Source code for icet.core.structure_container

import tarfile
import tempfile

from typing import BinaryIO, Dict, List, TextIO, Tuple, Union

import numpy as np
import ase.db
from ase import Atoms

from icet import ClusterSpace
from import logger
logger = logger.getChild('structure_container')

[docs]class StructureContainer: """ This class serves as a container for structure objects as well as their fit properties and cluster vectors. Parameters ---------- cluster_space : icet.ClusterSpace cluster space used for evaluating the cluster vectors list_of_atoms : list or tuple or list(tuple) list of atoms; if the list contains tuples, the second element of the tuple will be used as a tag of the structure list_of_properties : list(dict) list of properties, which are provided in dicts """ def __init__(self, cluster_space: ClusterSpace, list_of_atoms: Union[list, tuple] = None, list_of_properties: List[dict] = None, allow_duplicate: bool = True): self._cluster_space = cluster_space self._structure_list = [] # Add atomic structures if list_of_atoms is not None: if not isinstance(list_of_atoms, (list, tuple)): raise TypeError('atoms must be given as a list or a tuple') if list_of_properties is not None: if not len(list_of_properties) == len(list_of_atoms): raise ValueError('list of atoms and list of properties' ' must have the same length') else: list_of_properties = [None] * len(list_of_atoms) if not all(isinstance(x, tuple) for x in list_of_atoms): list_of_atoms = [(atoms, None) for atoms in list_of_atoms] for (atoms, user_tag), properties in zip(list_of_atoms, list_of_properties): try: self.add_structure(atoms=atoms, user_tag=user_tag, properties=properties, allow_duplicate=allow_duplicate) except (TypeError, ValueError): logger.exception('Skipping structure {}; '.format( list_of_atoms.index((atoms, user_tag)))) def __len__(self) -> int: return len(self._structure_list) def __getitem__(self, ind: int): return self._structure_list[ind]
[docs] def get_structure_indices(self, user_tag: str = None) -> List[int]: """ Get structure indices via user_tag Parameters ---------- user_tag user_tag used for selecting structures Returns ------- list of integers List of structure's indices """ return [i for i, s in enumerate(self) if user_tag is None or s.user_tag == user_tag]
def _get_string_representation(self, print_threshold: int = None, print_minimum: int = 10) -> str: """ String representation of the structure container that provides an overview of the structures in the container. Parameters ---------- print_threshold if the number of structures exceeds this number print dots print_minimum number of lines printed from the top and the bottom of the structure list if `print_threshold` is exceeded Returns ------- multi-line string string representation of the structure container """ def repr_structure(structure, index=-1, header=False): from collections import OrderedDict fields = OrderedDict([ ('index', '{:4}'.format(index)), ('user_tag', '{:21}'.format(structure.user_tag)), ('natoms', '{:5}'.format(len(structure))), ('chemical formula', structure._atoms.get_chemical_formula())]) fields.update(sorted( for key, value in fields.items(): if isinstance(value, float): fields[key] = '{:8.3f}'.format(value) if isinstance(value, int): fields[key] = '{:8}'.format(value) s = [] for name, value in fields.items(): n = max(len(name), len(value)) if header: s += ['{s:^{n}}'.format(s=name, n=n)] else: if name == 'user_tag' or name == 'chemical formula': # We want them aligned to the left value = '{:{padding}}'.format(value, padding=n - 1) s += ['{s:^{n}}'.format(s=value, n=n)] return ' | '.join(s) if len(self) == 0: return 'Empty StructureContainer' # basic information # (use last structure in list to obtain maximum line length) dummy = self._structure_list[-1] width = len(repr_structure(dummy)) # table header s = [] # type: List s += ['{s:=^{n}}'.format(s=' Structure Container ', n=width)] s += ['Total number of structures: {}'.format(len(self))] s += [''.center(width, '-')] s += [repr_structure(dummy, header=True).rstrip()] s += [''.center(width, '-')] # table body index = 0 while index < len(self): if (print_threshold is not None and len(self) > print_threshold and index >= print_minimum and index <= len(self) - print_minimum): index = len(self) - print_minimum s += [' ...'] s += [repr_structure(self._structure_list[index], index=index)] index += 1 s += [''.center(width, '=')] return '\n'.join(s) def __repr__(self) -> str: """ String representation. """ return self._get_string_representation(print_threshold=50)
