# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module provides classes used to define a non-periodic molecule and a
periodic structure.
"""
import math
import os
import json
import collections
import itertools
from abc import ABCMeta, abstractmethod
import random
import warnings
from fnmatch import fnmatch
import re
import functools
from typing import Dict, List, Tuple, Optional, Union, Iterator, Set, Sequence, Iterable
import numpy as np
from tabulate import tabulate
from monty.dev import deprecated
from monty.io import zopen
from monty.json import MSONable
from pymatgen.core.operations import SymmOp
from pymatgen.core.lattice import Lattice, get_points_in_spheres
from pymatgen.core.periodic_table import Element, Specie, get_el_sp, DummySpecie
from pymatgen.core.sites import Site, PeriodicSite
from pymatgen.core.bonds import CovalentBond, get_bond_length
from pymatgen.core.composition import Composition
from pymatgen.util.coord import get_angle, all_distances, \
lattice_points_in_supercell
from pymatgen.core.units import Mass, Length
__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "2.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__status__ = "Production"
__date__ = "Sep 23, 2011"
[docs]class Neighbor(Site):
"""
Simple Site subclass to contain a neighboring atom that skips all the
unnecessary checks for speed. Can be
used as a fixed-length tuple of size 3 to retain backwards compatibility
with past use cases.
(site, nn_distance, index).
In future, usage should be to call attributes, e.g., Neighbor.index,
Neighbor.distance, etc.
"""
def __init__(self,
species: Composition,
coords: np.ndarray,
properties: dict = None,
nn_distance: float = 0.0,
index: int = 0):
"""
:param species: Same as Site
:param coords: Same as Site, but must be fractional.
:param properties: Same as Site
:param nn_distance: Distance to some other Site.
:param index: Index within structure.
"""
self.coords = coords
self._species = species
self.properties = properties or {}
self.nn_distance = nn_distance
self.index = index
def __len__(self):
"""
Make neighbor Tuple-like to retain backwards compatibility.
"""
return 3
def __getitem__(self, i: int): # type: ignore
"""
Make neighbor Tuple-like to retain backwards compatibility.
:param i:
:return:
"""
return (self, self.nn_distance, self.index)[i]
[docs]class PeriodicNeighbor(PeriodicSite):
"""
Simple PeriodicSite subclass to contain a neighboring atom that skips all
the unnecessary checks for speed. Can be used as a fixed-length tuple of
size 4 to retain backwards compatibility with past use cases.
(site, distance, index, image).
In future, usage should be to call attributes, e.g., PeriodicNeighbor.index,
PeriodicNeighbor.distance, etc.
"""
def __init__(self,
species: Composition,
coords: np.ndarray,
lattice: Lattice,
properties: dict = None,
nn_distance: float = 0.0,
index: int = 0,
image: tuple = (0, 0, 0)):
"""
:param species: Same as PeriodicSite
:param coords: Same as PeriodicSite, but must be fractional.
:param lattice: Same as PeriodicSite
:param properties: Same as PeriodicSite
:param nn_distance: Distance to some other Site.
:param index: Index within structure.
:param image: PeriodicImage
"""
self._lattice = lattice
self._frac_coords = coords
self._species = species
self.properties = properties or {}
self.nn_distance = nn_distance
self.index = index
self.image = image
@property # type: ignore
def coords(self):
"""
:return: Cartesian coords.
"""
return self._lattice.get_cartesian_coords(self._frac_coords)
def __len__(self):
"""
Make neighbor Tuple-like to retain backwards compatibility.
"""
return 4
def __getitem__(self, i: int): # type: ignore
"""
Make neighbor Tuple-like to retain backwards compatibility.
:param i:
:return:
"""
return (self, self.nn_distance, self.index, self.image)[i]
[docs]class SiteCollection(collections.abc.Sequence, metaclass=ABCMeta):
"""
Basic SiteCollection. Essentially a sequence of Sites or PeriodicSites.
This serves as a base class for Molecule (a collection of Site, i.e., no
periodicity) and Structure (a collection of PeriodicSites, i.e.,
periodicity). Not meant to be instantiated directly.
"""
# Tolerance in Angstrom for determining if sites are too close.
DISTANCE_TOLERANCE = 0.5
@property
@abstractmethod
def sites(self) -> Tuple[Union[Site, PeriodicSite]]:
"""
Returns a tuple of sites.
"""
[docs] @abstractmethod
def get_distance(self, i: int, j: int) -> float:
"""
Returns distance between sites at index i and j.
Args:
i: Index of first site
j: Index of second site
Returns:
Distance between sites at index i and index j.
"""
@property
def distance_matrix(self) -> np.ndarray:
"""
Returns the distance matrix between all sites in the structure. For
periodic structures, this is overwritten to return the nearest image
distance.
"""
return all_distances(self.cart_coords, self.cart_coords)
@property
def species(self) -> List[Composition]:
"""
Only works for ordered structures.
Disordered structures will raise an AttributeError.
Returns:
([Specie]) List of species at each site of the structure.
"""
return [site.specie for site in self]
@property
def species_and_occu(self) -> List[Composition]:
"""
List of species and occupancies at each site of the structure.
"""
return [site.species for site in self]
@property
def ntypesp(self) -> int:
"""Number of types of atoms."""
return len(self.types_of_specie)
@property
def types_of_specie(self) -> Tuple[Union[Element, Specie, DummySpecie]]:
"""
List of types of specie.
"""
# Cannot use set since we want a deterministic algorithm.
types = [] # type: List[Union[Element, Specie, DummySpecie]]
for site in self:
for sp, v in site.species.items():
if v != 0:
types.append(sp)
return tuple(set(types)) # type: ignore
[docs] def group_by_types(self) -> Iterator[Union[Site, PeriodicSite]]:
"""Iterate over species grouped by type"""
for t in self.types_of_specie:
for site in self:
if site.specie == t:
yield site
[docs] def indices_from_symbol(self, symbol: str) -> Tuple[int, ...]:
"""
Returns a tuple with the sequential indices of the sites
that contain an element with the given chemical symbol.
"""
return tuple((i for i, specie in enumerate(self.species)
if specie.symbol == symbol))
@property
def symbol_set(self) -> Tuple[str]:
"""
Tuple with the set of chemical symbols.
Note that len(symbol_set) == len(types_of_specie)
"""
return tuple(sorted(specie.symbol for specie in self.types_of_specie)) # type: ignore
@property # type: ignore
def atomic_numbers(self) -> Tuple[int]:
"""List of atomic numbers."""
return tuple(site.specie.Z for site in self) # type: ignore
@property
def site_properties(self) -> Dict[str, List]:
"""
Returns the site properties as a dict of sequences. E.g.,
{"magmom": (5,-5), "charge": (-4,4)}.
"""
props = {} # type: Dict[str, List]
prop_keys = set() # type: Set[str]
for site in self:
prop_keys.update(site.properties.keys())
for k in prop_keys:
props[k] = [site.properties.get(k, None) for site in self]
return props
def __contains__(self, site):
return site in self.sites
def __iter__(self):
return self.sites.__iter__()
def __getitem__(self, ind):
return self.sites[ind]
def __len__(self):
return len(self.sites)
def __hash__(self):
# for now, just use the composition hash code.
return self.composition.__hash__()
@property
def num_sites(self) -> int:
"""
Number of sites.
"""
return len(self)
@property
def cart_coords(self):
"""
Returns a np.array of the cartesian coordinates of sites in the
structure.
"""
return np.array([site.coords for site in self])
@property
def formula(self) -> str:
"""
(str) Returns the formula.
"""
return self.composition.formula
@property
def composition(self) -> Composition:
"""
(Composition) Returns the composition
"""
elmap = collections.defaultdict(float) # type: Dict[Specie, float]
for site in self:
for species, occu in site.species.items():
elmap[species] += occu
return Composition(elmap)
@property
def charge(self) -> float:
"""
Returns the net charge of the structure based on oxidation states. If
Elements are found, a charge of 0 is assumed.
"""
charge = 0
for site in self:
for specie, amt in site.species.items():
charge += getattr(specie, "oxi_state", 0) * amt
return charge
@property
def is_ordered(self) -> bool:
"""
Checks if structure is ordered, meaning no partial occupancies in any
of the sites.
"""
return all((site.is_ordered for site in self))
[docs] def get_angle(self, i: int, j: int, k: int) -> float:
"""
Returns angle specified by three sites.
Args:
i: Index of first site.
j: Index of second site.
k: Index of third site.
Returns:
Angle in degrees.
"""
v1 = self[i].coords - self[j].coords
v2 = self[k].coords - self[j].coords
return get_angle(v1, v2, units="degrees")
[docs] def get_dihedral(self, i: int, j: int, k: int, l: int) -> float:
"""
Returns dihedral angle specified by four sites.
Args:
i: Index of first site
j: Index of second site
k: Index of third site
l: Index of fourth site
Returns:
Dihedral angle in degrees.
"""
v1 = self[k].coords - self[l].coords
v2 = self[j].coords - self[k].coords
v3 = self[i].coords - self[j].coords
v23 = np.cross(v2, v3)
v12 = np.cross(v1, v2)
return math.degrees(math.atan2(np.linalg.norm(v2) * np.dot(v1, v23),
np.dot(v12, v23)))
[docs] def is_valid(self, tol: float = DISTANCE_TOLERANCE) -> bool:
"""
True if SiteCollection does not contain atoms that are too close
together. Note that the distance definition is based on type of
SiteCollection. Cartesian distances are used for non-periodic
Molecules, while PBC is taken into account for periodic structures.
Args:
tol (float): Distance tolerance. Default is 0.5A.
Returns:
(bool) True if SiteCollection does not contain atoms that are too
close together.
"""
if len(self.sites) == 1:
return True
all_dists = self.distance_matrix[np.triu_indices(len(self), 1)]
return bool(np.min(all_dists) > tol)
[docs] @abstractmethod
def to(self, fmt: str = None, filename: str = None):
"""
Generates well-known string representations of SiteCollections (e.g.,
molecules / structures). Should return a string type or write to a file.
"""
[docs] @classmethod
@abstractmethod
def from_str(cls, input_string: str, fmt: str):
"""
Reads in SiteCollection from a string.
"""
[docs] @classmethod
@abstractmethod
def from_file(cls, filename: str):
"""
Reads in SiteCollection from a filename.
"""
[docs] def add_site_property(self, property_name: str, values: List):
"""
Adds a property to a site.
Args:
property_name (str): The name of the property to add.
values (list): A sequence of values. Must be same length as
number of sites.
"""
if len(values) != len(self.sites):
raise ValueError("Values must be same length as sites.")
for site, val in zip(self.sites, values):
site.properties[property_name] = val
[docs] def remove_site_property(self, property_name):
"""
Adds a property to a site.
Args:
property_name (str): The name of the property to add.
"""
for site in self.sites:
del site.properties[property_name]
[docs] def replace_species(self, species_mapping: Dict[str, str]):
"""
Swap species.
Args:
species_mapping (dict): dict of species to swap. Species can be
elements too. E.g., {Element("Li"): Element("Na")} performs
a Li for Na substitution. The second species can be a
sp_and_occu dict. For example, a site with 0.5 Si that is
passed the mapping {Element('Si): {Element('Ge'):0.75,
Element('C'):0.25} } will have .375 Ge and .125 C.
"""
species_mapping = {get_el_sp(k): v
for k, v in species_mapping.items()}
sp_to_replace = set(species_mapping.keys())
sp_in_structure = set(self.composition.keys())
if not sp_in_structure.issuperset(sp_to_replace):
warnings.warn(
"Some species to be substituted are not present in "
"structure. Pls check your input. Species to be "
"substituted = %s; Species in structure = %s"
% (sp_to_replace, sp_in_structure))
for site in self.sites:
if sp_to_replace.intersection(site.species):
c = Composition()
for sp, amt in site.species.items():
new_sp = species_mapping.get(sp, sp)
try:
c += Composition(new_sp) * amt
except Exception:
c += {new_sp: amt}
site.species = c
[docs] def add_oxidation_state_by_element(self, oxidation_states: Dict[str, float]):
"""
Add oxidation states.
Args:
oxidation_states (dict): Dict of oxidation states.
E.g., {"Li":1, "Fe":2, "P":5, "O":-2}
"""
try:
for site in self.sites:
new_sp = {}
for el, occu in site.species.items():
sym = el.symbol
new_sp[Specie(sym, oxidation_states[sym])] = occu
site.species = Composition(new_sp)
except KeyError:
raise ValueError("Oxidation state of all elements must be "
"specified in the dictionary.")
[docs] def add_oxidation_state_by_site(self, oxidation_states: List[float]):
"""
Add oxidation states to a structure by site.
Args:
oxidation_states (list): List of oxidation states.
E.g., [1, 1, 1, 1, 2, 2, 2, 2, 5, 5, 5, 5, -2, -2, -2, -2]
"""
if len(oxidation_states) != len(self.sites):
raise ValueError("Oxidation states of all sites must be "
"specified.")
for site, ox in zip(self.sites, oxidation_states):
new_sp = {}
for el, occu in site.species.items():
sym = el.symbol
new_sp[Specie(sym, ox)] = occu
site.species = Composition(new_sp)
[docs] def remove_oxidation_states(self):
"""
Removes oxidation states from a structure.
"""
for site in self.sites:
new_sp = collections.defaultdict(float)
for el, occu in site.species.items():
sym = el.symbol
new_sp[Element(sym)] += occu
site.species = Composition(new_sp)
[docs] def add_oxidation_state_by_guess(self, **kwargs):
"""
Decorates the structure with oxidation state, guessing
using Composition.oxi_state_guesses()
Args:
**kwargs: parameters to pass into oxi_state_guesses()
"""
oxid_guess = self.composition.oxi_state_guesses(**kwargs)
oxid_guess = oxid_guess or [{e.symbol: 0 for e in self.composition}]
self.add_oxidation_state_by_element(oxid_guess[0])
[docs] def add_spin_by_element(self, spins: Dict[str, float]):
"""
Add spin states to a structure.
Args:
spins (dict): Dict of spins associated with elements or species,
e.g. {"Ni":+5} or {"Ni2+":5}
"""
for site in self.sites:
new_sp = {}
for sp, occu in site.species.items():
sym = sp.symbol
oxi_state = getattr(sp, "oxi_state", None)
new_sp[Specie(sym, oxidation_state=oxi_state,
properties={'spin': spins.get(str(sp), spins.get(sym, None))})] = occu
site.species = Composition(new_sp)
[docs] def add_spin_by_site(self, spins: List[float]):
"""
Add spin states to a structure by site.
Args:
spins (list): List of spins
E.g., [+5, -5, 0, 0]
"""
if len(spins) != len(self.sites):
raise ValueError("Spin of all sites must be "
"specified in the dictionary.")
for site, spin in zip(self.sites, spins):
new_sp = {}
for sp, occu in site.species.items():
sym = sp.symbol
oxi_state = getattr(sp, "oxi_state", None)
new_sp[Specie(sym, oxidation_state=oxi_state,
properties={'spin': spin})] = occu
site.species = Composition(new_sp)
[docs] def remove_spin(self):
"""
Removes spin states from a structure.
