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.input_output.logging_tools import logger
from pandas import DataFrame
logger = logger.getChild('structure_container')


[docs] class StructureContainer: """This class serves as a container for structure objects as well as their properties and cluster vectors. Parameters ---------- cluster_space Cluster space used for evaluating the cluster vectors. Example ------- The following snippet illustrates the initialization and usage of a :class:`StructureContainer` object. A structure container provides convenient means for compiling the data needed to train a cluster expansion, i.e., a sensing matrix and target property values:: >>> from ase.build import bulk >>> from icet import ClusterSpace, StructureContainer >>> from icet.tools import enumerate_structures >>> from random import random >>> # create cluster space >>> prim = bulk('Au') >>> cs = ClusterSpace(prim, cutoffs=[7.0, 5.0], ... chemical_symbols=[['Au', 'Pd']]) >>> # build structure container >>> sc = StructureContainer(cs) >>> for structure in enumerate_structures(prim, range(5), ['Au', 'Pd']): >>> sc.add_structure(structure, ... properties={'my_random_energy': random()}) >>> print(sc) >>> # fetch sensing matrix and target energies >>> A, y = 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]: """ Returns indices of structures with the given user tag. This method provides a simple means for filtering structures. The :attr:`user_tag` is assigned when adding structures via the :func:`add_structure` method. Parameters ---------- user_tag The indices of structures with this user tag are returned. Returns ------- List of structure 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 ------- 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 __str__(self) -> str: """ String representation. """ return self._get_string_representation(print_threshold=50) def _repr_html_(self) -> str: """ HTML representation. Used, e.g., in jupyter notebooks. """ s = ['<h4>Structure Container</h4>'] s += [f'<p>Total number of structures: {len(self)}</p>'] s += self.to_dataframe()._repr_html_() return ''.join(s)
[docs] def to_dataframe(self) -> DataFrame: """Summary of :class:`StructureContainer` object in :class:`DataFrame <pandas.DataFrame>` format. """ data = [] for s in self: record = dict( user_tag=s.user_tag, natoms=len(s), formula=s.structure.get_chemical_formula('metal'), ) record.update(s.properties) data.append(record) return DataFrame.from_dict(data)
[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 Atomic structure to be added. user_tag User tag for labeling 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(f'structure must be an ASE Atoms object not {type(structure)}') if user_tag is not None: if not isinstance(user_tag, str): raise TypeError(f'user_tag must be a string not {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) -> float: """Returns the condition number for the sensing matrix. A very large condition number can be a sign of multicollinearity. More information can be found [here](https://en.wikipedia.org/wiki/Condition_number). Parameters ---------- structure_indices List of structure indices to include. By default (``None``) the method will return all fit data available. Returns ------- Condition number of the sensing matrix. """ return np.linalg.cond(self.get_fit_data(structure_indices, key=None)[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 Name of property to use. If ``None`` do not include property values. This can be useful if only the fit matrix is needed. 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] if key is None: prop_list = None else: 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) if key is None: prop_list = None else: prop_list.append(self._structure_list[i].properties[key]) if cv_list is None: raise Exception(f'No available fit data for {structure_indices}.') cv_list = np.array(cv_list) if key is not None: prop_list = np.array(prop_list) return cv_list, 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]) -> None: """ Writes structure container to a file. Parameters ---------- outfile Output file name or file object. """ # Write cluster space to tempfile temp_cs_file = tempfile.NamedTemporaryFile(delete=False) self.cluster_space.write(temp_cs_file.name) # Write fit structures as an ASE db in tempfile temp_db_file = tempfile.NamedTemporaryFile(delete=False) temp_db_file.close() 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 :class:`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(delete=False) 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'], cluster_vector=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 Supercell structure. user_tag Custom user tag. cluster_vector Cluster vector. properties Dictionary comprising name and value of properties. """ def __init__(self, structure: Atoms, user_tag: str, cluster_vector: np.ndarray, properties: dict = {}): self._structure = structure self._user_tag = user_tag self._cluster_vector = cluster_vector self.properties = properties @property def cluster_vector(self) -> np.ndarray: """ 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) def __str__(self) -> str: width = 50 s = [] s += ['{s:=^{n}}'.format(s=' Fit Structure ', n=width)] s += [' {:22} : {}'.format('user tag', self.user_tag)] for k, v in self.properties.items(): s += [f' {k:22} : {v}'] t = 'cell metric' for k, row in enumerate(self.structure.cell[:]): s += [f' {t:22} : {row}'] t = '' t = 'sites' for site in self.structure: s += [f' {t:22} : {site.index} {site.symbol:2} {site.position}'] t = '' s += [''.center(width, '=')] return '\n'.join(s) def _repr_html_(self) -> str: s = ['<h4>FitStructure</h4>'] s += ['<table border="1" class="dataframe">'] s += ['<thead><tr><th style="text-align: left;">Property</th><th>Value</th></tr></thead>'] s += ['<tbody>'] s += [f'<tr><td style="text-align: left;">user tag</td><td>{self.user_tag}</td></tr>'] for key, value in sorted(self.properties.items()): s += [f'<tr><td style="text-align: left;">{key}</td><td>{value}</td></tr>'] s += ['</tbody></table>'] s += ['<table border="1" class="dataframe">'] s += ['<thead><tr><th style="text-align: left;">Cell</th></tr></thead>'] s += ['<tbody>'] for row in self.structure.cell[:]: s += ['<tr>'] for c in row: s += [f'<td>{c}</td>'] s += ['</tr>'] s += ['</tbody></table>'] df = DataFrame(np.array([self.structure.symbols, self.structure.positions[:, 0], self.structure.positions[:, 1], self.structure.positions[:, 2]]).T, columns=['Species', 'Position x', 'Position y', 'Position z']) s += df._repr_html_() return ''.join(s)