operator.py 9.05 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) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
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import numpy as np
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from ..utilities import NiftyMetaBase, indent
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class Operator(NiftyMetaBase()):
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    """Transforms values defined on one domain into values defined on another
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    domain, and can also provide the Jacobian.
    """

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    @property
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    def domain(self):
        """DomainTuple or MultiDomain : the operator's input domain

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            The domain on which the Operator's input Field is defined."""
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        return self._domain
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    @property
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    def target(self):
        """DomainTuple or MultiDomain : the operator's output domain

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            The domain on which the Operator's output Field is defined."""
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        return self._target
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    @staticmethod
    def _check_domain_equality(dom_op, dom_field):
        if dom_op != dom_field:
            s = "The operator's and field's domains don't match."
            from ..domain_tuple import DomainTuple
            from ..multi_domain import MultiDomain
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            if not isinstance(dom_op, (DomainTuple, MultiDomain,)):
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                s += " Your operator's domain is neither a `DomainTuple`" \
                     " nor a `MultiDomain`."
            raise ValueError(s)

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    def scale(self, factor):
        if factor == 1:
            return self
        from .scaling_operator import ScalingOperator
        return ScalingOperator(factor, self.target)(self)

    def conjugate(self):
        from .simple_linear_operators import ConjugationOperator
        return ConjugationOperator(self.target)(self)

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    @property
    def real(self):
        from .simple_linear_operators import Realizer
        return Realizer(self.target)(self)

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    def __neg__(self):
        return self.scale(-1)

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    def __matmul__(self, x):
        if not isinstance(x, Operator):
            return NotImplemented
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        return _OpChain.make((self, x))
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    def __mul__(self, x):
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        if isinstance(x, Operator):
            return _OpProd(self, x)
        if np.isscalar(x):
            return self.scale(x)
        return NotImplemented
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    def __rmul__(self, x):
        return self.__mul__(x)

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    def __add__(self, x):
        if not isinstance(x, Operator):
            return NotImplemented
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        return _OpSum(self, x)
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    def __sub__(self, x):
        if not isinstance(x, Operator):
            return NotImplemented
        return _OpSum(self, -x)

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    def __pow__(self, power):
        if not np.isscalar(power):
            return NotImplemented
        return _OpChain.make((_PowerOp(self.target, power), self))

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    def clip(self, min=None, max=None):
        if min is None and max is None:
            return self
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        return _OpChain.make((_Clipper(self.target, min, max), self))
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    def apply(self, x):
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        """Returns the result of applying the operator to the Field x.

        Parameters
        ----------
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        x : Field or MultiField
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            the operator's input
        """
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        raise NotImplementedError
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    def force(self, x):
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        """Extract correct subset of domain of x and apply operator."""
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        return self.apply(x.extract(self.domain))

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    def _check_input(self, x):
        from ..linearization import Linearization
        d = x.target if isinstance(x, Linearization) else x.domain
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        self._check_domain_equality(self._domain, d)
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    def __call__(self, x):
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        if isinstance(x, Operator):
            return _OpChain.make((self, x))
        return self.apply(x)
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    def ducktape(self, name):
        from .simple_linear_operators import ducktape
        return self(ducktape(self, None, name))

    def ducktape_left(self, name):
        from .simple_linear_operators import ducktape
        return ducktape(None, self, name)(self)

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    def __repr__(self):
        return self.__class__.__name__

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for f in ["sqrt", "exp", "log", "tanh", "sigmoid", 'sin', 'cos', 'tan',
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          'sinh', 'cosh', 'absolute', 'sinc', 'one_over']:
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    def func(f):
        def func2(self):
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            fa = _FunctionApplier(self.target, f)
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            return _OpChain.make((fa, self))
        return func2
    setattr(Operator, f, func(f))


class _FunctionApplier(Operator):
    def __init__(self, domain, funcname):
        from ..sugar import makeDomain
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        self._domain = self._target = makeDomain(domain)
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        self._funcname = funcname

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    def apply(self, x):
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        self._check_input(x)
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        return getattr(x, self._funcname)()