"""
for site in self.sites:
new_sp = collections.defaultdict(float)
for sp, occu in site.species.items():
oxi_state = getattr(sp, "oxi_state", None)
new_sp[Specie(sp.symbol, oxidation_state=oxi_state)] += occu
site.species = new_sp
[docs]class IStructure(SiteCollection, MSONable):
"""
Basic immutable Structure object with periodicity. Essentially a sequence
of PeriodicSites having a common lattice. IStructure is made to be
(somewhat) immutable so that they can function as keys in a dict. To make
modifications, use the standard Structure object instead. Structure
extends Sequence and Hashable, which means that in many cases,
it can be used like any Python sequence. Iterating through a
structure is equivalent to going through the sites in sequence.
"""
def __init__(self,
lattice: Union[List, np.ndarray, Lattice],
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
charge: float = None,
validate_proximity: bool = False,
to_unit_cell: bool = False,
coords_are_cartesian: bool = False,
site_properties: dict = None):
"""
Create a periodic structure.
Args:
lattice (Lattice/3x3 array): The lattice, either as a
:class:`pymatgen.core.lattice.Lattice` or
simply as any 2D array. Each row should correspond to a lattice
vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a
lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
species ([Specie]): Sequence of species on each site. Can take in
flexible input, including:
i. A sequence of element / specie specified either as string
symbols, e.g. ["Li", "Fe2+", "P", ...] or atomic numbers,
e.g., (3, 56, ...) or actual Element or Specie objects.
ii. List of dict of elements/species and occupancies, e.g.,
[{"Fe" : 0.5, "Mn":0.5}, ...]. This allows the setup of
disordered structures.
coords (Nx3 array): list of fractional/cartesian coordinates of
each species.
charge (int): overall charge of the structure. Defaults to behavior
in SiteCollection where total charge is the sum of the oxidation
states.
validate_proximity (bool): Whether to check if there are sites
that are less than 0.01 Ang apart. Defaults to False.
to_unit_cell (bool): Whether to map all sites into the unit cell,
i.e., fractional coords between 0 and 1. Defaults to False.
coords_are_cartesian (bool): Set to True if you are providing
coordinates in cartesian coordinates. Defaults to False.
site_properties (dict): Properties associated with the sites as a
dict of sequences, e.g., {"magmom":[5,5,5,5]}. The sequences
have to be the same length as the atomic species and
fractional_coords. Defaults to None for no properties.
"""
if len(species) != len(coords):
raise StructureError("The list of atomic species must be of the"
" same length as the list of fractional"
" coordinates.")
if isinstance(lattice, Lattice):
self._lattice = lattice
else:
self._lattice = Lattice(lattice)
sites = []
for i, sp in enumerate(species):
prop = None
if site_properties:
prop = {k: v[i]
for k, v in site_properties.items()}
sites.append(
PeriodicSite(sp, coords[i], self._lattice,
to_unit_cell,
coords_are_cartesian=coords_are_cartesian,
properties=prop))
self._sites = tuple(sites)
if validate_proximity and not self.is_valid():
raise StructureError(("Structure contains sites that are ",
"less than 0.01 Angstrom apart!"))
self._charge = charge
[docs] @classmethod
def from_sites(cls,
sites: List[PeriodicSite],
charge: float = None,
validate_proximity: bool = False,
to_unit_cell: bool = False):
"""
Convenience constructor to make a Structure from a list of sites.
Args:
sites: Sequence of PeriodicSites. Sites must have the same
lattice.
charge: Charge of structure.
validate_proximity (bool): Whether to check if there are sites
that are less than 0.01 Ang apart. Defaults to False.
to_unit_cell (bool): Whether to translate sites into the unit
cell.
Returns:
(Structure) Note that missing properties are set as None.
"""
if len(sites) < 1:
raise ValueError("You need at least one site to construct a %s" %
cls)
prop_keys = [] # type: List[str]
props = {}
lattice = sites[0].lattice
for i, site in enumerate(sites):
if site.lattice != lattice:
raise ValueError("Sites must belong to the same lattice")
for k, v in site.properties.items():
if k not in prop_keys:
prop_keys.append(k)
props[k] = [None] * len(sites)
props[k][i] = v
for k, v in props.items():
if any((vv is None for vv in v)):
warnings.warn("Not all sites have property %s. Missing values "
"are set to None." % k)
return cls(lattice, [site.species for site in sites],
[site.frac_coords for site in sites],
charge=charge,
site_properties=props,
validate_proximity=validate_proximity,
to_unit_cell=to_unit_cell)
[docs] @classmethod
def from_spacegroup(cls,
sg: str,
lattice: Union[List, np.ndarray, Lattice],
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
site_properties: Dict[str, Sequence] = None,
coords_are_cartesian: bool = False,
tol: float = 1e-5):
"""
Generate a structure using a spacegroup. Note that only symmetrically
distinct species and coords should be provided. All equivalent sites
are generated from the spacegroup operations.
Args:
sg (str/int): The spacegroup. If a string, it will be interpreted
as one of the notations supported by
pymatgen.symmetry.groups.Spacegroup. E.g., "R-3c" or "Fm-3m".
If an int, it will be interpreted as an international number.
lattice (Lattice/3x3 array): The lattice, either as a
:class:`pymatgen.core.lattice.Lattice` or
simply as any 2D array. Each row should correspond to a lattice
vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a
lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
Note that no attempt is made to check that the lattice is
compatible with the spacegroup specified. This may be
introduced in a future version.
species ([Specie]): Sequence of species on each site. Can take in
flexible input, including:
i. A sequence of element / specie specified either as string
symbols, e.g. ["Li", "Fe2+", "P", ...] or atomic numbers,
e.g., (3, 56, ...) or actual Element or Specie objects.
ii. List of dict of elements/species and occupancies, e.g.,
[{"Fe" : 0.5, "Mn":0.5}, ...]. This allows the setup of
disordered structures.
coords (Nx3 array): list of fractional/cartesian coordinates of
each species.
coords_are_cartesian (bool): Set to True if you are providing
coordinates in cartesian coordinates. Defaults to False.
site_properties (dict): Properties associated with the sites as a
dict of sequences, e.g., {"magmom":[5,5,5,5]}. The sequences
have to be the same length as the atomic species and
fractional_coords. Defaults to None for no properties.
tol (float): A fractional tolerance to deal with numerical
precision issues in determining if orbits are the same.
"""
from pymatgen.symmetry.groups import SpaceGroup
try:
i = int(sg)
sgp = SpaceGroup.from_int_number(i)
except ValueError:
sgp = SpaceGroup(sg)
if isinstance(lattice, Lattice):
latt = lattice
else:
latt = Lattice(lattice)
if not sgp.is_compatible(latt):
raise ValueError(
"Supplied lattice with parameters %s is incompatible with "
"supplied spacegroup %s!" % (latt.parameters, sgp.symbol)
)
if len(species) != len(coords):
raise ValueError(
"Supplied species and coords lengths (%d vs %d) are "
"different!" % (len(species), len(coords))
)
frac_coords = np.array(coords, dtype=np.float) if not coords_are_cartesian else \
latt.get_fractional_coords(coords)
props = {} if site_properties is None else site_properties
all_sp = [] # type: List[Union[str, Element, Specie, DummySpecie, Composition]]
all_coords = [] # type: List[List[float]]
all_site_properties = collections.defaultdict(list) # type: Dict[str, List]
for i, (sp, c) in enumerate(zip(species, frac_coords)):
cc = sgp.get_orbit(c, tol=tol)
all_sp.extend([sp] * len(cc))
all_coords.extend(cc)
for k, v in props.items():
all_site_properties[k].extend([v[i]] * len(cc))
return cls(latt, all_sp, all_coords,
site_properties=all_site_properties)
[docs] @classmethod
def from_magnetic_spacegroup(
cls,
msg: Union[str, 'MagneticSpaceGroup'], # type: ignore # noqa: F821
lattice: Union[List, np.ndarray, Lattice],
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
site_properties: Dict[str, Sequence],
coords_are_cartesian: bool = False,
tol: float = 1e-5):
"""
Generate a structure using a magnetic spacegroup. Note that only
symmetrically distinct species, coords and magmoms should be provided.]
All equivalent sites are generated from the spacegroup operations.
Args:
msg (str/list/:class:`pymatgen.symmetry.maggroups.MagneticSpaceGroup`):
The magnetic spacegroup.
If a string, it will be interpreted as one of the notations
supported by MagneticSymmetryGroup, e.g., "R-3'c" or "Fm'-3'm".
If a list of two ints, it will be interpreted as the number of
the spacegroup in its Belov, Neronova and Smirnova (BNS) setting.
lattice (Lattice/3x3 array): The lattice, either as a
:class:`pymatgen.core.lattice.Lattice` or
simply as any 2D array. Each row should correspond to a lattice
vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a
lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
Note that no attempt is made to check that the lattice is
compatible with the spacegroup specified. This may be
introduced in a future version.
species ([Specie]): Sequence of species on each site. Can take in
flexible input, including:
i. A sequence of element / specie specified either as string
symbols, e.g. ["Li", "Fe2+", "P", ...] or atomic numbers,
e.g., (3, 56, ...) or actual Element or Specie objects.
ii. List of dict of elements/species and occupancies, e.g.,
[{"Fe" : 0.5, "Mn":0.5}, ...]. This allows the setup of
disordered structures.
coords (Nx3 array): list of fractional/cartesian coordinates of
each species.
site_properties (dict): Properties associated with the sites as a
dict of sequences, e.g., {"magmom":[5,5,5,5]}. The sequences
have to be the same length as the atomic species and
fractional_coords. Unlike Structure.from_spacegroup(),
this argument is mandatory, since magnetic moment information
has to be included. Note that the *direction* of the supplied
magnetic moment relative to the crystal is important, even if
the resulting structure is used for collinear calculations.
coords_are_cartesian (bool): Set to True if you are providing
coordinates in cartesian coordinates. Defaults to False.
tol (float): A fractional tolerance to deal with numerical
precision issues in determining if orbits are the same.
"""
from pymatgen.electronic_structure.core import Magmom
from pymatgen.symmetry.maggroups import MagneticSpaceGroup
if 'magmom' not in site_properties:
raise ValueError('Magnetic moments have to be defined.')
magmoms = [Magmom(m) for m in site_properties['magmom']]
if not isinstance(msg, MagneticSpaceGroup):
msg = MagneticSpaceGroup(msg) # type: ignore
if isinstance(lattice, Lattice):
latt = lattice
else:
latt = Lattice(lattice)
if not msg.is_compatible(latt):
raise ValueError(
"Supplied lattice with parameters %s is incompatible with "
"supplied spacegroup %s!" % (latt.parameters, msg.sg_symbol)
)
if len(species) != len(coords):
raise ValueError(
"Supplied species and coords lengths (%d vs %d) are "
"different!" % (len(species), len(coords))
)
if len(species) != len(magmoms):
raise ValueError(
"Supplied species and magmom lengths (%d vs %d) are "
"different!" % (len(species), len(magmoms))
)
frac_coords = coords if not coords_are_cartesian else latt.get_fractional_coords(coords)
all_sp = [] # type: List[Union[str, Element, Specie, DummySpecie, Composition]]
all_coords = [] # type: List[List[float]]
all_magmoms = [] # type: List[float]
all_site_properties = collections.defaultdict(list) # type: Dict[str, List]
for i, (sp, c, m) in enumerate(zip(species, frac_coords, magmoms)):
cc, mm = msg.get_orbit(c, m, tol=tol)
all_sp.extend([sp] * len(cc))
all_coords.extend(cc)
all_magmoms.extend(mm)
for k, v in site_properties.items():
if k != 'magmom':
all_site_properties[k].extend([v[i]] * len(cc))
all_site_properties['magmom'] = all_magmoms
return cls(latt, all_sp, all_coords,
site_properties=all_site_properties)
@property
def charge(self):
"""
Overall charge of the structure
"""
if self._charge is None:
return super().charge
return self._charge
@property
def distance_matrix(self):
"""
Returns the distance matrix between all sites in the structure. For
periodic structures, this should return the nearest image distance.
"""
return self.lattice.get_all_distances(self.frac_coords,
self.frac_coords)
@property
def sites(self):
"""
Returns an iterator for the sites in the Structure.
"""
return self._sites
@property
def lattice(self):
"""
Lattice of the structure.
"""
return self._lattice
@property
def density(self):
"""
Returns the density in units of g/cc
"""
m = Mass(self.composition.weight, "amu")
return m.to("g") / (self.volume * Length(1, "ang").to("cm") ** 3)
[docs] def get_space_group_info(self, symprec=1e-2, angle_tolerance=5.0):
"""
Convenience method to quickly get the spacegroup of a structure.
Args:
symprec (float): Same definition as in SpacegroupAnalyzer.
Defaults to 1e-2.
angle_tolerance (float): Same definition as in SpacegroupAnalyzer.
Defaults to 5 degrees.
Returns:
spacegroup_symbol, international_number
"""
# Import within method needed to avoid cyclic dependency.
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
a = SpacegroupAnalyzer(self, symprec=symprec,
angle_tolerance=angle_tolerance)
return a.get_space_group_symbol(), a.get_space_group_number()
[docs] def matches(self, other, anonymous=False, **kwargs):
"""
Check whether this structure is similar to another structure.
Basically a convenience method to call structure matching.
Args:
other (IStructure/Structure): Another structure.
**kwargs: Same **kwargs as in
:class:`pymatgen.analysis.structure_matcher.StructureMatcher`.
Returns:
(bool) True is the structures are similar under some affine
transformation.
"""
from pymatgen.analysis.structure_matcher import StructureMatcher
m = StructureMatcher(**kwargs)
if not anonymous:
return m.fit(self, other)
return m.fit_anonymous(self, other)
def __eq__(self, other):
if other is self:
return True
if other is None:
return False
if len(self) != len(other):
return False
if self.lattice != other.lattice:
return False
for site in self:
if site not in other:
return False
return True
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
# For now, just use the composition hash code.
return self.composition.__hash__()
def __mul__(self, scaling_matrix):
"""
Makes a supercell. Allowing to have sites outside the unit cell
Args:
scaling_matrix: A scaling matrix for transforming the lattice
vectors. Has to be all integers. Several options are possible:
a. A full 3x3 scaling matrix defining the linear combination
the old lattice vectors. E.g., [[2,1,0],[0,3,0],[0,0,
1]] generates a new structure with lattice vectors a' =
2a + b, b' = 3b, c' = c where a, b, and c are the lattice
vectors of the original structure.
b. An sequence of three scaling factors. E.g., [2, 1, 1]
specifies that the supercell should have dimensions 2a x b x
c.
c. A number, which simply scales all lattice vectors by the
same factor.
Returns:
Supercell structure. Note that a Structure is always returned,
even if the input structure is a subclass of Structure. This is
to avoid different arguments signatures from causing problems. If
you prefer a subclass to return its own type, you need to override
this method in the subclass.
"""
scale_matrix = np.array(scaling_matrix, np.int16)
if scale_matrix.shape != (3, 3):
scale_matrix = np.array(scale_matrix * np.eye(3), np.int16)
new_lattice = Lattice(np.dot(scale_matrix, self._lattice.matrix))
f_lat = lattice_points_in_supercell(scale_matrix)
c_lat = new_lattice.get_cartesian_coords(f_lat)
new_sites = []
for site in self:
for v in c_lat:
s = PeriodicSite(
site.species, site.coords + v,
new_lattice, properties=site.properties,
coords_are_cartesian=True, to_unit_cell=False,
skip_checks=True)
new_sites.append(s)
new_charge = self._charge * np.linalg.det(scale_matrix) if self._charge else None
return Structure.from_sites(new_sites, charge=new_charge)
def __rmul__(self, scaling_matrix):
"""
Similar to __mul__ to preserve commutativeness.