[docs] def print_overview(self, print_threshold: int = None, print_minimum: int = 10): """ Prints a list of structures in the structure container. Parameters ---------- print_threshold if the number of orbits exceeds this number print dots print_minimum number of lines printed from the top and the bottom of the orbit list if `print_threshold` is exceeded """ print(self._get_string_representation(print_threshold=print_threshold, print_minimum=print_minimum))
[docs] def add_structure(self, atoms: Atoms, user_tag: str = None, properties: dict = None, allow_duplicate: bool = True): """ Adds a structure to the structure container. Parameters ---------- atoms the atomic structure to be added user_tag custom user tag to label structure properties scalar properties. If properties are not specified the atoms object will be checked for an attached ASE calculator object with a calculated potential energy allow_duplicate whether or not to add the structure if there already exists a structure with identical cluster-vector """ # atoms must have a proper format and label if not isinstance(atoms, Atoms): raise TypeError('atoms must be an ASE Atoms object.' ' Not {}'.format(type(atoms))) if user_tag is not None: if not isinstance(user_tag, str): raise TypeError('user_tag must be a string.' ' Not {}.'.format(type(user_tag))) atoms_copy = atoms.copy() # check for properties in attached calculator if properties is None and atoms.calc: properties = {} if not atoms.calc.calculation_required(atoms, ['energy']): energy = atoms.get_potential_energy() properties['energy'] = energy / len(atoms) # check if there exists structures with identical cluster vector cv = self._cluster_space.get_cluster_vector(atoms_copy) if not allow_duplicate: for i, fs in enumerate(self): if np.allclose(cv, fs.cluster_vector): msg = "{} have identical cluster vector with {}".format( user_tag if user_tag is not None else 'Input atoms', fs.user_tag if fs.user_tag != 'None' else 'structure') msg += " at index {}".format(i) raise ValueError(msg) structure = FitStructure(atoms_copy, user_tag) structure.set_properties(properties) structure._set_cluster_vector(cv) self._structure_list.append(structure)
[docs] def get_condition_number(self, structure_indices: List[int] = None, key: str = 'energy') -> float: """ Returns the condition number for the sensing matrix. A very large condition number can be a sign of multicollinearity, read more here Parameters ---------- structure_indices list of structure indices. By default (``None``) the method will return all fit data available. key key of properties dictionary Returns ------- condition number for the sensing matrix """ return np.linalg.cond(self.get_fit_data(structure_indices, key)[0])
[docs] def get_fit_data(self, structure_indices: List[int] = None, key: str = 'energy') -> Tuple[np.ndarray, np.ndarray]: """ Returns fit data for all structures. The cluster vectors and target properties for all structures are stacked into NumPy arrays. Parameters ---------- structure_indices list of structure indices. By default (``None``) the method will return all fit data available. key key of properties dictionary Returns ------- cluster vectors and target properties for desired structures """ if structure_indices is None: cv_list = [s.cluster_vector for s in self._structure_list] prop_list = [[key] for s in self._structure_list] else: cv_list, prop_list = [], [] for i in structure_indices: cv_list.append(self._structure_list[i].cluster_vector) prop_list.append(self._structure_list[i].properties[key]) if len(cv_list) == 0: raise Exception('No available fit data for' ' {}'.format(structure_indices)) return np.array(cv_list), np.array(prop_list)
[docs] def add_properties(self, structure_indices: List[int] = None, properties: List[dict] = None): """ This method allows you to add properties and/or modify the values of existing properties Parameters ---------- structure_indices list of structure indices. By default (``None``) the method will add the properties to all structures. properties list of scalar properties """ if structure_indices is None: msg = 'len of properties does not equal len of fit structures' assert len(properties) == len(self), msg for s, prop in zip(self._structure_list, properties): s.set_properties(prop) else: for i, prop in zip(structure_indices, properties): self._structure_list[i].set_properties(prop)
[docs] def get_properties(self, structure_indices: List[int] = None, key: str = 'energy') -> List[float]: """ Returns a list with the value of properties with key='key' for a desired set of structures Parameters ---------- structures_indices list of structure indices. Default to None and in that case returns properties of all structures key key of properties dictionary. Default to 'energy' """ if structure_indices is None: prop_list = [[key] for s in self._structure_list] else: prop_list = [] for i in structure_indices: prop_list.append(self._structure_list[i].properties[key]) return prop_list
