Source code for pymatgen.transformations.transformation_abc

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.

Defines an abstract base class contract for Transformation object.

import abc

from monty.json import MSONable

__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = ""
__date__ = "Sep 23, 2011"

[docs]class AbstractTransformation(MSONable, metaclass=abc.ABCMeta): """ Abstract transformation class. """
[docs] @abc.abstractmethod def apply_transformation(self, structure): """ Applies the transformation to a structure. Depending on whether a transformation is one-to-many, there may be an option to return a ranked list of structures. Args: structure: input structure return_ranked_list: Boolean stating whether or not multiple structures are returned. If return_ranked_list is a number, that number of structures is returned. Returns: depending on returned_ranked list, either a transformed structure or a list of dictionaries, where each dictionary is of the form {'structure' = .... , 'other_arguments'} the key 'transformation' is reserved for the transformation that was actually applied to the structure. This transformation is parsed by the alchemy classes for generating a more specific transformation history. Any other information will be stored in the transformation_parameters dictionary in the transmuted structure class. """ return
@property @abc.abstractmethod def inverse(self): """ Returns the inverse transformation if available. Otherwise, should return None. """ return @property @abc.abstractmethod def is_one_to_many(self): """ Determines if a Transformation is a one-to-many transformation. If a Transformation is a one-to-many transformation, the apply_transformation method should have a keyword arg "return_ranked_list" which allows for the transformed structures to be returned as a ranked list. """ return False @property def use_multiprocessing(self): """ Indicates whether the transformation can be applied by a subprocessing pool. This should be overridden to return True for transformations that the transmuter can parallelize. """ return False