"""
return self.__mul__(scaling_matrix)
@property
def frac_coords(self):
"""
Fractional coordinates as a Nx3 numpy array.
"""
return np.array([site.frac_coords for site in self._sites])
@property
def volume(self):
"""
Returns the volume of the structure.
"""
return self._lattice.volume
[docs] def get_distance(self, i, j, jimage=None):
"""
Get distance between site i and j assuming periodic boundary
conditions. If the index jimage of two sites atom j is not specified it
selects the jimage nearest to the i atom and returns the distance and
jimage indices in terms of lattice vector translations if the index
jimage of atom j is specified it returns the distance between the i
atom and the specified jimage atom.
Args:
i (int): Index of first site
j (int): Index of second site
jimage: Number of lattice translations in each lattice direction.
Default is None for nearest image.
Returns:
distance
"""
return self[i].distance(self[j], jimage)
[docs] def get_sites_in_sphere(self, pt: np.array, r: float,
include_index: bool = False,
include_image: bool = False) \
-> List[Tuple[PeriodicSite, float, Optional[int], Optional[Tuple[int]]]]:
"""
Find all sites within a sphere from the point, including a site (if any)
sitting on the point itself. This includes sites in other periodic
images.
Algorithm:
1. place sphere of radius r in crystal and determine minimum supercell
(parallelpiped) which would contain a sphere of radius r. for this
we need the projection of a_1 on a unit vector perpendicular
to a_2 & a_3 (i.e. the unit vector in the direction b_1) to
determine how many a_1"s it will take to contain the sphere.
Nxmax = r * length_of_b_1 / (2 Pi)
2. keep points falling within r.
Args:
pt (3x1 array): cartesian coordinates of center of sphere.
r (float): Radius of sphere.
include_index (bool): Whether the non-supercell site index
is included in the returned data
include_image (bool): Whether to include the supercell image
is included in the returned data
Returns:
[(site, dist) ...] since most of the time, subsequent processing
requires the distance.
"""
site_fcoords = np.mod(self.frac_coords, 1)
neighbors = [] # type: List[Tuple[PeriodicSite, float, Optional[int], Optional[Tuple[int]]]]
for fcoord, dist, i, img in self._lattice.get_points_in_sphere(
site_fcoords, pt, r):
nnsite = PeriodicSite(self[i].species,
fcoord, self._lattice,
properties=self[i].properties,
skip_checks=True)
# Get the neighbor data
nn_data = (nnsite, dist) if not include_index else (nnsite, dist, i) # type: ignore
if include_image:
nn_data += (img, ) # type: ignore
neighbors.append(nn_data) # type: ignore
return neighbors
[docs] def get_neighbors(self, site: PeriodicSite, r: float,
include_index: bool = False, include_image: bool = False) \
-> List[PeriodicNeighbor]:
"""
Get all neighbors to a site within a sphere of radius r. Excludes the
site itself.
Args:
site (Site): Which is the center of the sphere.
r (float): Radius of sphere.
include_index (bool): Deprecated. Now, the non-supercell site index
is always included in the returned data.
include_image (bool): Deprecated. Now the supercell image
is always included in the returned data.
Returns:
[PeriodicNeighbor] where PeriodicNeighbor is a namedtuple containing
(site, distance, index, image).
"""
return self.get_all_neighbors(r, include_index=include_index,
include_image=include_image,
sites=[site])[0]
@deprecated(get_neighbors, "This is retained purely for checking purposes.")
def get_neighbors_old(self, site, r, include_index=False, include_image=False):
"""
Get all neighbors to a site within a sphere of radius r. Excludes the
site itself.
Args:
site (Site): Which is the center of the sphere.
r (float): Radius of sphere.
include_index (bool): Whether the non-supercell site index
is included in the returned data
include_image (bool): Whether to include the supercell image
is included in the returned data
Returns:
[PeriodicNeighbor] where PeriodicNeighbor is a namedtuple containing
(site, distance, index, image).
"""
nn = self.get_sites_in_sphere(site.coords, r,
include_index=include_index,
include_image=include_image)
return [d for d in nn if site != d[0]]
def _get_neighbor_list_py(self, r: float,
sites: List[PeriodicSite] = None,
numerical_tol: float = 1e-8,
exclude_self: bool = True) -> Tuple[np.ndarray, ...]:
"""
A python version of getting neighbor_list. The returned values are a tuple of
numpy arrays (center_indices, points_indices, offset_vectors, distances).
Atom `center_indices[i]` has neighbor atom `points_indices[i]` that is
translated by `offset_vectors[i]` lattice vectors, and the distance is
`distances[i]`.
Args:
r (float): Radius of sphere
sites (list of Sites or None): sites for getting all neighbors,
default is None, which means neighbors will be obtained for all
sites. This is useful in the situation where you are interested
only in one subspecies type, and makes it a lot faster.
numerical_tol (float): This is a numerical tolerance for distances.
Sites which are < numerical_tol are determined to be conincident
with the site. Sites which are r + numerical_tol away is deemed
to be within r from the site. The default of 1e-8 should be
ok in most instances.
exclude_self (bool): whether to exclude atom neighboring with itself within
numerical tolerance distance, default to True
Returns: (center_indices, points_indices, offset_vectors, distances)
"""
neighbors = self.get_all_neighbors_py(r=r, include_index=True, include_image=True,
sites=sites, numerical_tol=1e-8)
center_indices = []
points_indices = []
offsets = []
distances = []
for i, nns in enumerate(neighbors):
if len(nns) > 0:
for n in nns:
if exclude_self and (i == n.index) and (n.nn_distance <= numerical_tol):
continue
center_indices.append(i)
points_indices.append(n.index)
offsets.append(n.image)
distances.append(n.nn_distance)
return tuple((np.array(center_indices), np.array(points_indices),
np.array(offsets), np.array(distances)))
[docs] def get_neighbor_list(self, r: float,
sites: List[PeriodicSite] = None,
numerical_tol: float = 1e-8,
exclude_self: bool = True) -> Tuple[np.ndarray, ...]:
"""
Get neighbor lists using numpy array representations without constructing
Neighbor objects. If the cython extension is installed, this method will
be orders of magnitude faster than `get_all_neighbors`.
The returned values are a tuple of numpy arrays
(center_indices, points_indices, offset_vectors, distances).
Atom `center_indices[i]` has neighbor atom `points_indices[i]` that is
translated by `offset_vectors[i]` lattice vectors, and the distance is
`distances[i]`.
Args:
r (float): Radius of sphere
sites (list of Sites or None): sites for getting all neighbors,
default is None, which means neighbors will be obtained for all
sites. This is useful in the situation where you are interested
only in one subspecies type, and makes it a lot faster.
numerical_tol (float): This is a numerical tolerance for distances.
Sites which are < numerical_tol are determined to be conincident
with the site. Sites which are r + numerical_tol away is deemed
to be within r from the site. The default of 1e-8 should be
ok in most instances.
exclude_self (bool): whether to exclude atom neighboring with itself within
numerical tolerance distance, default to True
Returns: (center_indices, points_indices, offset_vectors, distances)
"""
try:
from pymatgen.optimization.neighbors import find_points_in_spheres # type: ignore
except ImportError:
return self._get_neighbor_list_py(r, sites, exclude_self=exclude_self)
else:
if sites is None:
sites = self.sites
site_coords = np.array([site.coords for site in sites], dtype=float)
cart_coords = np.ascontiguousarray(np.array(self.cart_coords), dtype=float)
lattice_matrix = np.ascontiguousarray(np.array(self.lattice.matrix), dtype=float)
r = float(r)
center_indices, points_indices, images, distances = \
find_points_in_spheres(cart_coords, site_coords, r=r,
pbc=np.array([1, 1, 1], dtype=int),
lattice=lattice_matrix, tol=numerical_tol)
cond = np.array([True] * len(center_indices))
if exclude_self:
self_pair = (center_indices == points_indices) & (distances <= numerical_tol)
cond = ~self_pair
return tuple((center_indices[cond], points_indices[cond],
images[cond], distances[cond]))
[docs] def get_all_neighbors(self, r: float,
include_index: bool = False,
include_image: bool = False,
sites: List[PeriodicSite] = None,
numerical_tol: float = 1e-8) -> List[List[PeriodicNeighbor]]:
"""
Get neighbors for each atom in the unit cell, out to a distance r
Returns a list of list of neighbors for each site in structure.
Use this method if you are planning on looping over all sites in the
crystal. If you only want neighbors for a particular site, use the
method get_neighbors as it may not have to build such a large supercell
However if you are looping over all sites in the crystal, this method
is more efficient since it only performs one pass over a large enough
supercell to contain all possible atoms out to a distance r.
The return type is a [(site, dist) ...] since most of the time,
subsequent processing requires the distance.
A note about periodic images: Before computing the neighbors, this
operation translates all atoms to within the unit cell (having
fractional coordinates within [0,1)). This means that the "image" of a
site does not correspond to how much it has been translates from its
current position, but which image of the unit cell it resides.
Args:
r (float): Radius of sphere.
include_index (bool): Deprecated. Now, the non-supercell site index
is always included in the returned data.
include_image (bool): Deprecated. Now the supercell image
is always included in the returned data.
sites (list of Sites or None): sites for getting all neighbors,
default is None, which means neighbors will be obtained for all
sites. This is useful in the situation where you are interested
only in one subspecies type, and makes it a lot faster.
numerical_tol (float): This is a numerical tolerance for distances.
Sites which are < numerical_tol are determined to be conincident
with the site. Sites which are r + numerical_tol away is deemed
to be within r from the site. The default of 1e-8 should be
ok in most instances.
Returns:
[PeriodicNeighbor] where PeriodicNeighbor is a namedtuple containing
(site, distance, index, image).
"""
if sites is None:
sites = self.sites
center_indices, points_indices, images, distances = \
self.get_neighbor_list(r=r, sites=sites, numerical_tol=numerical_tol)
if len(points_indices) < 1:
return [[]] * len(sites)
f_coords = self.frac_coords[points_indices] + images
neighbor_dict: Dict[int, List] = collections.defaultdict(list)
lattice = self.lattice
atol = Site.position_atol
all_sites = self.sites
for cindex, pindex, image, f_coord, d in zip(center_indices, points_indices, images, f_coords, distances):
psite = all_sites[pindex]
csite = sites[cindex]
if (d > numerical_tol or
# This simply compares the psite and csite. The reason why manual comparison is done is
# for speed. This does not check the lattice since they are always equal. Also, the or construct
# returns True immediately once one of the conditions are satisfied.
psite.species != csite.species or
(not np.allclose(psite.coords, csite.coords, atol=atol)) or
(not psite.properties == csite.properties)):
neighbor_dict[cindex].append(PeriodicNeighbor(
species=psite.species,
coords=f_coord,
lattice=lattice,
properties=psite.properties,
nn_distance=d,
index=pindex,
image=tuple(image)))
neighbors: List[List[PeriodicNeighbor]] = []
for i in range(len(sites)):
neighbors.append(neighbor_dict[i])
return neighbors
[docs] def get_all_neighbors_py(self, r: float,
include_index: bool = False,
include_image: bool = False,
sites: List[PeriodicSite] = None,
numerical_tol: float = 1e-8) \
-> List[List[PeriodicNeighbor]]:
"""
Get neighbors for each atom in the unit cell, out to a distance r
Returns a list of list of neighbors for each site in structure.
Use this method if you are planning on looping over all sites in the
crystal. If you only want neighbors for a particular site, use the
method get_neighbors as it may not have to build such a large supercell
However if you are looping over all sites in the crystal, this method
is more efficient since it only performs one pass over a large enough
supercell to contain all possible atoms out to a distance r.
The return type is a [(site, dist) ...] since most of the time,
subsequent processing requires the distance.
A note about periodic images: Before computing the neighbors, this
operation translates all atoms to within the unit cell (having
fractional coordinates within [0,1)). This means that the "image" of a
site does not correspond to how much it has been translates from its
current position, but which image of the unit cell it resides.
Args:
r (float): Radius of sphere.
include_index (bool): Deprecated. Now, the non-supercell site index
is always included in the returned data.
include_image (bool): Deprecated. Now the supercell image
is always included in the returned data.
sites (list of Sites or None): sites for getting all neighbors,
default is None, which means neighbors will be obtained for all
sites. This is useful in the situation where you are interested
only in one subspecies type, and makes it a lot faster.
numerical_tol (float): This is a numerical tolerance for distances.
Sites which are < numerical_tol are determined to be conincident
with the site. Sites which are r + numerical_tol away is deemed
to be within r from the site. The default of 1e-8 should be
ok in most instances.
Returns:
[PeriodicNeighbor] where PeriodicNeighbor is a namedtuple containing
(site, distance, index, image).
"""
if sites is None:
sites = self.sites
site_coords = np.array([site.coords for site in sites])
point_neighbors = get_points_in_spheres(self.cart_coords, site_coords, r=r, pbc=True,
numerical_tol=numerical_tol, lattice=self.lattice)
neighbors: List[List[PeriodicNeighbor]] = []
for point_neighbor, site in zip(point_neighbors, sites):
nns: List[PeriodicNeighbor] = []
if len(point_neighbor) < 1:
neighbors.append([])
continue
for n in point_neighbor:
coord, d, index, image = n
if (d > numerical_tol) or (self[index] != site):
neighbor = PeriodicNeighbor(
species=self[index].species,
coords=coord,
lattice=self.lattice,
properties=self[index].properties,
nn_distance=d,
index=index,
image=tuple(image)
)
nns.append(neighbor)
neighbors.append(nns)
return neighbors
@deprecated(get_all_neighbors, "This is retained purely for checking purposes.")
def get_all_neighbors_old(self, r, include_index=False, include_image=False,
include_site=True):
"""
Get neighbors for each atom in the unit cell, out to a distance r
Returns a list of list of neighbors for each site in structure.
Use this method if you are planning on looping over all sites in the
crystal. If you only want neighbors for a particular site, use the
method get_neighbors as it may not have to build such a large supercell
However if you are looping over all sites in the crystal, this method
is more efficient since it only performs one pass over a large enough
supercell to contain all possible atoms out to a distance r.
The return type is a [(site, dist) ...] since most of the time,
subsequent processing requires the distance.
A note about periodic images: Before computing the neighbors, this
operation translates all atoms to within the unit cell (having
fractional coordinates within [0,1)). This means that the "image" of a
site does not correspond to how much it has been translates from its
current position, but which image of the unit cell it resides.
Args:
r (float): Radius of sphere.
include_index (bool): Whether to include the non-supercell site
in the returned data
include_image (bool): Whether to include the supercell image
in the returned data
include_site (bool): Whether to include the site in the returned
data. Defaults to True.
Returns:
[Neighbor] where Neighbor is a namedtuple containing
(site, distance, index, image).
