mask_operator.py 2.13 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/>.
#
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# Copyright(C) 2013-2019 Max-Planck-Society
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#
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# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
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import numpy as np
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from ..domain_tuple import DomainTuple
from ..domains.unstructured_domain import UnstructuredDomain
from ..field import Field
from .linear_operator import LinearOperator


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# MR FIXME: this needs a redesign to avoid most _global_data() calls
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# Possible approach: keep everything defined on `domain` distributed and only
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# collect the unstructured Fields.
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class MaskOperator(LinearOperator):
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    """Implementation of a mask response
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    Takes a field, applies flags and returns the values of the field in a
    :class:`UnstructuredDomain`.
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    Parameters
    ----------
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    flags : Field
        Is converted to boolean. Where True, the input field is flagged.
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    """
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    def __init__(self, flags):
        if not isinstance(flags, Field):
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            raise TypeError
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        self._domain = DomainTuple.make(flags.domain)
        self._flags = np.logical_not(flags.to_global_data())
        self._target = DomainTuple.make(UnstructuredDomain(self._flags.sum()))
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        self._capability = self.TIMES | self.ADJOINT_TIMES
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    def apply(self, x, mode):
        self._check_input(x, mode)
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        x = x.to_global_data()
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        if mode == self.TIMES:
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            res = x[self._flags]
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            return Field.from_global_data(self.target, res)
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        res = np.empty(self.domain.shape, x.dtype)
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        res[self._flags] = x
        res[~self._flags] = 0
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        return Field.from_global_data(self.domain, res)