[docs] def get_structures(self, structure_indices: List[int] = None) -> List[Atoms]: """ Returns a list of structures in the form of ASE Atoms Parameters ---------- structure_indices list of structure indices. By default (``None``) the method will return all structures listed in the container """ if structure_indices is None: s_list = [s.atoms for s in self._structure_list] else: s_list = [] for i in structure_indices: s_list.append(self._structure_list[i].atoms) return s_list
[docs] def get_user_tags(self, structure_indices: List[int] = None) -> List[str]: """ Return a list of user tags for the structures in the structure container Parameters ---------- structure_indices list of structure indices. By default (``None``) the method will return all user tags listed in the container """ if structure_indices is None: tag_list = [s.user_tag for s in self._structure_list] else: tag_list = [] for i in structure_indices: tag_list.append(self._structure_list[i].user_tag) return tag_list
@property def cluster_space(self) -> ClusterSpace: """Cluster space used to calculate the cluster vectors.""" return self._cluster_space @property def fit_structures(self): """List of the fit structures.""" return self._structure_list @property def available_properties(self) -> List[str]: """List of the available properties.""" return sorted(set([p for fs in self for p in]))
[docs] def write(self, outfile: Union[str, BinaryIO, TextIO]): """ Writes structure container to a file. Parameters ---------- outfile output file name or file object """ # Write cluster space to tempfile temp_cs_file = tempfile.NamedTemporaryFile() self.cluster_space.write( # Write fit structures as an ASE db in tempfile temp_db_file = tempfile.NamedTemporaryFile() if self.fit_structures: db = ase.db.connect(, type='db', append=False) for fit_structure in self.fit_structures: data_dict = {'user_tag': fit_structure.user_tag, 'properties':, 'cluster_vector': fit_structure.cluster_vector} db.write(fit_structure.atoms, data=data_dict) with, mode='w') as handle: handle.add(, arcname='database') handle.add(, arcname='cluster_space')
[docs] @staticmethod def read(infile: Union[str, BinaryIO, TextIO]): """ Reads StructureContainer object from file. Parameters ---------- infile file from which to read """ if isinstance(infile, str): filename = infile else: filename = if not tarfile.is_tarfile(filename): raise TypeError('{} is not a tar file'.format(filename)) temp_db_file = tempfile.NamedTemporaryFile() with'r', name=filename) as tar_file: cs_file = tar_file.extractfile('cluster_space') temp_db_file.write(tar_file.extractfile('database').read()) cluster_space = database = ase.db.connect(, type='db') structure_container = StructureContainer(cluster_space) fit_structures = [] for row in data = fit_structure = FitStructure(row.toatoms(), user_tag=data['user_tag'], cv=data['cluster_vector'], properties=data['properties']) fit_structures.append(fit_structure) structure_container._structure_list = fit_structures return structure_container
class FitStructure: """ This class holds a supercell along with its properties and cluster vector. Attributes ---------- atoms : ASE Atoms supercell structure user_tag : str custom user tag cvs : NumPy array calculated cluster vector for actual structure properties : dict the properties dictionary """ def __init__(self, atoms: Atoms, user_tag: str, cv: np.ndarray = None, properties: dict = None): self._atoms = atoms self._user_tag = user_tag self._properties = {} self._set_cluster_vector(cv) self.set_properties(properties) @property def cluster_vector(self) -> np.ndarray: """calculated cluster vector""" return self._cluster_vector @property def atoms(self) -> Atoms: """supercell structure""" return self._atoms @property def user_tag(self) -> str: """structure label""" return str(self._user_tag) @property def properties(self) -> Dict[str, Union[float, int]]: """scalar properties of structure""" return self._properties def __getattr__(self, key): """Accesses properties if possible and returns value""" if key not in return super().__getattribute__(key) return[key] def _set_cluster_vector(self, cv: np.ndarray): """ Sets the cluster vectors of the structure. """ if cv is not None: self._cluster_vector = cv else: self._cluster_vector = None def set_properties(self, properties: Dict[str, float]): """ Sets properties associated with the structure. """ if properties is not None: self._properties.update(properties) def __len__(self) -> int: """ Number of sites in the structure. """ return len(self._atoms)