"""
# Use same algorithm as get_sites_in_sphere to determine supercell but
# loop over all atoms in crystal
recp_len = np.array(self.lattice.reciprocal_lattice.abc)
maxr = np.ceil((r + 0.15) * recp_len / (2 * math.pi))
nmin = np.floor(np.min(self.frac_coords, axis=0)) - maxr
nmax = np.ceil(np.max(self.frac_coords, axis=0)) + maxr
all_ranges = [np.arange(x, y) for x, y in zip(nmin, nmax)]
latt = self._lattice
matrix = latt.matrix
neighbors = [list() for _ in range(len(self._sites))]
all_fcoords = np.mod(self.frac_coords, 1)
coords_in_cell = np.dot(all_fcoords, matrix)
site_coords = self.cart_coords
indices = np.arange(len(self))
for image in itertools.product(*all_ranges):
coords = np.dot(image, matrix) + coords_in_cell
all_dists = all_distances(coords, site_coords)
all_within_r = np.bitwise_and(all_dists <= r, all_dists > 1e-8)
for (j, d, within_r) in zip(indices, all_dists, all_within_r):
if include_site:
nnsite = PeriodicSite(self[j].species, coords[j],
latt, properties=self[j].properties,
coords_are_cartesian=True,
skip_checks=True)
for i in indices[within_r]:
item = []
if include_site:
item.append(nnsite)
item.append(d[i])
if include_index:
item.append(j)
# Add the image, if requested
if include_image:
item.append(image)
neighbors[i].append(item)
return neighbors
[docs] def get_neighbors_in_shell(self, origin, r, dr, include_index=False, include_image=False):
"""
Returns all sites in a shell centered on origin (coords) between radii
r-dr and r+dr.
Args:
origin (3x1 array): Cartesian coordinates of center of sphere.
r (float): Inner radius of shell.
dr (float): Width of shell.
include_index (bool): Deprecated. Now, the non-supercell site index
is always included in the returned data.
include_image (bool): Deprecated. Now the supercell image
is always included in the returned data.
Returns:
[NearestNeighbor] where Nearest Neighbor is a named tuple containing
(site, distance, index, image).
"""
outer = self.get_sites_in_sphere(origin, r + dr,
include_index=include_index,
include_image=include_image)
inner = r - dr
return [t for t in outer if t[1] > inner]
[docs] def get_sorted_structure(self, key=None, reverse=False):
"""
Get a sorted copy of the structure. The parameters have the same
meaning as in list.sort. By default, sites are sorted by the
electronegativity of the species.
Args:
key: Specifies a function of one argument that is used to extract
a comparison key from each list element: key=str.lower. The
default value is None (compare the elements directly).
reverse (bool): If set to True, then the list elements are sorted
as if each comparison were reversed.
"""
sites = sorted(self, key=key, reverse=reverse)
return self.__class__.from_sites(sites, charge=self._charge)
[docs] def get_reduced_structure(self, reduction_algo: str = "niggli"):
"""
Get a reduced structure.
Args:
reduction_algo (str): The lattice reduction algorithm to use.
Currently supported options are "niggli" or "LLL".
"""
if reduction_algo == "niggli":
reduced_latt = self._lattice.get_niggli_reduced_lattice()
elif reduction_algo == "LLL":
reduced_latt = self._lattice.get_lll_reduced_lattice()
else:
raise ValueError("Invalid reduction algo : {}"
.format(reduction_algo))
if reduced_latt != self.lattice:
return self.__class__( # type: ignore
reduced_latt,
self.species_and_occu,
self.cart_coords,
coords_are_cartesian=True,
to_unit_cell=True,
site_properties=self.site_properties,
charge=self._charge)
return self.copy()
[docs] def copy(self, site_properties=None, sanitize=False):
"""
Convenience method to get a copy of the structure, with options to add
site properties.
Args:
site_properties (dict): Properties to add or override. The
properties are specified in the same way as the constructor,
i.e., as a dict of the form {property: [values]}. The
properties should be in the order of the *original* structure
if you are performing sanitization.
sanitize (bool): If True, this method will return a sanitized
structure. Sanitization performs a few things: (i) The sites are
sorted by electronegativity, (ii) a LLL lattice reduction is
carried out to obtain a relatively orthogonalized cell,
(iii) all fractional coords for sites are mapped into the
unit cell.
Returns:
A copy of the Structure, with optionally new site_properties and
optionally sanitized.
"""
props = self.site_properties
if site_properties:
props.update(site_properties)
if not sanitize:
return self.__class__(self._lattice,
self.species_and_occu,
self.frac_coords,
charge=self._charge,
site_properties=props)
reduced_latt = self._lattice.get_lll_reduced_lattice()
new_sites = []
for i, site in enumerate(self):
frac_coords = reduced_latt.get_fractional_coords(site.coords)
site_props = {}
for p in props:
site_props[p] = props[p][i]
new_sites.append(PeriodicSite(site.species,
frac_coords, reduced_latt,
to_unit_cell=True,
properties=site_props,
skip_checks=True))
new_sites = sorted(new_sites)
return self.__class__.from_sites(new_sites, charge=self._charge)
[docs] def interpolate(self, end_structure,
nimages: Union[int, Iterable] = 10,
interpolate_lattices: bool = False,
pbc: bool = True,
autosort_tol: float = 0):
"""
Interpolate between this structure and end_structure. Useful for
construction of NEB inputs.
Args:
end_structure (Structure): structure to interpolate between this
structure and end.
nimages (int,list): No. of interpolation images or a list of
interpolation images. Defaults to 10 images.
interpolate_lattices (bool): Whether to interpolate the lattices.
Interpolates the lengths and angles (rather than the matrix)
so orientation may be affected.
pbc (bool): Whether to use periodic boundary conditions to find
the shortest path between endpoints.
autosort_tol (float): A distance tolerance in angstrom in
which to automatically sort end_structure to match to the
closest points in this particular structure. This is usually
what you want in a NEB calculation. 0 implies no sorting.
Otherwise, a 0.5 value usually works pretty well.
Returns:
List of interpolated structures. The starting and ending
structures included as the first and last structures respectively.
A total of (nimages + 1) structures are returned.
"""
# Check length of structures
if len(self) != len(end_structure):
raise ValueError("Structures have different lengths!")
if not (interpolate_lattices or self.lattice == end_structure.lattice):
raise ValueError("Structures with different lattices!")
if not isinstance(nimages, collections.abc.Iterable):
images = np.arange(nimages + 1) / nimages
else:
images = nimages
# Check that both structures have the same species
for i, site in enumerate(self):
if site.species != end_structure[i].species:
raise ValueError("Different species!\nStructure 1:\n" +
str(self) + "\nStructure 2\n" +
str(end_structure))
start_coords = np.array(self.frac_coords)
end_coords = np.array(end_structure.frac_coords)
if autosort_tol:
dist_matrix = self.lattice.get_all_distances(start_coords,
end_coords)
site_mappings = collections.defaultdict(list) # type: Dict[int, List[int]]
unmapped_start_ind = []
for i, row in enumerate(dist_matrix):
ind = np.where(row < autosort_tol)[0]
if len(ind) == 1:
site_mappings[i].append(ind[0])
else:
unmapped_start_ind.append(i)
if len(unmapped_start_ind) > 1:
raise ValueError("Unable to reliably match structures "
"with auto_sort_tol = %f. unmapped indices "
"= %s" % (autosort_tol, unmapped_start_ind))
sorted_end_coords = np.zeros_like(end_coords)
matched = []
for i, j in site_mappings.items():
if len(j) > 1:
raise ValueError("Unable to reliably match structures "
"with auto_sort_tol = %f. More than one "
"site match!" % autosort_tol)
sorted_end_coords[i] = end_coords[j[0]]
matched.append(j[0])
if len(unmapped_start_ind) == 1:
i = unmapped_start_ind[0]
j = list(set(range(len(start_coords))).difference(matched))[0] # type: ignore
sorted_end_coords[i] = end_coords[j]
end_coords = sorted_end_coords
vec = end_coords - start_coords
if pbc:
vec -= np.round(vec)
sp = self.species_and_occu
structs = []
if interpolate_lattices:
# interpolate lattice matrices using polar decomposition
from scipy.linalg import polar
# u is unitary (rotation), p is stretch
u, p = polar(np.dot(end_structure.lattice.matrix.T,
np.linalg.inv(self.lattice.matrix.T)))
lvec = p - np.identity(3)
lstart = self.lattice.matrix.T
for x in images:
if interpolate_lattices:
l_a = np.dot(np.identity(3) + x * lvec, lstart).T
lat = Lattice(l_a)
else:
lat = self.lattice
fcoords = start_coords + x * vec
structs.append(self.__class__(lat, sp, fcoords, site_properties=self.site_properties)) # type: ignore
return structs
[docs] def get_miller_index_from_site_indexes(self, site_ids, round_dp=4,
verbose=True):
"""
Get the Miller index of a plane from a set of sites indexes.
A minimum of 3 sites are required. If more than 3 sites are given
the best plane that minimises the distance to all points will be
calculated.
Args:
site_ids (list of int): A list of site indexes to consider. A
minimum of three site indexes are required. If more than three
sites are provided, the best plane that minimises the distance
to all sites will be calculated.
round_dp (int, optional): The number of decimal places to round the
miller index to.
verbose (bool, optional): Whether to print warnings.
Returns:
(tuple): The Miller index.
"""
return self.lattice.get_miller_index_from_coords(
self.frac_coords[site_ids], coords_are_cartesian=False,
round_dp=round_dp, verbose=verbose)
[docs] def get_primitive_structure(self, tolerance=0.25, use_site_props=False,
constrain_latt=None):
"""
This finds a smaller unit cell than the input. Sometimes it doesn"t
find the smallest possible one, so this method is recursively called
until it is unable to find a smaller cell.
NOTE: if the tolerance is greater than 1/2 the minimum inter-site
distance in the primitive cell, the algorithm will reject this lattice.
Args:
tolerance (float), Angstroms: Tolerance for each coordinate of a
particular site. For example, [0.1, 0, 0.1] in cartesian
coordinates will be considered to be on the same coordinates
as [0, 0, 0] for a tolerance of 0.25. Defaults to 0.25.
use_site_props (bool): Whether to account for site properties in
differntiating sites.
constrain_latt (list/dict): List of lattice parameters we want to
preserve, e.g. ["alpha", "c"] or dict with the lattice
parameter names as keys and values we want the parameters to
be e.g. {"alpha": 90, "c": 2.5}.
Returns:
The most primitive structure found.
"""
if constrain_latt is None:
constrain_latt = []
def site_label(site):
if not use_site_props:
return site.species_string
d = [site.species_string]
for k in sorted(site.properties.keys()):
d.append(k + "=" + str(site.properties[k]))
return ", ".join(d)
# group sites by species string
sites = sorted(self._sites, key=site_label)
grouped_sites = [
list(a[1])
for a in itertools.groupby(sites, key=site_label)]
grouped_fcoords = [np.array([s.frac_coords for s in g])
for g in grouped_sites]
# min_vecs are approximate periodicities of the cell. The exact
# periodicities from the supercell matrices are checked against these
# first
min_fcoords = min(grouped_fcoords, key=lambda x: len(x))
min_vecs = min_fcoords - min_fcoords[0]
# fractional tolerance in the supercell
super_ftol = np.divide(tolerance, self.lattice.abc)
super_ftol_2 = super_ftol * 2
def pbc_coord_intersection(fc1, fc2, tol):
"""
Returns the fractional coords in fc1 that have coordinates
within tolerance to some coordinate in fc2
"""
d = fc1[:, None, :] - fc2[None, :, :]
d -= np.round(d)
np.abs(d, d)
return fc1[np.any(np.all(d < tol, axis=-1), axis=-1)]
# here we reduce the number of min_vecs by enforcing that every
# vector in min_vecs approximately maps each site onto a similar site.
# The subsequent processing is O(fu^3 * min_vecs) = O(n^4) if we do no
# reduction.
# This reduction is O(n^3) so usually is an improvement. Using double
# the tolerance because both vectors are approximate
for g in sorted(grouped_fcoords, key=lambda x: len(x)):
for f in g:
min_vecs = pbc_coord_intersection(min_vecs, g - f, super_ftol_2)
def get_hnf(fu):
"""
Returns all possible distinct supercell matrices given a
number of formula units in the supercell. Batches the matrices
by the values in the diagonal (for less numpy overhead).
Computational complexity is O(n^3), and difficult to improve.
Might be able to do something smart with checking combinations of a
and b first, though unlikely to reduce to O(n^2).
"""
def factors(n):
for i in range(1, n + 1):
if n % i == 0:
yield i
for det in factors(fu):
if det == 1:
continue
for a in factors(det):
for e in factors(det // a):
g = det // a // e
yield det, np.array(
[[[a, b, c], [0, e, f], [0, 0, g]]
for b, c, f in
itertools.product(range(a), range(a),
range(e))])
# we cant let sites match to their neighbors in the supercell
grouped_non_nbrs = []
for gfcoords in grouped_fcoords:
fdist = gfcoords[None, :, :] - gfcoords[:, None, :]
fdist -= np.round(fdist)
np.abs(fdist, fdist)
non_nbrs = np.any(fdist > 2 * super_ftol[None, None, :], axis=-1)
# since we want sites to match to themselves
np.fill_diagonal(non_nbrs, True)
grouped_non_nbrs.append(non_nbrs)
num_fu = functools.reduce(math.gcd, map(len, grouped_sites))
for size, ms in get_hnf(num_fu):
inv_ms = np.linalg.inv(ms)
# find sets of lattice vectors that are are present in min_vecs
dist = inv_ms[:, :, None, :] - min_vecs[None, None, :, :]
dist -= np.round(dist)
np.abs(dist, dist)
is_close = np.all(dist < super_ftol, axis=-1)
any_close = np.any(is_close, axis=-1)
inds = np.all(any_close, axis=-1)
for inv_m, m in zip(inv_ms[inds], ms[inds]):
new_m = np.dot(inv_m, self.lattice.matrix)
ftol = np.divide(tolerance, np.sqrt(np.sum(new_m ** 2, axis=1)))
valid = True
new_coords = []
new_sp = []
new_props = collections.defaultdict(list)
for gsites, gfcoords, non_nbrs in zip(grouped_sites,
grouped_fcoords,
grouped_non_nbrs):
all_frac = np.dot(gfcoords, m)
# calculate grouping of equivalent sites, represented by
# adjacency matrix
fdist = all_frac[None, :, :] - all_frac[:, None, :]
fdist = np.abs(fdist - np.round(fdist))
close_in_prim = np.all(fdist < ftol[None, None, :], axis=-1)
groups = np.logical_and(close_in_prim, non_nbrs)
# check that groups are correct
if not np.all(np.sum(groups, axis=0) == size):
valid = False
break
# check that groups are all cliques
for g in groups:
if not np.all(groups[g][:, g]):
valid = False
break
if not valid:
break
# add the new sites, averaging positions
added = np.zeros(len(gsites))
new_fcoords = all_frac % 1
for i, group in enumerate(groups):
if not added[i]:
added[group] = True
inds = np.where(group)[0]
coords = new_fcoords[inds[0]]
for n, j in enumerate(inds[1:]):
offset = new_fcoords[j] - coords
coords += (offset - np.round(offset)) / (n + 2)
new_sp.append(gsites[inds[0]].species)
for k in gsites[inds[0]].properties:
new_props[k].append(gsites[inds[0]].properties[k])
new_coords.append(coords)
if valid:
inv_m = np.linalg.inv(m)
new_l = Lattice(np.dot(inv_m, self.lattice.matrix))
s = Structure(new_l, new_sp, new_coords,
site_properties=new_props,
coords_are_cartesian=False)
# Default behavior
p = s.get_primitive_structure(
tolerance=tolerance, use_site_props=use_site_props,
constrain_latt=constrain_latt
).get_reduced_structure()
if not constrain_latt:
return p
# Only return primitive structures that
# satisfy the restriction condition
p_latt, s_latt = p.lattice, self.lattice
if type(constrain_latt).__name__ == "list":
if all([getattr(p_latt, p) == getattr(s_latt, p) for p in constrain_latt]):
return p
elif type(constrain_latt).__name__ == "dict":
if all([getattr(p_latt, p) == constrain_latt[p] for p in constrain_latt.keys()]):
return p
return self.copy()
def __repr__(self):
outs = ["Structure Summary", repr(self.lattice)]
if self._charge:
if self._charge >= 0:
outs.append("Overall Charge: +{}".format(self._charge))
else:
outs.append("Overall Charge: -{}".format(self._charge))
for s in self:
outs.append(repr(s))
return "\n".join(outs)
def __str__(self):
outs = ["Full Formula ({s})".format(s=self.composition.formula),
"Reduced Formula: {}".format(self.composition.reduced_formula)]
def to_s(x):
return "%0.6f" % x
outs.append("abc : " + " ".join([to_s(i).rjust(10)
for i in self.lattice.abc]))
outs.append("angles: " + " ".join([to_s(i).rjust(10)
for i in self.lattice.angles]))
if self._charge:
if self._charge >= 0:
outs.append("Overall Charge: +{}".format(self._charge))
else:
outs.append("Overall Charge: -{}".format(self._charge))
outs.append("Sites ({i})".format(i=len(self)))
data = []
props = self.site_properties
keys = sorted(props.keys())
for i, site in enumerate(self):
row = [str(i), site.species_string]
row.extend([to_s(j) for j in site.frac_coords])
for k in keys:
row.append(props[k][i])
data.append(row)
outs.append(tabulate(data, headers=["#", "SP", "a", "b", "c"] + keys,
))
return "\n".join(outs)
[docs] def as_dict(self, verbosity=1, fmt=None, **kwargs):
"""
Dict representation of Structure.
