
        if not isinstance(domain, MultiDomain):
            raise TypeError("MultiDomain expected")
        if not isinstance(operators, tuple):
            raise TypeError("tuple expected")
        self._domain = domain
        self._ops = operators
        self._capability = self._all_ops
        for op in self._ops:
            if op is not None:
                self._capability &= op.capability

    def apply(self, x, mode):
        self._check_input(x, mode)
        val = tuple(op.apply(v, mode=mode) if op is not None else None
                    for op, v in zip(self._ops, x.values()))
        return MultiField(self._domain, val)

#    def draw_sample(self, from_inverse=False, dtype=np.float64):
#        dtype = MultiField.build_dtype(dtype, self._domain)
#        val = tuple(op.draw_sample(from_inverse, dtype)
#                    for op in self._op)
#        return MultiField(self._domain, val)

    def _combine_chain(self, op):
        if self._domain != op._domain:
            raise ValueError("domain mismatch")
        res = tuple(v1(v2) for v1, v2 in zip(self._ops, op._ops))
        return BlockDiagonalOperator(self._domain, res)

    def _combine_sum(self, op, selfneg, opneg):
        from ..operators.sum_operator import SumOperator
        if self._domain != op._domain:
            raise ValueError("domain mismatch")
        res = tuple(SumOperator.make([v1, v2], [selfneg, opneg])
                    for v1, v2 in zip(self._ops, op._ops))
        return BlockDiagonalOperator(self._domain, res)