Source code for pymatgen.alchemy.materials

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

This module provides various representations of transformed structures. A
TransformedStructure is a structure that has been modified by undergoing a
series of transformations.

import os
import re
import json
import datetime

from monty.json import MontyDecoder, jsanitize

from pymatgen.core.structure import Structure
from import CifParser
from import Poscar
from monty.json import MSONable

from import MPRelaxSet

from warnings import warn

__author__ = "Shyue Ping Ong, Will Richards"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = ""
__date__ = "Mar 2, 2012"

dec = MontyDecoder()

[docs]class TransformedStructure(MSONable): """ Container object for new structures that include history of transformations. Each transformed structure is made up of a sequence of structures with associated transformation history. """ def __init__(self, structure, transformations=None, history=None, other_parameters=None): """ Initializes a transformed structure from a structure. Args: structure (Structure): Input structure transformations ([Transformations]): List of transformations to apply. history (list): Previous history. other_parameters (dict): Additional parameters to be added. """ self.final_structure = structure self.history = history or [] self.other_parameters = other_parameters or {} self._undone = [] transformations = transformations or [] for t in transformations: self.append_transformation(t)
[docs] def undo_last_change(self): """ Undo the last change in the TransformedStructure. Raises: IndexError: If already at the oldest change. """ if len(self.history) == 0: raise IndexError("Can't undo. Already at oldest change.") if 'input_structure' not in self.history[-1]: raise IndexError("Can't undo. Latest history has no " "input_structure") h = self.history.pop() self._undone.append((h, self.final_structure)) s = h["input_structure"] if isinstance(s, dict): s = Structure.from_dict(s) self.final_structure = s
[docs] def redo_next_change(self): """ Redo the last undone change in the TransformedStructure. Raises: IndexError: If already at the latest change. """ if len(self._undone) == 0: raise IndexError("Can't redo. Already at latest change.") h, s = self._undone.pop() self.history.append(h) self.final_structure = s
def __getattr__(self, name): s = object.__getattribute__(self, 'final_structure') return getattr(s, name) def __len__(self): return len(self.history)
[docs] def append_transformation(self, transformation, return_alternatives=False, clear_redo=True): """ Appends a transformation to the TransformedStructure. Args: transformation: Transformation to append return_alternatives: Whether to return alternative TransformedStructures for one-to-many transformations. return_alternatives can be a number, which stipulates the total number of structures to return. clear_redo: Boolean indicating whether to clear the redo list. By default, this is True, meaning any appends clears the history of undoing. However, when using append_transformation to do a redo, the redo list should not be cleared to allow multiple redos. """ if clear_redo: self._undone = [] if return_alternatives and transformation.is_one_to_many: ranked_list = transformation.apply_transformation( self.final_structure, return_ranked_list=return_alternatives) input_structure = self.final_structure.as_dict() alts = [] for x in ranked_list[1:]: s = x.pop("structure") actual_transformation = x.pop("transformation", transformation) hdict = actual_transformation.as_dict() hdict["input_structure"] = input_structure hdict["output_parameters"] = x self.final_structure = s d = self.as_dict() d['history'].append(hdict) d['final_structure'] = s.as_dict() alts.append(TransformedStructure.from_dict(d)) x = ranked_list[0] s = x.pop("structure") actual_transformation = x.pop("transformation", transformation) hdict = actual_transformation.as_dict() hdict["input_structure"] = self.final_structure.as_dict() hdict["output_parameters"] = x self.history.append(hdict) self.final_structure = s return alts else: s = transformation.apply_transformation(self.final_structure) hdict = transformation.as_dict() hdict["input_structure"] = self.final_structure.as_dict() hdict["output_parameters"] = {} self.history.append(hdict) self.final_structure = s
[docs] def append_filter(self, structure_filter): """ Adds a filter. Args: structure_filter (StructureFilter): A filter implementating the AbstractStructureFilter API. Tells transmuter waht structures to retain. """ hdict = structure_filter.as_dict() hdict["input_structure"] = self.final_structure.as_dict() self.history.append(hdict)
[docs] def extend_transformations(self, transformations, return_alternatives=False): """ Extends a sequence of transformations to the TransformedStructure. Args: transformations: Sequence of Transformations return_alternatives: Whether to return alternative TransformedStructures for one-to-many transformations. return_alternatives can be a number, which stipulates the total number of structures to return. """ for t in transformations: self.append_transformation(t, return_alternatives=return_alternatives)
[docs] def get_vasp_input(self, vasp_input_set=MPRelaxSet, **kwargs): """ Returns VASP input as a dict of vasp objects. Args: vasp_input_set ( input set to create vasp input files from structures """ d = vasp_input_set(self.final_structure, **kwargs).get_vasp_input() d["transformations.json"] = json.dumps(self.as_dict()) return d