Args:
verbosity (int): Verbosity level. Default of 1 includes both
direct and cartesian coordinates for all sites, lattice
parameters, etc. Useful for reading and for insertion into a
database. Set to 0 for an extremely lightweight version
that only includes sufficient information to reconstruct the
object.
fmt (str): Specifies a format for the dict. Defaults to None,
which is the default format used in pymatgen. Other options
include "abivars".
**kwargs: Allow passing of other kwargs needed for certain
formats, e.g., "abivars".
Returns:
JSON serializable dict representation.
"""
if fmt == "abivars":
"""Returns a dictionary with the ABINIT variables."""
from pymatgen.io.abinit.abiobjects import structure_to_abivars
return structure_to_abivars(self, **kwargs)
latt_dict = self._lattice.as_dict(verbosity=verbosity)
del latt_dict["@module"]
del latt_dict["@class"]
d = {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"charge": self._charge,
"lattice": latt_dict, "sites": []}
for site in self:
site_dict = site.as_dict(verbosity=verbosity)
del site_dict["lattice"]
del site_dict["@module"]
del site_dict["@class"]
d["sites"].append(site_dict)
return d
[docs] def as_dataframe(self):
"""
Returns a Pandas dataframe of the sites. Structure level attributes are stored in DataFrame.attrs. Example:
Species a b c x y z magmom
0 (Si) 0.0 0.0 0.000000e+00 0.0 0.000000e+00 0.000000e+00 5
1 (Si) 0.0 0.0 1.000000e-07 0.0 -2.217138e-07 3.135509e-07 -5
"""
data = []
site_properties = self.site_properties
prop_keys = list(site_properties.keys())
for site in self:
row = [site.species] + list(site.frac_coords) + list(site.coords)
for k in prop_keys:
row.append(site.properties.get(k))
data.append(row)
import pandas as pd
df = pd.DataFrame(data, columns=["Species", "a", "b", "c", "x", "y", "z"] + prop_keys)
df.attrs["Reduced Formula"] = self.composition.reduced_formula
df.attrs["Lattice"] = self.lattice
return df
[docs] @classmethod
def from_dict(cls, d, fmt=None):
"""
Reconstitute a Structure object from a dict representation of Structure
created using as_dict().
Args:
d (dict): Dict representation of structure.
Returns:
Structure object
"""
if fmt == "abivars":
from pymatgen.io.abinit.abiobjects import structure_from_abivars
return structure_from_abivars(cls=cls, **d)
lattice = Lattice.from_dict(d["lattice"])
sites = [PeriodicSite.from_dict(sd, lattice) for sd in d["sites"]]
charge = d.get("charge", None)
return cls.from_sites(sites, charge=charge)
[docs] def to(self, fmt=None, filename=None, **kwargs):
r"""
Outputs the structure to a file or string.
Args:
fmt (str): Format to output to. Defaults to JSON unless filename
is provided. If fmt is specifies, it overrides whatever the
filename is. Options include "cif", "poscar", "cssr", "json".
Non-case sensitive.
filename (str): If provided, output will be written to a file. If
fmt is not specified, the format is determined from the
filename. Defaults is None, i.e. string output.
**kwargs: Kwargs passthru to relevant methods. E.g., This allows
the passing of parameters like symprec to the
CifWriter.__init__ method for generation of symmetric cifs.
Returns:
(str) if filename is None. None otherwise.
"""
filename = filename or ""
fmt = "" if fmt is None else fmt.lower()
fname = os.path.basename(filename)
if fmt == "cif" or fnmatch(fname.lower(), "*.cif*"):
from pymatgen.io.cif import CifWriter
writer = CifWriter(self, **kwargs)
elif fmt == "mcif" or fnmatch(fname.lower(), "*.mcif*"):
from pymatgen.io.cif import CifWriter
writer = CifWriter(self, write_magmoms=True, **kwargs)
elif fmt == "poscar" or fnmatch(fname, "*POSCAR*"):
from pymatgen.io.vasp import Poscar
writer = Poscar(self, **kwargs)
elif fmt == "cssr" or fnmatch(fname.lower(), "*.cssr*"):
from pymatgen.io.cssr import Cssr
writer = Cssr(self, **kwargs)
elif fmt == "json" or fnmatch(fname.lower(), "*.json"):
s = json.dumps(self.as_dict())
if filename:
with zopen(filename, "wt") as f:
f.write("%s" % s)
return s
elif fmt == "xsf" or fnmatch(fname.lower(), "*.xsf*"):
from pymatgen.io.xcrysden import XSF
s = XSF(self).to_string()
if filename:
with zopen(fname, "wt", encoding='utf8') as f:
f.write(s)
return s
elif fmt == 'mcsqs' or fnmatch(fname, "*rndstr.in*") \
or fnmatch(fname, "*lat.in*") \
or fnmatch(fname, "*bestsqs*"):
from pymatgen.io.atat import Mcsqs
s = Mcsqs(self).to_string()
if filename:
with zopen(fname, "wt", encoding='ascii') as f:
f.write(s)
return s
elif fmt == 'prismatic' or fnmatch(fname, "*prismatic*"):
from pymatgen.io.prismatic import Prismatic
s = Prismatic(self).to_string()
return s
elif fmt == "yaml" or fnmatch(fname, "*.yaml*") or fnmatch(fname, "*.yml*"):
import ruamel.yaml as yaml
if filename:
with zopen(filename, "wt") as f:
yaml.safe_dump(self.as_dict(), f)
return None
return yaml.safe_dump(self.as_dict())
else:
raise ValueError("Invalid format: `%s`" % str(fmt))
if filename:
writer.write_file(filename)
return None
return writer.__str__()
[docs] @classmethod
def from_str(cls, input_string, fmt, primitive=False, sort=False,
merge_tol=0.0):
"""
Reads a structure from a string.
Args:
input_string (str): String to parse.
fmt (str): A format specification.
primitive (bool): Whether to find a primitive cell. Defaults to
False.
sort (bool): Whether to sort the sites in accordance to the default
ordering criteria, i.e., electronegativity.
merge_tol (float): If this is some positive number, sites that
are within merge_tol from each other will be merged. Usually
0.01 should be enough to deal with common numerical issues.
Returns:
IStructure / Structure
"""
from pymatgen.io.cif import CifParser
from pymatgen.io.vasp import Poscar
from pymatgen.io.cssr import Cssr
from pymatgen.io.xcrysden import XSF
from pymatgen.io.atat import Mcsqs
fmt = fmt.lower()
if fmt == "cif":
parser = CifParser.from_string(input_string)
s = parser.get_structures(primitive=primitive)[0]
elif fmt == "poscar":
s = Poscar.from_string(input_string, False,
read_velocities=False).structure
elif fmt == "cssr":
cssr = Cssr.from_string(input_string)
s = cssr.structure
elif fmt == "json":
d = json.loads(input_string)
s = Structure.from_dict(d)
elif fmt == "yaml":
import ruamel.yaml as yaml
d = yaml.safe_load(input_string)
s = Structure.from_dict(d)
elif fmt == "xsf":
s = XSF.from_string(input_string).structure
elif fmt == "mcsqs":
s = Mcsqs.structure_from_string(input_string)
else:
raise ValueError("Unrecognized format `%s`!" % fmt)
if sort:
s = s.get_sorted_structure()
if merge_tol:
s.merge_sites(merge_tol)
return cls.from_sites(s)
[docs] @classmethod
def from_file(cls, filename, primitive=False, sort=False, merge_tol=0.0):
"""
Reads a structure from a file. For example, anything ending in
a "cif" is assumed to be a Crystallographic Information Format file.
Supported formats include CIF, POSCAR/CONTCAR, CHGCAR, LOCPOT,
vasprun.xml, CSSR, Netcdf and pymatgen's JSON serialized structures.
Args:
filename (str): The filename to read from.
primitive (bool): Whether to convert to a primitive cell
Only available for cifs. Defaults to False.
sort (bool): Whether to sort sites. Default to False.
merge_tol (float): If this is some positive number, sites that
are within merge_tol from each other will be merged. Usually
0.01 should be enough to deal with common numerical issues.
Returns:
Structure.
"""
filename = str(filename)
if filename.endswith(".nc"):
# Read Structure from a netcdf file.
from pymatgen.io.abinit.netcdf import structure_from_ncdata
s = structure_from_ncdata(filename, cls=cls)
if sort:
s = s.get_sorted_structure()
return s
from pymatgen.io.lmto import LMTOCtrl
from pymatgen.io.vasp import Vasprun, Chgcar
from pymatgen.io.exciting import ExcitingInput
fname = os.path.basename(filename)
with zopen(filename, "rt") as f:
contents = f.read()
if fnmatch(fname.lower(), "*.cif*") or fnmatch(fname.lower(), "*.mcif*"):
return cls.from_str(contents, fmt="cif",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
if fnmatch(fname, "*POSCAR*") or fnmatch(fname, "*CONTCAR*") or fnmatch(fname, "*.vasp"):
s = cls.from_str(contents, fmt="poscar",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "CHGCAR*") or fnmatch(fname, "LOCPOT*"):
s = Chgcar.from_file(filename).structure
elif fnmatch(fname, "vasprun*.xml*"):
s = Vasprun(filename).final_structure
elif fnmatch(fname.lower(), "*.cssr*"):
return cls.from_str(contents, fmt="cssr",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "*.json*") or fnmatch(fname, "*.mson*"):
return cls.from_str(contents, fmt="json",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "*.yaml*"):
return cls.from_str(contents, fmt="yaml",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "*.xsf"):
return cls.from_str(contents, fmt="xsf",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "input*.xml"):
return ExcitingInput.from_file(fname).structure
elif fnmatch(fname, "*rndstr.in*") \
or fnmatch(fname, "*lat.in*") \
or fnmatch(fname, "*bestsqs*"):
return cls.from_str(contents, fmt="mcsqs",
primitive=primitive, sort=sort,
merge_tol=merge_tol)
elif fnmatch(fname, "CTRL*"):
return LMTOCtrl.from_file(filename=filename).structure
else:
raise ValueError("Unrecognized file extension!")
if sort:
s = s.get_sorted_structure()
if merge_tol:
s.merge_sites(merge_tol)
s.__class__ = cls
return s
[docs]class IMolecule(SiteCollection, MSONable):
"""
Basic immutable Molecule object without periodicity. Essentially a
sequence of sites. IMolecule is made to be immutable so that they can
function as keys in a dict. For a mutable molecule,
use the :class:Molecule.
Molecule extends Sequence and Hashable, which means that in many cases,
it can be used like any Python sequence. Iterating through a molecule is
equivalent to going through the sites in sequence.
"""
def __init__(self,
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
charge: float = 0.0,
spin_multiplicity: float = None,
validate_proximity: bool = False,
site_properties: dict = None):
"""
Creates a Molecule.
Args:
species: list of atomic species. Possible kinds of input include a
list of dict of elements/species and occupancies, a List of
elements/specie specified as actual Element/Specie, Strings
("Fe", "Fe2+") or atomic numbers (1,56).
coords (3x1 array): list of cartesian coordinates of each species.
charge (float): Charge for the molecule. Defaults to 0.
spin_multiplicity (int): Spin multiplicity for molecule.
Defaults to None, which means that the spin multiplicity is
set to 1 if the molecule has no unpaired electrons and to 2
if there are unpaired electrons.
validate_proximity (bool): Whether to check if there are sites
that are less than 1 Ang apart. Defaults to False.
site_properties (dict): Properties associated with the sites as
a dict of sequences, e.g., {"magmom":[5,5,5,5]}. The
sequences have to be the same length as the atomic species
and fractional_coords. Defaults to None for no properties.
"""
if len(species) != len(coords):
raise StructureError(("The list of atomic species must be of the",
" same length as the list of fractional ",
"coordinates."))
sites = []
for i in range(len(species)):
prop = None
if site_properties:
prop = {k: v[i] for k, v in site_properties.items()}
sites.append(Site(species[i], coords[i], properties=prop))
self._sites = tuple(sites)
if validate_proximity and not self.is_valid():
raise StructureError(("Molecule contains sites that are ",
"less than 0.01 Angstrom apart!"))
self._charge = charge
nelectrons = 0.0
for site in sites:
for sp, amt in site.species.items():
if not isinstance(sp, DummySpecie):
nelectrons += sp.Z * amt # type: ignore
nelectrons -= charge
self._nelectrons = nelectrons
if spin_multiplicity:
if (nelectrons + spin_multiplicity) % 2 != 1:
raise ValueError(
"Charge of %d and spin multiplicity of %d is"
" not possible for this molecule" %
(self._charge, spin_multiplicity))
self._spin_multiplicity = spin_multiplicity
else:
self._spin_multiplicity = 1 if nelectrons % 2 == 0 else 2
@property
def charge(self):
"""
Charge of molecule
"""
return self._charge
@property
def spin_multiplicity(self):
"""
Spin multiplicity of molecule.
"""
return self._spin_multiplicity
@property
def nelectrons(self):
"""
Number of electrons in the molecule.
