block_diagonal_operator.py 2.91 KB
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# 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.

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from __future__ import absolute_import, division, print_function
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from ..compat import *
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from .endomorphic_operator import EndomorphicOperator
from ..multi_domain import MultiDomain
from ..multi_field import MultiField
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class BlockDiagonalOperator(EndomorphicOperator):
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    def __init__(self, domain, operators):
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        """
        Parameters
        ----------
        operators : dict
            dictionary with operators domain names as keys and
            LinearOperators as items
        """
        super(BlockDiagonalOperator, self).__init__()
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        if not isinstance(domain, MultiDomain):
            raise TypeError("MultiDomain expected")
        if not isinstance(operators, tuple):
            raise TypeError("tuple expected")
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        self._domain = domain
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        self._ops = operators
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        self._cap = self._all_ops
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        for op in self._ops:
            if op is not None:
                self._cap &= op.capability
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    @property
    def capability(self):
        return self._cap

    def apply(self, x, mode):
        self._check_input(x, mode)
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        val = tuple(op.apply(v, mode=mode) if op is not None else None
                    for op, v in zip(self._ops, x.values()))
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        return MultiField(self._domain, val)
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#    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)
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    def _combine_chain(self, op):
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        if self._domain is not op._domain:
            raise ValueError("domain mismatch")
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        res = tuple(v1(v2) for v1, v2 in zip(self._ops, op._ops))
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        return BlockDiagonalOperator(self._domain, res)
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    def _combine_sum(self, op, selfneg, opneg):
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        from ..operators.sum_operator import SumOperator
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        if self._domain is not op._domain:
            raise ValueError("domain mismatch")
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        res = tuple(SumOperator.make([v1, v2], [selfneg, opneg])
                    for v1, v2 in zip(self._ops, op._ops))
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        return BlockDiagonalOperator(self._domain, res)