[docs] def write_vasp_input(self, vasp_input_set=MPRelaxSet, output_dir=".", create_directory=True, **kwargs): r""" Writes VASP input to an output_dir. Args: vasp_input_set: like object that creates vasp input files from structures output_dir: Directory to output files create_directory: Create the directory if not present. Defaults to True. **kwargs: All keyword args supported by the VASP input set. """ vasp_input_set(self.final_structure, **kwargs).write_input( output_dir, make_dir_if_not_present=create_directory) with open(os.path.join(output_dir, "transformations.json"), "w") as fp: json.dump(self.as_dict(), fp)
def __str__(self): output = ["Current structure", "------------", str(self.final_structure), "\nHistory", "------------"] for h in self.history: h.pop('input_structure', None) output.append(str(h)) output.append("\nOther parameters") output.append("------------") output.append(str(self.other_parameters)) return "\n".join(output)
[docs] def set_parameter(self, key, value): """ Set a parameter :param key: The string key :param value: The value. """ self.other_parameters[key] = value
@property def was_modified(self): """ Boolean describing whether the last transformation on the structure made any alterations to it one example of when this would return false is in the case of performing a substitution transformation on the structure when the specie to replace isn't in the structure. """ return not self.final_structure == self.structures[-2] @property def structures(self): """ Copy of all structures in the TransformedStructure. A structure is stored after every single transformation. """ hstructs = [Structure.from_dict(s['input_structure']) for s in self.history if 'input_structure' in s] return hstructs + [self.final_structure]
[docs] @staticmethod def from_cif_string(cif_string, transformations=None, primitive=True, occupancy_tolerance=1.): """ Generates TransformedStructure from a cif string. Args: cif_string (str): Input cif string. Should contain only one structure. For cifs containing multiple structures, please use CifTransmuter. transformations ([Transformations]): Sequence of transformations to be applied to the input structure. primitive (bool): Option to set if the primitive cell should be extracted. Defaults to True. However, there are certain instances where you might want to use a non-primitive cell, e.g., if you are trying to generate all possible orderings of partial removals or order a disordered structure. occupancy_tolerance (float): If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1. Returns: TransformedStructure """ parser = CifParser.from_string(cif_string, occupancy_tolerance) raw_string = re.sub(r"'", "\"", cif_string) cif_dict = parser.as_dict() cif_keys = list(cif_dict.keys()) s = parser.get_structures(primitive)[0] partial_cif = cif_dict[cif_keys[0]] if "_database_code_ICSD" in partial_cif: source = partial_cif["_database_code_ICSD"] + "-ICSD" else: source = "uploaded cif" source_info = {"source": source, "datetime": str(, "original_file": raw_string, "cif_data": cif_dict[cif_keys[0]]} return TransformedStructure(s, transformations, history=[source_info])
[docs] @staticmethod def from_poscar_string(poscar_string, transformations=None): """ Generates TransformedStructure from a poscar string. Args: poscar_string (str): Input POSCAR string. transformations ([Transformations]): Sequence of transformations to be applied to the input structure. """ p = Poscar.from_string(poscar_string) if not p.true_names: raise ValueError("Transformation can be craeted only from POSCAR " "strings with proper VASP5 element symbols.") raw_string = re.sub(r"'", "\"", poscar_string) s = p.structure source_info = {"source": "POSCAR", "datetime": str(, "original_file": raw_string} return TransformedStructure(s, transformations, history=[source_info])
[docs] def as_dict(self): """ Dict representation of the TransformedStructure. """ d = self.final_structure.as_dict() d["@module"] = self.__class__.__module__ d["@class"] = self.__class__.__name__ d["history"] = jsanitize(self.history) d["version"] = __version__ d["last_modified"] = str(datetime.datetime.utcnow()) d["other_parameters"] = jsanitize(self.other_parameters) return d
[docs] @classmethod def from_dict(cls, d): """ Creates a TransformedStructure from a dict. """ s = Structure.from_dict(d) return cls(s, history=d["history"], other_parameters=d.get("other_parameters", None))
[docs] def to_snl(self, authors, **kwargs): """ Generate SNL from TransformedStructure. :param authors: List of authors :param **kwargs: All kwargs supported by StructureNL. :return: StructureNL """ if self.other_parameters: warn('Data in TransformedStructure.other_parameters discarded ' 'during type conversion to SNL') hist = [] for h in self.history: snl_metadata = h.pop('_snl', {}) hist.append({'name': snl_metadata.pop('name', 'pymatgen'), 'url': snl_metadata.pop( 'url', ''), 'description': h}) from pymatgen.util.provenance import StructureNL return StructureNL(self.final_structure, authors, history=hist, **kwargs)
[docs] @classmethod def from_snl(cls, snl): """ Create TransformedStructure from SNL. Args: snl (StructureNL): Starting snl Returns: TransformedStructure """ hist = [] for h in snl.history: d = h.description d['_snl'] = {'url': h.url, 'name':} hist.append(d) return cls(snl.structure, history=hist)