"""
return self._nelectrons
@property
def center_of_mass(self):
"""
Center of mass of molecule.
"""
center = np.zeros(3)
total_weight = 0
for site in self:
wt = site.species.weight
center += site.coords * wt
total_weight += wt
return center / total_weight
@property
def sites(self):
"""
Returns a tuple of sites in the Molecule.
"""
return self._sites
[docs] @classmethod
def from_sites(cls, sites, charge=0, spin_multiplicity=None,
validate_proximity=False):
"""
Convenience constructor to make a Molecule from a list of sites.
Args:
sites ([Site]): Sequence of Sites.
charge (int): Charge of molecule. Defaults to 0.
spin_multiplicity (int): Spin multicipity. Defaults to None,
in which it is determined automatically.
validate_proximity (bool): Whether to check that atoms are too
close.
"""
props = collections.defaultdict(list)
for site in sites:
for k, v in site.properties.items():
props[k].append(v)
return cls([site.species for site in sites],
[site.coords for site in sites],
charge=charge, spin_multiplicity=spin_multiplicity,
validate_proximity=validate_proximity,
site_properties=props)
[docs] def break_bond(self, ind1, ind2, tol=0.2):
"""
Returns two molecules based on breaking the bond between atoms at index
ind1 and ind2.
Args:
ind1 (int): Index of first site.
ind2 (int): Index of second site.
tol (float): Relative tolerance to test. Basically, the code
checks if the distance between the sites is less than (1 +
tol) * typical bond distances. Defaults to 0.2, i.e.,
20% longer.
Returns:
Two Molecule objects representing the two clusters formed from
breaking the bond.
"""
sites = self._sites
clusters = [[sites[ind1]], [sites[ind2]]]
sites = [site for i, site in enumerate(sites) if i not in (ind1, ind2)]
def belongs_to_cluster(site, cluster):
for test_site in cluster:
if CovalentBond.is_bonded(site, test_site, tol=tol):
return True
return False
while len(sites) > 0:
unmatched = []
for site in sites:
for cluster in clusters:
if belongs_to_cluster(site, cluster):
cluster.append(site)
break
else:
unmatched.append(site)
if len(unmatched) == len(sites):
raise ValueError("Not all sites are matched!")
sites = unmatched
return (self.__class__.from_sites(cluster)
for cluster in clusters)
[docs] def get_covalent_bonds(self, tol=0.2):
"""
Determines the covalent bonds in a molecule.
Args:
tol (float): The tol to determine bonds in a structure. See
CovalentBond.is_bonded.
Returns:
List of bonds
"""
bonds = []
for site1, site2 in itertools.combinations(self._sites, 2):
if CovalentBond.is_bonded(site1, site2, tol):
bonds.append(CovalentBond(site1, site2))
return bonds
def __eq__(self, other):
if other is None:
return False
if len(self) != len(other):
return False
if self.charge != other.charge:
return False
if self.spin_multiplicity != other.spin_multiplicity:
return False
for site in self:
if site not in other:
return False
return True
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
# For now, just use the composition hash code.
return self.composition.__hash__()
def __repr__(self):
outs = ["Molecule Summary"]
for s in self:
outs.append(s.__repr__())
return "\n".join(outs)
def __str__(self):
outs = ["Full Formula (%s)" % self.composition.formula,
"Reduced Formula: " + self.composition.reduced_formula,
"Charge = %s, Spin Mult = %s" % (
self._charge, self._spin_multiplicity),
"Sites (%d)" % len(self)]
for i, site in enumerate(self):
outs.append(" ".join([str(i), site.species_string,
" ".join([("%0.6f" % j).rjust(12)
for j in site.coords])]))
return "\n".join(outs)
[docs] def as_dict(self):
"""
Json-serializable dict representation of Molecule
"""
d = {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"charge": self._charge,
"spin_multiplicity": self._spin_multiplicity,
"sites": []}
for site in self:
site_dict = site.as_dict()
del site_dict["@module"]
del site_dict["@class"]
d["sites"].append(site_dict)
return d
[docs] @classmethod
def from_dict(cls, d):
"""
Reconstitute a Molecule object from a dict representation created using
as_dict().
Args:
d (dict): dict representation of Molecule.
Returns:
Molecule object
"""
sites = [Site.from_dict(sd) for sd in d["sites"]]
charge = d.get("charge", 0)
spin_multiplicity = d.get("spin_multiplicity")
return cls.from_sites(sites, charge=charge, spin_multiplicity=spin_multiplicity)
[docs] def get_distance(self, i, j):
"""
Get distance between site i and j.
Args:
i (int): Index of first site
j (int): Index of second site
Returns:
Distance between the two sites.
"""
return self[i].distance(self[j])
[docs] def get_sites_in_sphere(self, pt, r):
"""
Find all sites within a sphere from a point.
Args:
pt (3x1 array): Cartesian coordinates of center of sphere
r (float): Radius of sphere.
Returns:
[Neighbor] since most of the time, subsequent processing
requires the distance.
"""
neighbors = []
for i, site in enumerate(self._sites):
dist = site.distance_from_point(pt)
if dist <= r:
neighbors.append(Neighbor(site.species, site.coords,
site.properties, dist, i))
return neighbors
[docs] def get_neighbors(self, site, r):
"""
Get all neighbors to a site within a sphere of radius r. Excludes the
site itself.
Args:
site (Site): Site at the center of the sphere.
r (float): Radius of sphere.
Returns:
[(site, dist) ...] since most of the time, subsequent processing
requires the distance.
"""
nns = self.get_sites_in_sphere(site.coords, r)
return [nn for nn in nns if nn != site]
[docs] def get_neighbors_in_shell(self, origin, r, dr):
"""
Returns all sites in a shell centered on origin (coords) between radii
r-dr and r+dr.
Args:
origin (3x1 array): Cartesian coordinates of center of sphere.
r (float): Inner radius of shell.
dr (float): Width of shell.
Returns:
[(site, dist) ...] since most of the time, subsequent processing
requires the distance.
"""
outer = self.get_sites_in_sphere(origin, r + dr)
inner = r - dr
return [nn for nn in outer if nn.nn_distance > inner]
[docs] def get_boxed_structure(self, a, b, c, images=(1, 1, 1),
random_rotation=False, min_dist=1, cls=None,
offset=None, no_cross=False, reorder=True):
"""
Creates a Structure from a Molecule by putting the Molecule in the
center of a orthorhombic box. Useful for creating Structure for
calculating molecules using periodic codes.
Args:
a (float): a-lattice parameter.
b (float): b-lattice parameter.
c (float): c-lattice parameter.
images: No. of boxed images in each direction. Defaults to
(1, 1, 1), meaning single molecule with 1 lattice parameter
in each direction.
random_rotation (bool): Whether to apply a random rotation to
each molecule. This jumbles all the molecules so that they
are not exact images of each other.
min_dist (float): The minimum distance that atoms should be from
each other. This is only used if random_rotation is True.
The randomized rotations are searched such that no two atoms
are less than min_dist from each other.
cls: The Structure class to instantiate (defaults to pymatgen
structure)
offset: Translation to offset molecule from center of mass coords
no_cross: Whether to forbid molecule coords from extending beyond
boundary of box.
reorder: Whether to reorder the sites to be in electronegativity
order.
Returns:
Structure containing molecule in a box.
"""
if offset is None:
offset = np.array([0, 0, 0])
coords = np.array(self.cart_coords)
x_range = max(coords[:, 0]) - min(coords[:, 0])
y_range = max(coords[:, 1]) - min(coords[:, 1])
z_range = max(coords[:, 2]) - min(coords[:, 2])
if a <= x_range or b <= y_range or c <= z_range:
raise ValueError("Box is not big enough to contain Molecule.")
lattice = Lattice.from_parameters(a * images[0], b * images[1],
c * images[2],
90, 90, 90)
nimages = images[0] * images[1] * images[2]
coords = []
centered_coords = self.cart_coords - self.center_of_mass + offset
for i, j, k in itertools.product(list(range(images[0])),
list(range(images[1])),
list(range(images[2]))):
box_center = [(i + 0.5) * a, (j + 0.5) * b, (k + 0.5) * c]
if random_rotation:
while True:
op = SymmOp.from_origin_axis_angle(
(0, 0, 0), axis=np.random.rand(3),
angle=random.uniform(-180, 180))
m = op.rotation_matrix
new_coords = np.dot(m, centered_coords.T).T + box_center
if no_cross:
x_max, x_min = max(new_coords[:, 0]), min(new_coords[:, 0])
y_max, y_min = max(new_coords[:, 1]), min(new_coords[:, 1])
z_max, z_min = max(new_coords[:, 2]), min(new_coords[:, 2])
if x_max > a or x_min < 0 or y_max > b or y_min < 0 or z_max > c or z_min < 0:
raise ValueError("Molecule crosses boundary of box.")
if len(coords) == 0:
break
distances = lattice.get_all_distances(
lattice.get_fractional_coords(new_coords),
lattice.get_fractional_coords(coords))
if np.amin(distances) > min_dist:
break
else:
new_coords = centered_coords + box_center
if no_cross:
x_max, x_min = max(new_coords[:, 0]), min(new_coords[:, 0])
y_max, y_min = max(new_coords[:, 1]), min(new_coords[:, 1])
z_max, z_min = max(new_coords[:, 2]), min(new_coords[:, 2])
if x_max > a or x_min < 0 or y_max > b or y_min < 0 or z_max > c or z_min < 0:
raise ValueError("Molecule crosses boundary of box.")
coords.extend(new_coords)
sprops = {k: v * nimages for k, v in self.site_properties.items()}
if cls is None:
cls = Structure
if reorder:
return cls(lattice, self.species * nimages, coords,
coords_are_cartesian=True,
site_properties=sprops).get_sorted_structure()
else:
return cls(lattice, self.species * nimages, coords,
coords_are_cartesian=True,
site_properties=sprops)
[docs] def get_centered_molecule(self):
"""
Returns a Molecule centered at the center of mass.
Returns:
Molecule centered with center of mass at origin.
"""
center = self.center_of_mass
new_coords = np.array(self.cart_coords) - center
return self.__class__(self.species_and_occu, new_coords,
charge=self._charge,
spin_multiplicity=self._spin_multiplicity,
site_properties=self.site_properties)
[docs] def to(self, fmt=None, filename=None):
"""
Outputs the molecule to a file or string.
Args:
fmt (str): Format to output to. Defaults to JSON unless filename
is provided. If fmt is specifies, it overrides whatever the
filename is. Options include "xyz", "gjf", "g03", "json". If
you have OpenBabel installed, any of the formats supported by
OpenBabel. Non-case sensitive.
filename (str): If provided, output will be written to a file. If
fmt is not specified, the format is determined from the
filename. Defaults is None, i.e. string output.
Returns:
(str) if filename is None. None otherwise.
"""
from pymatgen.io.xyz import XYZ
from pymatgen.io.gaussian import GaussianInput
from pymatgen.io.babel import BabelMolAdaptor
fmt = "" if fmt is None else fmt.lower()
fname = os.path.basename(filename or "")
if fmt == "xyz" or fnmatch(fname.lower(), "*.xyz*"):
writer = XYZ(self)
elif any([fmt == r or fnmatch(fname.lower(), "*.{}*".format(r))
for r in ["gjf", "g03", "g09", "com", "inp"]]):
writer = GaussianInput(self)
elif fmt == "json" or fnmatch(fname, "*.json*") or fnmatch(fname,
"*.mson*"):
if filename:
with zopen(filename, "wt", encoding='utf8') as f:
return json.dump(self.as_dict(), f)
else:
return json.dumps(self.as_dict())
elif fmt == "yaml" or fnmatch(fname, "*.yaml*"):
import ruamel.yaml as yaml
if filename:
with zopen(fname, "wt", encoding='utf8') as f:
return yaml.safe_dump(self.as_dict(), f)
else:
return yaml.safe_dump(self.as_dict())
else:
m = re.search(r"\.(pdb|mol|mdl|sdf|sd|ml2|sy2|mol2|cml|mrv)",
fname.lower())
if (not fmt) and m:
fmt = m.group(1)
writer = BabelMolAdaptor(self)
return writer.write_file(filename, file_format=fmt)
if filename:
writer.write_file(filename)
return str(writer)
[docs] @classmethod
def from_str(cls, input_string, fmt):
"""
Reads the molecule from a string.
Args:
input_string (str): String to parse.
fmt (str): Format to output to. Defaults to JSON unless filename
is provided. If fmt is specifies, it overrides whatever the
filename is. Options include "xyz", "gjf", "g03", "json". If
you have OpenBabel installed, any of the formats supported by
OpenBabel. Non-case sensitive.
Returns:
IMolecule or Molecule.
"""
from pymatgen.io.xyz import XYZ
from pymatgen.io.gaussian import GaussianInput
if fmt.lower() == "xyz":
m = XYZ.from_string(input_string).molecule
elif fmt in ["gjf", "g03", "g09", "com", "inp"]:
m = GaussianInput.from_string(input_string).molecule
elif fmt == "json":
d = json.loads(input_string)
return cls.from_dict(d)
elif fmt == "yaml":
import ruamel.yaml as yaml
d = yaml.safe_load(input_string)
return cls.from_dict(d)
else:
from pymatgen.io.babel import BabelMolAdaptor
m = BabelMolAdaptor.from_string(input_string,
file_format=fmt).pymatgen_mol
return cls.from_sites(m)
[docs] @classmethod
def from_file(cls, filename):
"""
Reads a molecule from a file. Supported formats include xyz,
gaussian input (gjf|g03|g09|com|inp), Gaussian output (.out|and
pymatgen's JSON serialized molecules. Using openbabel,
many more extensions are supported but requires openbabel to be
installed.
Args:
filename (str): The filename to read from.
Returns:
Molecule
"""
filename = str(filename)
from pymatgen.io.gaussian import GaussianOutput
with zopen(filename) as f:
contents = f.read()
fname = filename.lower()
if fnmatch(fname, "*.xyz*"):
return cls.from_str(contents, fmt="xyz")
if any([fnmatch(fname.lower(), "*.{}*".format(r))
for r in ["gjf", "g03", "g09", "com", "inp"]]):
return cls.from_str(contents, fmt="g09")
if any([fnmatch(fname.lower(), "*.{}*".format(r))
for r in ["out", "lis", "log"]]):
return GaussianOutput(filename).final_structure
if fnmatch(fname, "*.json*") or fnmatch(fname, "*.mson*"):
return cls.from_str(contents, fmt="json")
if fnmatch(fname, "*.yaml*"):
return cls.from_str(contents, fmt="yaml")
from pymatgen.io.babel import BabelMolAdaptor
m = re.search(r"\.(pdb|mol|mdl|sdf|sd|ml2|sy2|mol2|cml|mrv)",
filename.lower())
if m:
new = BabelMolAdaptor.from_file(filename,
m.group(1)).pymatgen_mol
new.__class__ = cls
return new
raise ValueError("Cannot determine file type.")
[docs]class Structure(IStructure, collections.abc.MutableSequence):
"""
Mutable version of structure.
"""
__hash__ = None # type: ignore
def __init__(self,
lattice: Union[List, np.ndarray, Lattice],
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
charge: float = None,
validate_proximity: bool = False,
to_unit_cell: bool = False,
coords_are_cartesian: bool = False,
site_properties: dict = None):
"""
Create a periodic structure.
