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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) 2013-2017 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 ..utilities import memo, NiftyMeta
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from future.utils import with_metaclass
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class Energy(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
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    """ Provides the functional used by minimization schemes.
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   The Energy object is an implementation of a scalar function including its
   gradient and curvature at some position.
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    Parameters
    ----------
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    position : Field
        The input parameter of the scalar function.
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    Attributes
    ----------
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    position : Field
        The Field location in parameter space where value, gradient and
        curvature are evaluated.
    value : np.float
        The value of the energy functional at given `position`.
    gradient : Field
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        The gradient at given `position`.
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    curvature : LinearOperator, callable
        A positive semi-definite operator or function describing the curvature
        of the potential at the given `position`.
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    Notes
    -----
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    An instance of the Energy class is defined at a certain location. If one
    is interested in the value, gradient or curvature of the abstract energy
    functional one has to 'jump' to the new position using the `at` method.
    This method returns a new energy instance residing at the new position. By
    this approach, intermediate results from computing e.g. the gradient can
    safely be reused for e.g. the value or the curvature.
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    Memorizing the evaluations of some quantities (using the memo decorator)
    minimizes the computational effort for multiple calls.

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    See Also
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    --------
    memo
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    """
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    def __init__(self, position):
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        super(Energy, self).__init__()
        self._position = position.copy()
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    def at(self, position):
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        """ Initializes and returns a new Energy object at the new position.
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        Parameters
        ----------
        position : Field
            Parameter for the new Energy object.

        Returns
        -------
        out : Energy
            Energy object at new position.
        """
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        return self.__class__(position)

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    @property
    def position(self):
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        """
        The Field location in parameter space where value, gradient and
        curvature are evaluated.
        """
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        return self._position

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    @property
    def value(self):
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        """
        The value of the energy functional at given `position`.
        """
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        raise NotImplementedError

    @property
    def gradient(self):
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        """
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        The gradient at given `position`.
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        """
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        raise NotImplementedError

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    @property
    @memo
    def gradient_norm(self):
        """
        The length of the gradient at given `position`.
        """
        return self.gradient.norm()

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    @property
    def curvature(self):
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        """
        A positive semi-definite operator or function describing the curvature
        of the potential at the given `position`.
        """
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        raise NotImplementedError