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class _Clipper(Operator):
    def __init__(self, domain, min=None, max=None):
        from ..sugar import makeDomain
        self._domain = self._target = makeDomain(domain)
        self._min = min
        self._max = max

    def apply(self, x):
        self._check_input(x)
        return x.clip(self._min, self._max)


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class _PowerOp(Operator):
    def __init__(self, domain, power):
        from ..sugar import makeDomain
        self._domain = self._target = makeDomain(domain)
        self._power = power

    def apply(self, x):
        self._check_input(x)
        return x**self._power


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class _CombinedOperator(Operator):
    def __init__(self, ops, _callingfrommake=False):
        if not _callingfrommake:
            raise NotImplementedError
        self._ops = tuple(ops)

    @classmethod
    def unpack(cls, ops, res):
        for op in ops:
            if isinstance(op, cls):
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                res = cls.unpack(op._ops, res)
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            else:
                res = res + [op]
        return res

    @classmethod
    def make(cls, ops):
        res = cls.unpack(ops, [])
        if len(res) == 1:
            return res[0]
        return cls(res, _callingfrommake=True)


class _OpChain(_CombinedOperator):
    def __init__(self, ops, _callingfrommake=False):
        super(_OpChain, self).__init__(ops, _callingfrommake)
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        self._domain = self._ops[-1].domain
        self._target = self._ops[0].target
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        for i in range(1, len(self._ops)):
            if self._ops[i-1].domain != self._ops[i].target:
                raise ValueError("domain mismatch")
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    def apply(self, x):
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        self._check_input(x)
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        for op in reversed(self._ops):
            x = op(x)
        return x

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    def __repr__(self):
        subs = "\n".join(sub.__repr__() for sub in self._ops)
        return "_OpChain:\n" + indent(subs)


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class _OpProd(Operator):
    def __init__(self, op1, op2):
        from ..sugar import domain_union
        self._domain = domain_union((op1.domain, op2.domain))
        self._target = op1.target
        if op1.target != op2.target:
            raise ValueError("target mismatch")
        self._op1 = op1
        self._op2 = op2
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    def apply(self, x):
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        from ..linearization import Linearization
        from ..sugar import makeOp
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        self._check_input(x)
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        lin = isinstance(x, Linearization)
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        v = x._val if lin else x
        v1 = v.extract(self._op1.domain)
        v2 = v.extract(self._op2.domain)
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        if not lin:
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            return self._op1(v1) * self._op2(v2)
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        wm = x.want_metric
        lin1 = self._op1(Linearization.make_var(v1, wm))
        lin2 = self._op2(Linearization.make_var(v2, wm))
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        op = (makeOp(lin1._val)(lin2._jac))._myadd(
            makeOp(lin2._val)(lin1._jac), False)
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        return lin1.new(lin1._val*lin2._val, op(x.jac))
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    def __repr__(self):
        subs = "\n".join(sub.__repr__() for sub in (self._op1, self._op2))
        return "_OpProd:\n"+indent(subs)


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class _OpSum(Operator):
    def __init__(self, op1, op2):
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        from ..sugar import domain_union
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        self._domain = domain_union((op1.domain, op2.domain))
        self._target = domain_union((op1.target, op2.target))
        self._op1 = op1
        self._op2 = op2
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    def apply(self, x):
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        from ..linearization import Linearization
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        self._check_input(x)
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        lin = isinstance(x, Linearization)
        v = x._val if lin else x
        v1 = v.extract(self._op1.domain)
        v2 = v.extract(self._op2.domain)
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        res = None
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        if not lin:
            return self._op1(v1).unite(self._op2(v2))
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        wm = x.want_metric
        lin1 = self._op1(Linearization.make_var(v1, wm))
        lin2 = self._op2(Linearization.make_var(v2, wm))
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        op = lin1._jac._myadd(lin2._jac, False)
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        res = lin1.new(lin1._val.unite(lin2._val), op(x.jac))
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        if lin1._metric is not None and lin2._metric is not None:
            res = res.add_metric(lin1._metric + lin2._metric)
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        return res
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    def __repr__(self):
        subs = "\n".join(sub.__repr__() for sub in (self._op1, self._op2))
        return "_OpSum:\n"+indent(subs)