Args:
lattice: The lattice, either as a pymatgen.core.lattice.Lattice or
simply as any 2D array. Each row should correspond to a lattice
vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a
lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
species: List of species on each site. Can take in flexible input,
including:
i. A sequence of element / specie specified either as string
symbols, e.g. ["Li", "Fe2+", "P", ...] or atomic numbers,
e.g., (3, 56, ...) or actual Element or Specie objects.
ii. List of dict of elements/species and occupancies, e.g.,
[{"Fe" : 0.5, "Mn":0.5}, ...]. This allows the setup of
disordered structures.
coords (Nx3 array): list of fractional/cartesian coordinates of
each species.
charge (int): overall charge of the structure. Defaults to behavior
in SiteCollection where total charge is the sum of the oxidation
states.
validate_proximity (bool): Whether to check if there are sites
that are less than 0.01 Ang apart. Defaults to False.
to_unit_cell (bool): Whether to map all sites into the unit cell,
i.e., fractional coords between 0 and 1. Defaults to False.
coords_are_cartesian (bool): Set to True if you are providing
coordinates in cartesian coordinates. Defaults to False.
site_properties (dict): Properties associated with the sites as a
dict of sequences, e.g., {"magmom":[5,5,5,5]}. The sequences
have to be the same length as the atomic species and
fractional_coords. Defaults to None for no properties.
"""
super().__init__(
lattice, species, coords, charge=charge,
validate_proximity=validate_proximity, to_unit_cell=to_unit_cell,
coords_are_cartesian=coords_are_cartesian,
site_properties=site_properties)
self._sites = list(self._sites) # type: ignore
def __setitem__(self, i, site):
"""
Modify a site in the structure.
Args:
i (int, [int], slice, Specie-like): Indices to change. You can
specify these as an int, a list of int, or a species-like
string.
site (PeriodicSite/Specie/Sequence): Three options exist. You
can provide a PeriodicSite directly (lattice will be
checked). Or more conveniently, you can provide a
specie-like object or a tuple of up to length 3.
Examples:
s[0] = "Fe"
s[0] = Element("Fe")
both replaces the species only.
s[0] = "Fe", [0.5, 0.5, 0.5]
Replaces site and *fractional* coordinates. Any properties
are inherited from current site.
s[0] = "Fe", [0.5, 0.5, 0.5], {"spin": 2}
Replaces site and *fractional* coordinates and properties.
s[(0, 2, 3)] = "Fe"
Replaces sites 0, 2 and 3 with Fe.
s[0::2] = "Fe"
Replaces all even index sites with Fe.
s["Mn"] = "Fe"
Replaces all Mn in the structure with Fe. This is
a short form for the more complex replace_species.
s["Mn"] = "Fe0.5Co0.5"
Replaces all Mn in the structure with Fe: 0.5, Co: 0.5, i.e.,
creates a disordered structure!
"""
if isinstance(i, int):
indices = [i]
elif isinstance(i, (str, Element, Specie)):
self.replace_species({i: site})
return
elif isinstance(i, slice):
to_mod = self[i]
indices = [ii for ii, s in enumerate(self._sites)
if s in to_mod]
else:
indices = list(i)
for ii in indices:
if isinstance(site, PeriodicSite):
if site.lattice != self._lattice:
raise ValueError("PeriodicSite added must have same lattice "
"as Structure!")
if len(indices) != 1:
raise ValueError("Site assignments makes sense only for "
"single int indices!")
self._sites[ii] = site
else:
if isinstance(site, str) or (
not isinstance(site, collections.abc.Sequence)):
self._sites[ii].species = site
else:
self._sites[ii].species = site[0]
if len(site) > 1:
self._sites[ii].frac_coords = site[1]
if len(site) > 2:
self._sites[ii].properties = site[2]
def __delitem__(self, i):
"""
Deletes a site from the Structure.
"""
self._sites.__delitem__(i)
@property
def lattice(self):
"""
:return: Lattice assciated with structure.
"""
return self._lattice
@lattice.setter
def lattice(self, lattice):
self._lattice = lattice
for site in self._sites:
site.lattice = lattice
[docs] def append(self, species, coords, coords_are_cartesian=False,
validate_proximity=False, properties=None):
"""
Append a site to the structure.
Args:
species: Species of inserted site
coords (3x1 array): Coordinates of inserted site
coords_are_cartesian (bool): Whether coordinates are cartesian.
Defaults to False.
validate_proximity (bool): Whether to check if inserted site is
too close to an existing site. Defaults to False.
properties (dict): Properties of the site.
Returns:
New structure with inserted site.
"""
return self.insert(len(self), species, coords,
coords_are_cartesian=coords_are_cartesian,
validate_proximity=validate_proximity,
properties=properties)
[docs] def insert(self, i, species, coords, coords_are_cartesian=False,
validate_proximity=False, properties=None):
"""
Insert a site to the structure.
Args:
i (int): Index to insert site
species (species-like): Species of inserted site
coords (3x1 array): Coordinates of inserted site
coords_are_cartesian (bool): Whether coordinates are cartesian.
Defaults to False.
validate_proximity (bool): Whether to check if inserted site is
too close to an existing site. Defaults to False.
properties (dict): Properties associated with the site.
Returns:
New structure with inserted site.
"""
if not coords_are_cartesian:
new_site = PeriodicSite(species, coords, self._lattice,
properties=properties)
else:
frac_coords = self._lattice.get_fractional_coords(coords)
new_site = PeriodicSite(species, frac_coords, self._lattice,
properties=properties)
if validate_proximity:
for site in self:
if site.distance(new_site) < self.DISTANCE_TOLERANCE:
raise ValueError("New site is too close to an existing "
"site!")
self._sites.insert(i, new_site)
[docs] def replace(self, i, species, coords=None, coords_are_cartesian=False,
properties=None):
"""
Replace a single site. Takes either a species or a dict of species and
occupations.
Args:
i (int): Index of the site in the _sites list.
species (species-like): Species of replacement site
coords (3x1 array): Coordinates of replacement site. If None,
the current coordinates are assumed.
coords_are_cartesian (bool): Whether coordinates are cartesian.
Defaults to False.
properties (dict): Properties associated with the site.
"""
if coords is None:
frac_coords = self[i].frac_coords
elif coords_are_cartesian:
frac_coords = self._lattice.get_fractional_coords(coords)
else:
frac_coords = coords
new_site = PeriodicSite(species, frac_coords, self._lattice,
properties=properties)
self._sites[i] = new_site
[docs] def substitute(self, index, func_grp, bond_order=1):
"""
Substitute atom at index with a functional group.
Args:
index (int): Index of atom to substitute.
func_grp: Substituent molecule. There are two options:
1. Providing an actual Molecule as the input. The first atom
must be a DummySpecie X, indicating the position of
nearest neighbor. The second atom must be the next
nearest atom. For example, for a methyl group
substitution, func_grp should be X-CH3, where X is the
first site and C is the second site. What the code will
do is to remove the index site, and connect the nearest
neighbor to the C atom in CH3. The X-C bond indicates the
directionality to connect the atoms.
2. A string name. The molecule will be obtained from the
relevant template in func_groups.json.
bond_order (int): A specified bond order to calculate the bond
length between the attached functional group and the nearest
neighbor site. Defaults to 1.
"""
# Find the nearest neighbor that is not a terminal atom.
all_non_terminal_nn = []
for nn, dist, _, _ in self.get_neighbors(self[index], 3):
# Check that the nn has neighbors within a sensible distance but
# is not the site being substituted.
for inn, dist2, _, _ in self.get_neighbors(nn, 3):
if inn != self[index] and \
dist2 < 1.2 * get_bond_length(nn.specie, inn.specie):
all_non_terminal_nn.append((nn, dist))
break
if len(all_non_terminal_nn) == 0:
raise RuntimeError("Can't find a non-terminal neighbor to attach"
" functional group to.")
non_terminal_nn = min(all_non_terminal_nn, key=lambda d: d[1])[0]
# Set the origin point to be the coordinates of the nearest
# non-terminal neighbor.
origin = non_terminal_nn.coords
# Pass value of functional group--either from user-defined or from
# functional.json
if isinstance(func_grp, Molecule):
func_grp = func_grp
else:
# Check to see whether the functional group is in database.
if func_grp not in FunctionalGroups:
raise RuntimeError("Can't find functional group in list. "
"Provide explicit coordinate instead")
func_grp = FunctionalGroups[func_grp]
# If a bond length can be found, modify func_grp so that the X-group
# bond length is equal to the bond length.
try:
bl = get_bond_length(non_terminal_nn.specie, func_grp[1].specie,
bond_order=bond_order)
# Catches for case of incompatibility between Element(s) and Specie(s)
except TypeError:
bl = None
if bl is not None:
func_grp = func_grp.copy()
vec = func_grp[0].coords - func_grp[1].coords
vec /= np.linalg.norm(vec)
func_grp[0] = "X", func_grp[1].coords + float(bl) * vec
# Align X to the origin.
x = func_grp[0]
func_grp.translate_sites(list(range(len(func_grp))), origin - x.coords)
# Find angle between the attaching bond and the bond to be replaced.
v1 = func_grp[1].coords - origin
v2 = self[index].coords - origin
angle = get_angle(v1, v2)
if 1 < abs(angle % 180) < 179:
# For angles which are not 0 or 180, we perform a rotation about
# the origin along an axis perpendicular to both bonds to align
# bonds.
axis = np.cross(v1, v2)
op = SymmOp.from_origin_axis_angle(origin, axis, angle)
func_grp.apply_operation(op)
elif abs(abs(angle) - 180) < 1:
# We have a 180 degree angle. Simply do an inversion about the
# origin
for i, fg in enumerate(func_grp):
func_grp[i] = (fg.species, origin - (fg.coords - origin))
# Remove the atom to be replaced, and add the rest of the functional
# group.
del self[index]
for site in func_grp[1:]:
s_new = PeriodicSite(site.species, site.coords,
self.lattice, coords_are_cartesian=True)
self._sites.append(s_new)
[docs] def remove_species(self, species):
"""
Remove all occurrences of several species from a structure.
Args:
species: Sequence of species to remove, e.g., ["Li", "Na"].
"""
new_sites = []
species = [get_el_sp(s) for s in species]
for site in self._sites:
new_sp_occu = {sp: amt for sp, amt in site.species.items()
if sp not in species}
if len(new_sp_occu) > 0:
new_sites.append(PeriodicSite(
new_sp_occu, site.frac_coords, self._lattice,
properties=site.properties))
self._sites = new_sites
[docs] def remove_sites(self, indices):
"""
Delete sites with at indices.
Args:
indices: Sequence of indices of sites to delete.
"""
self._sites = [s for i, s in enumerate(self._sites)
if i not in indices]
[docs] def apply_operation(self, symmop, fractional=False):
"""
Apply a symmetry operation to the structure and return the new
structure. The lattice is operated by the rotation matrix only.
Coords are operated in full and then transformed to the new lattice.
Args:
symmop (SymmOp): Symmetry operation to apply.
fractional (bool): Whether the symmetry operation is applied in
fractional space. Defaults to False, i.e., symmetry operation
is applied in cartesian coordinates.
"""
if not fractional:
self._lattice = Lattice([symmop.apply_rotation_only(row)
for row in self._lattice.matrix])
def operate_site(site):
new_cart = symmop.operate(site.coords)
new_frac = self._lattice.get_fractional_coords(new_cart)
return PeriodicSite(site.species, new_frac,
self._lattice,
properties=site.properties,
skip_checks=True)
else:
new_latt = np.dot(symmop.rotation_matrix, self._lattice.matrix)
self._lattice = Lattice(new_latt)
def operate_site(site):
return PeriodicSite(site.species,
symmop.operate(site.frac_coords),
self._lattice,
properties=site.properties,
skip_checks=True)
self._sites = [operate_site(s) for s in self._sites]
@deprecated(message="Simply set using Structure.lattice = lattice. This will be removed in pymatgen v2020.")
def modify_lattice(self, new_lattice):
"""
Modify the lattice of the structure. Mainly used for changing the
basis.
Args:
new_lattice (Lattice): New lattice
"""
self._lattice = new_lattice
for site in self._sites:
site.lattice = new_lattice
[docs] def apply_strain(self, strain):
"""
Apply a strain to the lattice.
Args:
strain (float or list): Amount of strain to apply. Can be a float,
or a sequence of 3 numbers. E.g., 0.01 means all lattice
vectors are increased by 1%. This is equivalent to calling
modify_lattice with a lattice with lattice parameters that
are 1% larger.
"""
s = (1 + np.array(strain)) * np.eye(3)
self.lattice = Lattice(np.dot(self._lattice.matrix.T, s).T)
[docs] def sort(self, key=None, reverse=False):
"""
Sort a structure in place. The parameters have the same meaning as in
list.sort. By default, sites are sorted by the electronegativity of
the species. The difference between this method and
get_sorted_structure (which also works in IStructure) is that the
latter returns a new Structure, while this just sorts the Structure
in place.
Args:
key: Specifies a function of one argument that is used to extract
a comparison key from each list element: key=str.lower. The
default value is None (compare the elements directly).
reverse (bool): If set to True, then the list elements are sorted
as if each comparison were reversed.
"""
self._sites.sort(key=key, reverse=reverse)
[docs] def translate_sites(self, indices, vector, frac_coords=True,
to_unit_cell=True):
"""
Translate specific sites by some vector, keeping the sites within the
unit cell.
Args:
indices: Integer or List of site indices on which to perform the
translation.
vector: Translation vector for sites.
frac_coords (bool): Whether the vector corresponds to fractional or
cartesian coordinates.
to_unit_cell (bool): Whether new sites are transformed to unit
cell
"""
if not isinstance(indices, collections.abc.Iterable):
indices = [indices]
for i in indices:
site = self._sites[i]
if frac_coords:
fcoords = site.frac_coords + vector
else:
fcoords = self._lattice.get_fractional_coords(
site.coords + vector)
if to_unit_cell:
fcoords = np.mod(fcoords, 1)
self._sites[i].frac_coords = fcoords
[docs] def rotate_sites(self, indices=None, theta=0, axis=None, anchor=None,
to_unit_cell=True):
"""
Rotate specific sites by some angle around vector at anchor.
Args:
indices (list): List of site indices on which to perform the
translation.
theta (float): Angle in radians
axis (3x1 array): Rotation axis vector.
anchor (3x1 array): Point of rotation.
to_unit_cell (bool): Whether new sites are transformed to unit
cell
"""
from numpy.linalg import norm
from numpy import cross, eye
from scipy.linalg import expm
if indices is None:
indices = range(len(self))
if axis is None:
axis = [0, 0, 1]
if anchor is None:
anchor = [0, 0, 0]
anchor = np.array(anchor)
axis = np.array(axis)
theta %= 2 * np.pi
rm = expm(cross(eye(3), axis / norm(axis)) * theta)
for i in indices:
site = self._sites[i]
coords = ((np.dot(rm, np.array(site.coords - anchor).T)).T + anchor).ravel()
new_site = PeriodicSite(
site.species, coords, self._lattice,
to_unit_cell=to_unit_cell, coords_are_cartesian=True,
properties=site.properties,
skip_checks=True)
self._sites[i] = new_site
[docs] def perturb(self, distance, min_distance=None):
"""
Performs a random perturbation of the sites in a structure to break
symmetries.
