import copy
import itertools
import logging
from collections import defaultdict, Counter
import numpy as np
from gvasp.common.base import Atoms, Lattice, Atom
from gvasp.common.error import StructureOverlapError
logger = logging.getLogger(__name__)
[docs]class Structure(object):
def __init__(self, atoms: Atoms = None, lattice: Lattice = None):
"""
@parameter:
atoms: atoms of the structure, <class Atoms>
lattice: Lattice vector
@property:
neighbour_tabel: neighbour table of structure, number of neighbour_atom default is 12
@func:
find_neighbour_tables(self, neighbour_num: int = 12, adj_matrix=None) --> self.neighbour_table
dist(structure1, structure2): calculate distance between two structures
align(structure1, structure2, tolerance1=0.2, tolerance2=100): tailor atoms' order to align structures
from_file(name, style=None, mol_index=None, **kargs) --> Structure
write(self, name, system=None, factor=1): output the structure into `POSCAR/CONTCAR` file
"""
self.atoms = atoms
self.lattice = lattice
self.neighbour_table = NeighbourTable(list)
def __sub__(self, other):
diff_frac = self.atoms.frac_coord - other.atoms.frac_coord
diff_frac = np.where(diff_frac >= 0.5, diff_frac - 1, diff_frac)
diff_frac = np.where(diff_frac <= -0.5, diff_frac + 1, diff_frac)
diff_cart = np.dot(diff_frac, self.lattice.matrix)
return diff_cart
def __eq__(self, other):
return self.lattice == other.lattice and self.atoms == other.atoms
def __repr__(self):
return f"------------------------------------------------------------\n" \
f"<Structure> \n" \
f"-Lattice- \n" \
f"{self.lattice.matrix} \n" \
f"-Atoms- \n" \
f"{self.atoms} \n" \
f"------------------------------------------------------------" \
if self.lattice is not None else f"<Structure object>"
[docs] @staticmethod
def dist(structure1, structure2):
"""
Calculate the distance of two structures, distance = sqrt(sum((i-j)**2))
@param:
structure1: first Structure
structure2: second Structure
@return
diff: distance between two structures
"""
diff = structure1 - structure2
diff = np.sum(diff ** 2) ** 0.5
return diff
[docs] @staticmethod
def align(structure1, structure2, tolerance1=0.2, tolerance2=100):
"""
Tailor the atoms' order to make the distance of structure1 and structure2 minimum
@param:
structure1: first Structure
structure2: second Structure
tolerance1: first sort tolerance
tolerance2: second sort tolerance
"""
def sort(atoms1, match_list, atoms1_sort, atoms2_sort, tolerance=0.2):
for atom_i in atoms1:
distances_candidate = []
bonds_i = Counter([item[0].formula for item in atom_i.bonds])
for atom_j in match_list:
bonds_j = Counter([item[0].formula for item in atom_j.bonds])
formula_cond = (atom_j.formula == atom_i.formula)
bonds_cond = (bonds_i == bonds_j)
if formula_cond and (bonds_cond or tolerance >= 10):
image = Atom.search_image(atom_i, atom_j)
atom_j_image = Atom(formula=atom_j.formula, frac_coord=atom_j.frac_coord + image).set_coord(
lattice=structure2.lattice)
distance = np.linalg.norm(atom_j_image.cart_coord - atom_i.cart_coord)
logger.debug(f"distance={distance}")
if distance <= tolerance:
distances_candidate.append((atom_j, distance))
if len(distances_candidate):
sorted_distances_candidate = sorted(distances_candidate, key=lambda x: x[1])
atoms1_sort.append(atom_i)
atoms2_sort.append(sorted_distances_candidate[0][0])
match_list.remove(sorted_distances_candidate[0][0])
return atoms1_sort, atoms2_sort
if not len(structure1.atoms.bonds):
structure1.find_neighbour_table()
if not len(structure2.atoms.bonds):
structure2.find_neighbour_table()
