Source code for icet.core.structure_container

"""
This module provides the StructureContainer class.
"""

import tarfile
import tempfile

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

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

from icet import ClusterSpace
from icet.io.logging 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 Example ------- The following snippet illustrates the initialization and usage of a StructureContainer object. The construction of a structure container is convenient for compiling the data needed to train a cluster expansion, i.e., a sensing matrix and target energies:: from ase.build import bulk from icet import ClusterSpace, StructureContainer from icet.tools import enumerate_structures import numpy as np # create cluster space prim = bulk('Au') cs = ClusterSpace(prim, cutoffs=[7.0, 5.0], chemical_symbols=[['Au', 'Pd']]) sc = StructureContainer(cs) for structure in enumerate_structures(prim, range(5), ['Au', 'Pd']): sc.add_structure(structure, properties={'my_random_energy': np.random.rand()}) print(sc) sensing_matrix, target_energies = sc.get_fit_data(key='my_random_energy') """ def __init__(self, cluster_space: ClusterSpace): if not isinstance(cluster_space, ClusterSpace): raise TypeError('cluster_space must be a ClusterSpace object') self._cluster_space = cluster_space self._structure_list = [] 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) -> 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 Returns ------- multi-line string string representation of the structure container """ if len(self) == 0: return 'Empty StructureContainer' # Number of structures to print before cutting and printing dots if print_threshold is None or print_threshold >= len(self): print_threshold = len(self) + 2 # format specifiers for fields in table def get_format(val): if isinstance(val, float): return '{:9.4f}' else: return '{}' # table headers default_headers = ['index', 'user_tag', 'n_atoms', 'chemical formula'] property_headers = sorted(set(key for fs in self for key in fs.properties)) headers = default_headers + property_headers # collect the table data str_table = [] for i, fs in enumerate(self): default_data = [i, fs.user_tag, len(fs), fs.structure.get_chemical_formula()] property_data = [fs.properties.get(key, '') for key in property_headers] str_row = [get_format(d).format(d) for d in default_data+property_data] str_table.append(str_row) str_table = np.array(str_table) # find maximum widths for each column widths = [] for i in range(str_table.shape[1]): data_width = max(len(val) for val in str_table[:, i]) header_width = len(headers[i]) widths.append(max([data_width, header_width])) total_width = sum(widths) + 3 * len(headers) row_format = ' | '.join('{:'+str(width)+'}' for width in widths) # Make string representation of table s = [] s += ['{s:=^{n}}'.format(s=' Structure Container ', n=total_width)] s += ['Total number of structures: {}'.format(len(self))] s += [''.center(total_width, '-')] s += [row_format.format(*headers)] s += [''.center(total_width, '-')] for i, fs_data in enumerate(str_table, start=1): s += [row_format.format(*fs_data)] if i+1 >= print_threshold: s += [' ...'] s += [row_format.format(*str_table[-1])] break s += [''.center(total_width, '=')] s = '\n'.join(s) return s def __repr__(self) -> str: """ String representation. """ return self._get_string_representation(print_threshold=50)
[docs] def print_overview(self, print_threshold: int = None): """ Prints a list of structures in the structure container. Parameters ---------- print_threshold if the number of orbits exceeds this number print dots """ print(self._get_string_representation(print_threshold=print_threshold))
[docs] def add_structure(self, structure: Atoms, user_tag: str = None, properties: dict = None, allow_duplicate: bool = True, sanity_check: bool = True): """ Adds a structure to the structure container. Parameters ---------- structure the atomic structure to be added user_tag custom user tag to label structure properties scalar properties. If properties are not specified the structure 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 sanity_check whether or not to carry out a sanity check before adding the structure. This includes checking occupations and volume. """ # structure must have a proper format and label if not isinstance(structure, Atoms): raise TypeError('structure must be an ASE Atoms object not {}'.format(type(structure))) 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))) if sanity_check: self._cluster_space.assert_structure_compatibility(structure) # check for properties in attached calculator if properties is None: properties = {} if structure.calc is not None: if not structure.calc.calculation_required(structure, ['energy']): energy = structure.get_potential_energy() properties['energy'] = energy / len(structure) # check if there exist structures with identical cluster vectors structure_copy = structure.copy() cv = self._cluster_space.get_cluster_vector(structure_copy) if not allow_duplicate: for i, fs in enumerate(self): if np.allclose(cv, fs.cluster_vector): msg = '{} and {} have identical cluster vectors'.format( user_tag if user_tag is not None else 'Input structure', fs.user_tag if fs.user_tag != 'None' else 'structure') msg += ' at index {}'.format(i) raise ValueError(msg) # add structure structure = FitStructure(structure_copy, user_tag, cv, properties) 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 https://en.wikipedia.org/wiki/Condition_number 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 of 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 = [s.properties[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 cv_list is None: raise Exception('No available fit data for {}' .format(structure_indices)) return np.array(cv_list), np.array(prop_list)
@property def cluster_space(self) -> ClusterSpace: """Cluster space used to calculate the cluster vectors.""" return self._cluster_space @property def available_properties(self) -> List[str]: """List of the available properties.""" return sorted(set([p for fs in self for p in fs.properties.keys()]))
[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(temp_cs_file.name) # Write fit structures as an ASE db in tempfile temp_db_file = tempfile.NamedTemporaryFile() if self._structure_list: db = ase.db.connect(temp_db_file.name, type='db', append=False) for fit_structure in self._structure_list: data_dict = {'user_tag': fit_structure.user_tag, 'properties': fit_structure.properties, 'cluster_vector': fit_structure.cluster_vector} db.write(fit_structure.structure, data=data_dict) with tarfile.open(outfile, mode='w') as handle: handle.add(temp_db_file.name, arcname='database') handle.add(temp_cs_file.name, 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 = infile.name if not tarfile.is_tarfile(filename): raise TypeError('{} is not a tar file'.format(filename)) temp_db_file = tempfile.NamedTemporaryFile() with tarfile.open(mode='r', name=filename) as tar_file: cs_file = tar_file.extractfile('cluster_space') temp_db_file.write(tar_file.extractfile('database').read()) temp_db_file.seek(0) cluster_space = ClusterSpace.read(cs_file) database = ase.db.connect(temp_db_file.name, type='db') structure_container = StructureContainer(cluster_space) fit_structures = [] for row in database.select(): data = row.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 ---------- structure : Atoms supercell structure user_tag : str custom user tag cvs : np.ndarray calculated cluster vector for actual structure properties : dict dictionary of properties """ def __init__(self, structure: Atoms, user_tag: str, cv: np.ndarray, properties: dict = {}): self._structure = structure self._user_tag = user_tag self._cluster_vector = cv self.properties = properties @property def cluster_vector(self) -> np.ndarray: """calculated cluster vector""" return self._cluster_vector @property def structure(self) -> Atoms: """atomic structure""" return self._structure @property def user_tag(self) -> str: """structure label""" return str(self._user_tag) def __getattr__(self, key): """ Accesses properties if possible and returns value. """ if key not in self.properties.keys(): return super().__getattribute__(key) return self.properties[key] def __len__(self) -> int: """ Number of sites in the structure. """ return len(self._structure)