Args:
distance (float): Distance in angstroms by which to perturb each
site.
min_distance (None, int, or float): if None, all displacements will
be equal amplitude. If int or float, perturb each site a
distance drawn from the uniform distribution between
'min_distance' and 'distance'.
"""
def get_rand_vec():
# deals with zero vectors.
vector = np.random.randn(3)
vnorm = np.linalg.norm(vector)
dist = distance
if isinstance(min_distance, (float, int)):
dist = np.random.uniform(min_distance, dist)
return vector / vnorm * dist if vnorm != 0 else get_rand_vec()
for i in range(len(self._sites)):
self.translate_sites([i], get_rand_vec(), frac_coords=False)
[docs] def make_supercell(self, scaling_matrix, to_unit_cell=True):
"""
Create a supercell.
Args:
scaling_matrix: A scaling matrix for transforming the lattice
vectors. Has to be all integers. Several options are possible:
a. A full 3x3 scaling matrix defining the linear combination
the old lattice vectors. E.g., [[2,1,0],[0,3,0],[0,0,
1]] generates a new structure with lattice vectors a' =
2a + b, b' = 3b, c' = c where a, b, and c are the lattice
vectors of the original structure.
b. An sequence of three scaling factors. E.g., [2, 1, 1]
specifies that the supercell should have dimensions 2a x b x
c.
c. A number, which simply scales all lattice vectors by the
same factor.
to_unit_cell: Whether or not to fall back sites into the unit cell
"""
s = self * scaling_matrix
if to_unit_cell:
for site in s:
site.to_unit_cell(in_place=True)
self._sites = s.sites
self._lattice = s.lattice
[docs] def scale_lattice(self, volume):
"""
Performs a scaling of the lattice vectors so that length proportions
and angles are preserved.
Args:
volume (float): New volume of the unit cell in A^3.
"""
self.lattice = self._lattice.scale(volume)
[docs] def merge_sites(self, tol=0.01, mode="sum"):
"""
Merges sites (adding occupancies) within tol of each other.
Removes site properties.
Args:
tol (float): Tolerance for distance to merge sites.
mode (str): Three modes supported. "delete" means duplicate sites are
deleted. "sum" means the occupancies are summed for the sites.
"average" means that the site is deleted but the properties are averaged
Only first letter is considered.
"""
mode = mode.lower()[0]
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import fcluster, linkage
d = self.distance_matrix
np.fill_diagonal(d, 0)
clusters = fcluster(linkage(squareform((d + d.T) / 2)),
tol, 'distance')
sites = []
for c in np.unique(clusters):
inds = np.where(clusters == c)[0]
species = self[inds[0]].species
coords = self[inds[0]].frac_coords
props = self[inds[0]].properties
for n, i in enumerate(inds[1:]):
sp = self[i].species
if mode == "s":
species += sp
offset = self[i].frac_coords - coords
coords = coords + ((offset - np.round(offset)) / (n + 2)).astype(
coords.dtype)
for key in props.keys():
if props[key] is not None and self[i].properties[key] != props[key]:
if mode == 'a' and isinstance(props[key], float):
# update a running total
props[key] = props[key] * (n + 1) / (n + 2) + self[i].properties[key] / (n + 2)
else:
props[key] = None
warnings.warn("Sites with different site property %s are merged. "
"So property is set to none" % key)
sites.append(PeriodicSite(species, coords, self.lattice, properties=props))
self._sites = sites
[docs] def set_charge(self, new_charge: float = 0.):
"""
Sets the overall structure charge
Args:
new_charge (float): new charge to set
"""
self._charge = new_charge
[docs]class Molecule(IMolecule, collections.abc.MutableSequence):
"""
Mutable Molecule. It has all the methods in IMolecule, but in addition,
it allows a user to perform edits on the molecule.
"""
__hash__ = None # type: ignore
def __init__(self,
species: Sequence[Union[str, Element, Specie, DummySpecie, Composition]],
coords: Sequence[Sequence[float]],
charge: float = 0.0,
spin_multiplicity: float = None,
validate_proximity: bool = False,
site_properties: dict = None):
"""
Creates a MutableMolecule.
Args:
species: list of atomic species. Possible kinds of input include a
list of dict of elements/species and occupancies, a List of
elements/specie specified as actual Element/Specie, Strings
("Fe", "Fe2+") or atomic numbers (1,56).
coords (3x1 array): list of cartesian coordinates of each species.
charge (float): Charge for the molecule. Defaults to 0.
spin_multiplicity (int): Spin multiplicity for molecule.
Defaults to None, which means that the spin multiplicity is
set to 1 if the molecule has no unpaired electrons and to 2
if there are unpaired electrons.
validate_proximity (bool): Whether to check if there are sites
that are less than 1 Ang apart. Defaults to False.
site_properties (dict): Properties associated with the sites as
a dict of sequences, e.g., {"magmom":[5,5,5,5]}. The
sequences have to be the same length as the atomic species
and fractional_coords. Defaults to None for no properties.
"""
super().__init__(species, coords, charge=charge,
spin_multiplicity=spin_multiplicity,
validate_proximity=validate_proximity,
site_properties=site_properties)
self._sites = list(self._sites) # type: ignore
def __setitem__(self, i, site):
"""
Modify a site in the molecule.
Args:
i (int, [int], slice, Specie-like): Indices to change. You can
specify these as an int, a list of int, or a species-like
string.
site (PeriodicSite/Specie/Sequence): Three options exist. You can
provide a Site directly, or for convenience, you can provide
simply a Specie-like string/object, or finally a (Specie,
coords) sequence, e.g., ("Fe", [0.5, 0.5, 0.5]).
"""
if isinstance(i, int):
indices = [i]
elif isinstance(i, (str, Element, Specie)):
self.replace_species({i: site})
return
elif isinstance(i, slice):
to_mod = self[i]
indices = [ii for ii, s in enumerate(self._sites)
if s in to_mod]
else:
indices = list(i)
for ii in indices:
if isinstance(site, Site):
self._sites[ii] = site
else:
if isinstance(site, str) or (
not isinstance(site, collections.abc.Sequence)):
self._sites[ii].species = site
else:
self._sites[ii].species = site[0]
if len(site) > 1:
self._sites[ii].coords = site[1]
if len(site) > 2:
self._sites[ii].properties = site[2]
def __delitem__(self, i):
"""
Deletes a site from the Structure.
"""
self._sites.__delitem__(i)
[docs] def append(self, species, coords, validate_proximity=True, properties=None):
"""
Appends a site to the molecule.
Args:
species: Species of inserted site
coords: Coordinates of inserted site
validate_proximity (bool): Whether to check if inserted site is
too close to an existing site. Defaults to True.
properties (dict): A dict of properties for the Site.
Returns:
New molecule with inserted site.
"""
return self.insert(len(self), species, coords,
validate_proximity=validate_proximity,
properties=properties)
[docs] def set_charge_and_spin(self, charge: float, spin_multiplicity: Optional[float] = None):
"""
Set the charge and spin multiplicity.
Args:
charge (int): Charge for the molecule. Defaults to 0.
spin_multiplicity (int): Spin multiplicity for molecule.
Defaults to None, which means that the spin multiplicity is
set to 1 if the molecule has no unpaired electrons and to 2
if there are unpaired electrons.
"""
self._charge = charge
nelectrons = 0.0
for site in self._sites:
for sp, amt in site.species.items():
if not isinstance(sp, DummySpecie):
nelectrons += sp.Z * amt
nelectrons -= charge
self._nelectrons = nelectrons
if spin_multiplicity:
if (nelectrons + spin_multiplicity) % 2 != 1:
raise ValueError(
"Charge of {} and spin multiplicity of {} is"
" not possible for this molecule".format(
self._charge, spin_multiplicity))
self._spin_multiplicity = spin_multiplicity
else:
self._spin_multiplicity = 1 if nelectrons % 2 == 0 else 2
[docs] def insert(self, i, species, coords, validate_proximity=False,
properties=None):
"""
Insert a site to the molecule.
Args:
i (int): Index to insert site
species: species of inserted site
coords (3x1 array): coordinates of inserted site
validate_proximity (bool): Whether to check if inserted site is
too close to an existing site. Defaults to True.
properties (dict): Dict of properties for the Site.
Returns:
New molecule with inserted site.
"""
new_site = Site(species, coords, properties=properties)
if validate_proximity:
for site in self:
if site.distance(new_site) < self.DISTANCE_TOLERANCE:
raise ValueError("New site is too close to an existing "
"site!")
self._sites.insert(i, new_site)
[docs] def remove_species(self, species):
"""
Remove all occurrences of a species from a molecule.
Args:
species: Species to remove.
"""
new_sites = []
species = [get_el_sp(sp) for sp in species]
for site in self._sites:
new_sp_occu = {sp: amt for sp, amt in site.species.items()
if sp not in species}
if len(new_sp_occu) > 0:
new_sites.append(Site(new_sp_occu, site.coords,
properties=site.properties))
self._sites = new_sites
[docs] def remove_sites(self, indices):
"""
Delete sites with at indices.
Args:
indices: Sequence of indices of sites to delete.
"""
self._sites = [self._sites[i] for i in range(len(self._sites))
if i not in indices]
[docs] def translate_sites(self, indices=None, vector=None):
"""
Translate specific sites by some vector, keeping the sites within the
unit cell.
Args:
indices (list): List of site indices on which to perform the
translation.
vector (3x1 array): Translation vector for sites.
"""
if indices is None:
indices = range(len(self))
if vector is None:
vector == [0, 0, 0]
for i in indices:
site = self._sites[i]
new_site = Site(site.species, site.coords + vector,
properties=site.properties)
self._sites[i] = new_site
[docs] def rotate_sites(self, indices=None, theta=0, axis=None, anchor=None):
"""
Rotate specific sites by some angle around vector at anchor.
Args:
indices (list): List of site indices on which to perform the
translation.
theta (float): Angle in radians
axis (3x1 array): Rotation axis vector.
anchor (3x1 array): Point of rotation.
"""
from numpy.linalg import norm
from numpy import cross, eye
from scipy.linalg import expm
if indices is None:
indices = range(len(self))
if axis is None:
axis = [0, 0, 1]
if anchor is None:
anchor = [0, 0, 0]
anchor = np.array(anchor)
axis = np.array(axis)
theta %= 2 * np.pi
rm = expm(cross(eye(3), axis / norm(axis)) * theta)
for i in indices:
site = self._sites[i]
s = ((np.dot(rm, (site.coords - anchor).T)).T + anchor).ravel()
new_site = Site(site.species, s,
properties=site.properties)
self._sites[i] = new_site
[docs] def perturb(self, distance):
"""
Performs a random perturbation of the sites in a structure to break
symmetries.
Args:
distance (float): Distance in angstroms by which to perturb each
site.
"""
def get_rand_vec():
# deals with zero vectors.
vector = np.random.randn(3)
vnorm = np.linalg.norm(vector)
return vector / vnorm * distance if vnorm != 0 else get_rand_vec()
for i in range(len(self._sites)):
self.translate_sites([i], get_rand_vec())
[docs] def apply_operation(self, symmop):
"""
Apply a symmetry operation to the molecule.
Args:
symmop (SymmOp): Symmetry operation to apply.
"""
def operate_site(site):
new_cart = symmop.operate(site.coords)
return Site(site.species, new_cart,
properties=site.properties)
self._sites = [operate_site(s) for s in self._sites]
[docs] def copy(self):
"""
Convenience method to get a copy of the molecule.
Returns:
A copy of the Molecule.
"""
return self.__class__.from_sites(self)
[docs] def substitute(self, index, func_grp, bond_order=1):
"""
Substitute atom at index with a functional group.
Args:
index (int): Index of atom to substitute.
func_grp: Substituent molecule. There are two options:
1. Providing an actual molecule as the input. The first atom
must be a DummySpecie X, indicating the position of
nearest neighbor. The second atom must be the next
nearest atom. For example, for a methyl group
substitution, func_grp should be X-CH3, where X is the
first site and C is the second site. What the code will
do is to remove the index site, and connect the nearest
neighbor to the C atom in CH3. The X-C bond indicates the
directionality to connect the atoms.
2. A string name. The molecule will be obtained from the
relevant template in func_groups.json.
bond_order (int): A specified bond order to calculate the bond
length between the attached functional group and the nearest
neighbor site. Defaults to 1.
"""
# Find the nearest neighbor that is not a terminal atom.
all_non_terminal_nn = []
for nn in self.get_neighbors(self[index], 3):
# Check that the nn has neighbors within a sensible distance but
# is not the site being substituted.
for nn2 in self.get_neighbors(nn, 3):
if nn2 != self[index] and nn2.nn_distance < 1.2 * get_bond_length(nn.specie, nn2.specie):
all_non_terminal_nn.append(nn)
break
if len(all_non_terminal_nn) == 0:
raise RuntimeError("Can't find a non-terminal neighbor to attach"
" functional group to.")
non_terminal_nn = min(all_non_terminal_nn, key=lambda nn: nn.nn_distance)
# Set the origin point to be the coordinates of the nearest
# non-terminal neighbor.
origin = non_terminal_nn.coords
# Pass value of functional group--either from user-defined or from
# functional.json
if isinstance(func_grp, Molecule):
func_grp = func_grp
else:
# Check to see whether the functional group is in database.
if func_grp not in FunctionalGroups:
raise RuntimeError("Can't find functional group in list. "
"Provide explicit coordinate instead")
func_grp = FunctionalGroups[func_grp]
# If a bond length can be found, modify func_grp so that the X-group
# bond length is equal to the bond length.
bl = get_bond_length(non_terminal_nn.specie, func_grp[1].specie,
bond_order=bond_order)
if bl is not None:
func_grp = func_grp.copy()
vec = func_grp[0].coords - func_grp[1].coords
vec /= np.linalg.norm(vec)
func_grp[0] = "X", func_grp[1].coords + float(bl) * vec
# Align X to the origin.
x = func_grp[0]
func_grp.translate_sites(list(range(len(func_grp))), origin - x.coords)
# Find angle between the attaching bond and the bond to be replaced.
v1 = func_grp[1].coords - origin
v2 = self[index].coords - origin
angle = get_angle(v1, v2)
if 1 < abs(angle % 180) < 179:
# For angles which are not 0 or 180, we perform a rotation about
# the origin along an axis perpendicular to both bonds to align
# bonds.
axis = np.cross(v1, v2)
op = SymmOp.from_origin_axis_angle(origin, axis, angle)
func_grp.apply_operation(op)
elif abs(abs(angle) - 180) < 1:
# We have a 180 degree angle. Simply do an inversion about the
# origin
for i, fg in enumerate(func_grp):
func_grp[i] = (fg.species, origin - (fg.coords - origin))
# Remove the atom to be replaced, and add the rest of the functional
# group.
del self[index]
for site in func_grp[1:]:
self._sites.append(site)
[docs]class StructureError(Exception):
"""
Exception class for Structure.
Raised when the structure has problems, e.g., atoms that are too close.
"""
pass
with open(os.path.join(os.path.dirname(__file__),
"func_groups.json"), "rt") as f:
FunctionalGroups = {k: Molecule(v["species"], v["coords"])
for k, v in json.load(f).items()}