atoms1, atoms2 = structure1.atoms, structure2.atoms
match_list = copy.deepcopy(atoms2.atom_list)
atoms1_sort, atoms2_sort = [], []
# First align, according to the `min_distance rule`
atoms1_sort, atoms2_sort = sort(atoms1, match_list, atoms1_sort, atoms2_sort, tolerance1)
# Second align
atoms1_remain = [atom for atom in atoms1 if atom not in atoms1_sort]
atoms2_remain = [atom for atom in atoms2 if atom not in atoms2_sort]
atoms1_sort, atoms2_sort = sort(atoms1_remain, atoms2_remain, atoms1_sort, atoms2_sort, tolerance2)
# construct new structure
for atom_i, atom_j in zip(atoms1_sort, atoms2_sort):
atom_i.order, atom_j.order = None, None
atoms1_new, atoms2_new = Atoms.from_list(atoms1_sort), Atoms.from_list(atoms2_sort)
structure1_new = Structure(atoms=atoms1_new, lattice=structure1.lattice)
structure2_new = Structure(atoms=atoms2_new, lattice=structure2.lattice)
return structure1_new, structure2_new
# TODO: performance optimization
[docs] def find_neighbour_table(self, neighbour_num: int = 12, adj_matrix=None, sort=True, including_self=False):
new_atoms = []
neighbour_table = NeighbourTable(list)
for atom_i in self.atoms:
neighbour_table_i = []
atom_j_list = self.atoms if adj_matrix is None else [self.atoms[atom_j_order] for atom_j_order in
adj_matrix[atom_i.order]]
for atom_j in atom_j_list:
if not including_self and atom_i == atom_j:
continue
image = Atom.search_image(atom_i, atom_j)
atom_j_image = Atom(formula=atom_j.formula, frac_coord=atom_j.frac_coord + image).set_coord(
lattice=self.lattice)
distance = np.linalg.norm(atom_j_image.cart_coord - atom_i.cart_coord)
logger.debug(f"distance={distance}")
if f'Element {atom_j.formula}' in atom_i._default_bonds.keys() \
and distance <= atom_i._default_bonds[f'Element {atom_j.formula}'] * 1.1:
neighbour_table_i.append((atom_j, distance, (atom_j_image.cart_coord - atom_i.cart_coord), 1))
else:
neighbour_table_i.append((atom_j, distance, (atom_j_image.cart_coord - atom_i.cart_coord), 0))
neighbour_table_i = sorted(neighbour_table_i,
key=lambda x: x[1]) if adj_matrix is None and sort else neighbour_table_i
neighbour_table[atom_i] = neighbour_table_i[:neighbour_num]
# update bonds && coordination number
atom_i.bonds = [(item[0], item[1]) for item in neighbour_table_i if item[3]]
atom_i.coordination_number = sum([item[3] for item in neighbour_table_i])
new_atoms.append(atom_i)
self.atoms = Atoms.from_list(new_atoms)
setattr(self, "neighbour_table", neighbour_table)
return self
[docs] def check_overlap(self, cutoff=0.1):
self.find_neighbour_table(neighbour_num=1)
dist = self.neighbour_table.dist.reshape(-1)
if dist[np.where(dist <= cutoff)].size:
raise StructureOverlapError(f"Exist atoms' distance <= {cutoff}, please check")
else:
logger.info("No structure overlap occurrence")
[docs] @staticmethod
def from_POSCAR(name):
logger.debug(f"Handle the {name}")
with open(name) as f:
cfg = f.readlines()
lattice = Lattice.from_string(cfg[2:5])
formula = [(name, int(count)) for name, count in zip(cfg[5].split(), cfg[6].split())]
formula = sum([[formula] * count for (formula, count) in formula], [])
selective = cfg[7].lower()[0] == "s"
coor_type_index = 8 if selective else 7
coor_type = cfg[coor_type_index].rstrip()
coords = np.array(
[[float(item) for item in coor.split()[:3]] for coor in
cfg[coor_type_index + 1:coor_type_index + 1 + len(formula)]])
frac_coord = coords if coor_type.lower()[0] == "d" else None
cart_coord = coords if coor_type.lower()[0] == "c" else None
selective_matrix = np.array(
list([item.split()[3:6] for item in
