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# coding: utf-8 

# Copyright (c) Pymatgen Development Team. 

# Distributed under the terms of the MIT License. 

 

from __future__ import unicode_literals 

 

""" 

This module provides various representations of transformed structures. A 

TransformedStructure is a structure that has been modified by undergoing a 

series of transformations. 

""" 

 

 

__author__ = "Shyue Ping Ong, Will Richards" 

__copyright__ = "Copyright 2012, The Materials Project" 

__version__ = "1.0" 

__maintainer__ = "Shyue Ping Ong" 

__email__ = "shyuep@gmail.com" 

__date__ = "Mar 2, 2012" 

 

import os 

import re 

import json 

import datetime 

from copy import deepcopy 

 

from monty.json import MontyDecoder 

 

from pymatgen.core.structure import Structure 

from pymatgen.io.cif import CifParser 

from pymatgen.io.vasp.inputs import Poscar 

from monty.json import MSONable 

from pymatgen.matproj.snl import StructureNL 

 

from warnings import warn 

 

dec = MontyDecoder() 

 

 

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) 

 

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 

 

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) 

 

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 

 

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) 

 

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) 

 

def get_vasp_input(self, vasp_input_set, generate_potcar=True): 

""" 

Returns VASP input as a dict of vasp objects. 

 

Args: 

vasp_input_set (pymatgen.io.vaspio_set.VaspInputSet): input set 

to create vasp input files from structures 

generate_potcar (bool): Set to False to generate a POTCAR.spec 

file instead of a POTCAR, which contains the POTCAR labels 

but not the actual POTCAR. Defaults to True. 

""" 

d = vasp_input_set.get_all_vasp_input(self.final_structure, 

generate_potcar) 

d["transformations.json"] = json.dumps(self.as_dict()) 

return d 

 

def write_vasp_input(self, vasp_input_set, output_dir, 

create_directory=True): 

""" 

Writes VASP input to an output_dir. 

 

Args: 

vasp_input_set: 

pymatgen.io.vaspio_set.VaspInputSet 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. 

""" 

vasp_input_set.write_input(self.final_structure, 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) 

 

def set_parameter(self, key, 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] 

 

@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("'", "\"", 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(datetime.datetime.now()), 

"original_file": raw_string, 

"cif_data": cif_dict[cif_keys[0]]} 

return TransformedStructure(s, transformations, history=[source_info]) 

 

@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("'", "\"", poscar_string) 

s = p.structure 

source_info = {"source": "POSCAR", 

"datetime": str(datetime.datetime.now()), 

"original_file": raw_string} 

return TransformedStructure(s, transformations, history=[source_info]) 

 

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"] = deepcopy(self.history) 

d["version"] = __version__ 

d["last_modified"] = str(datetime.datetime.utcnow()) 

d["other_parameters"] = deepcopy(self.other_parameters) 

return d 

 

@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)) 

 

def to_snl(self, authors, projects=None, references='', remarks=None, 

data=None, created_at=None): 

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', 

'http://pypi.python.org/pypi/pymatgen'), 

'description' : h}) 

return StructureNL(self.final_structure, authors, projects, references, 

remarks, data, hist, created_at) 

 

@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' : h.name} 

hist.append(d) 

return cls(snl.structure, history=hist)