cfg[coor_type_index + 1:coor_type_index + 1 + len(formula)]])) if selective else None
atoms = Atoms(formula=formula, frac_coord=frac_coord, cart_coord=cart_coord, selective_matrix=selective_matrix)
atoms.set_coord(lattice)
return Structure(atoms=atoms, lattice=lattice)
[docs] @staticmethod
def from_cell(name):
logger.debug(f"Handle the {name}")
with open(name, "r") as f:
strings = f.readlines()
lattice_index = [index for index, line in enumerate(strings) if line.find("LATTICE_CART") != -1]
atom_index = [index for index, line in enumerate(strings) if line.find("POSITIONS_FRAC") != -1]
lattice = Lattice.from_string(strings[lattice_index[0] + 1:lattice_index[1]])
atom = [(line.split()[0], line.split()[1:]) for line in strings[atom_index[0] + 1:atom_index[1]]]
formula, frac_coord = list(map(list, zip(*atom)))
frac_coord = list(map(lambda x: [float(x[0]), float(x[1]), float(x[2])], frac_coord))
atoms = Atoms(formula=formula, frac_coord=frac_coord)
return Structure(atoms=atoms, lattice=lattice)
[docs] @staticmethod
def from_structure(structure, coord, type="cart"):
"""
Generate the Structure instance from original structure with changing its atoms' coord
Args:
structure: which structure you want to base
coord: coord for new structure, <frac or cart>
type: which type of new coord, should be one of ["frac", "cart"], default: "cart"
Returns: new Structure instance
"""
atoms = copy.deepcopy(structure.atoms)
if type == "cart":
atoms.frac_coord = [None] * len(atoms)
atoms.cart_coord = coord
elif type == "frac":
atoms.cart_coord = [None] * len(atoms)
atoms.frac_coord = coord
else:
raise TypeError(f"{type} not supported, should be `cart` or `frac`")
atoms.set_coord(structure.lattice)
return Structure(atoms=atoms, lattice=structure.lattice)
[docs] def write_POSCAR(self, name, title=None, factor=1.0):
title = title if title is not None else "AutoGenerated"
lattice = self.lattice.strings
elements = [(key, str(len(list(value)))) for key, value in itertools.groupby(self.atoms.formula)]
element_name, element_count = list(map(list, zip(*elements)))
element_name, element_count = " ".join(element_name), " ".join(element_count)
selective = None not in getattr(self.atoms, "selective_matrix")
coords = "\n".join([" ".join([f"{item:15.12f}" for item in atom.frac_coord]) for atom in self.atoms])
if selective:
coords = "".join([coord + "\t" + " ".join(selective_matrix) + "\n" for coord, selective_matrix in
zip(coords.split("\n"), self.atoms.selective_matrix)])
with open(name, "w") as f:
f.write(f"{title}\n")
f.write(f"{factor}\n")
f.write(lattice)
f.write(f"{element_name}\n")
f.write(f"{element_count}\n")
if selective:
f.write("Selective Dynamics\n")
f.write("Direct\n")
f.write(coords)
f.write("\n\n")
logger.debug(f"{name} write finished!")
[docs]class NeighbourTable(defaultdict):
def __repr__(self):
return " ".join([f"{key} <---> <{value[0]}> \n" for key, value in self.items()])
@property
def index(self): # adj_matrix
return np.array([[value[0].order for value in values] for key, values in self.items()])
@property
def index_tuple(self): # adj_matrix_tuple
return np.array([[(key.order, value[0].order) for value in values] for key, values in self.items()])
@property
def dist(self):
return np.array([[value[1] for value in values] for _, values in self.items()])
@property
def dist3d(self):
return np.array([[value[2] for value in values] for _, values in self.items()])
@property
def coordination(self):
return np.array([sum([value[3] for value in values]) for _, values in self.items()])