Rings

The rings class contains functions for calculating ring statistics on an Atoms objects. The implementation of rings is an adapted version of the code for calculating rings from https://github.com/MotokiShiga/sova-cui making the code compatible with the vitrum package.

Currently supported ring types are: - Guttman

Included functions for ring objects: - Center - Size

Ring

Bases: object

A class representing a ring in a atomistic system.

Source code in src/vitrum/rings.py

class Ring(object):
    """
    A class representing a ring in a atomistic system.
    """

    def __init__(self, atoms: Atoms, indexes: Optional[List[int]] = None):
        """
        Initialize a Ring object.

        Args:
            atoms (Atoms): An Atoms object representing the atoms in the ring.
            indexes (Optional[List[int]], optional): A list of indices of the atoms involved in the ring. Defaults to None.
        """
        self.atoms = atoms
        self.indexes = indexes
        self._roundness = None
        self._roughness = None
        self.ellipsoid_lengths = None
        self.atom_symbols = np.array(self.atoms.get_chemical_symbols())
        self.atom_types = np.unique(self.atom_symbols).tolist()
        self.atom_ids = [np.where(self.atom_symbols == atom_type)[0] for atom_type in self.atom_types]

    def center(self) -> np.ndarray:
        """
        Calculate the center of the ring.

        Returns:
            np.ndarray: An array of shape (3,) representing the center of the ring.
        """
        positions = self.atoms.get_positions()
        cell = np.diagonal(self.atoms.get_cell())
        differences = np.diff(positions, axis=0, append=positions[:1])
        crossings = np.floor_divide(differences + 0.5 * cell, cell).astype(int)
        adjusted_positions = positions - np.cumsum(cell * crossings, axis=0)
        center = np.mean(adjusted_positions, axis=0)
        center = np.mod(center + cell, cell)
        return center

    def size(self) -> int:
        """
        Calculate the size of the ring, i.e., the number of atoms in the ring.

        Returns:
            int: The size of the ring.
        """
        return len(self.atoms)

__init__(atoms, indexes=None)

Initialize a Ring object.

Parameters:

Name	Type	Description	Default
atoms	Atoms	An Atoms object representing the atoms in the ring.	required
indexes	Optional[List[int]]	A list of indices of the atoms involved in the ring. Defaults to None.	None

Source code in src/vitrum/rings.py

def __init__(self, atoms: Atoms, indexes: Optional[List[int]] = None):
    """
    Initialize a Ring object.

    Args:
        atoms (Atoms): An Atoms object representing the atoms in the ring.
        indexes (Optional[List[int]], optional): A list of indices of the atoms involved in the ring. Defaults to None.
    """
    self.atoms = atoms
    self.indexes = indexes
    self._roundness = None
    self._roughness = None
    self.ellipsoid_lengths = None
    self.atom_symbols = np.array(self.atoms.get_chemical_symbols())
    self.atom_types = np.unique(self.atom_symbols).tolist()
    self.atom_ids = [np.where(self.atom_symbols == atom_type)[0] for atom_type in self.atom_types]

center()

Calculate the center of the ring.

Returns:

Type	Description
ndarray	np.ndarray: An array of shape (3,) representing the center of the ring.

Source code in src/vitrum/rings.py

def center(self) -> np.ndarray:
    """
    Calculate the center of the ring.

    Returns:
        np.ndarray: An array of shape (3,) representing the center of the ring.
    """
    positions = self.atoms.get_positions()
    cell = np.diagonal(self.atoms.get_cell())
    differences = np.diff(positions, axis=0, append=positions[:1])
    crossings = np.floor_divide(differences + 0.5 * cell, cell).astype(int)
    adjusted_positions = positions - np.cumsum(cell * crossings, axis=0)
    center = np.mean(adjusted_positions, axis=0)
    center = np.mod(center + cell, cell)
    return center

size()

Calculate the size of the ring, i.e., the number of atoms in the ring.

Returns:

Name	Type	Description
int	int	The size of the ring.

Source code in src/vitrum/rings.py

def size(self) -> int:
    """
    Calculate the size of the ring, i.e., the number of atoms in the ring.

    Returns:
        int: The size of the ring.
    """
    return len(self.atoms)

RingAnalysis

A class for calculating and analyzing rings in atomistic systems.

Source code in src/vitrum/rings.py

class RingAnalysis:
    """
    A class for calculating and analyzing rings in atomistic systems.
    """

    def __init__(self, atoms: Atoms, included_atoms: List[str], bonding_dict: Optional[List[Tuple[str, str]]] = None):
        """
        Initialize the RingAnalysis class.

        Args:
            atoms (Atoms): An Atoms object representing the atoms in the system.
            included_atoms (List[str]): A list of strings representing the chemical symbols of the atoms to include in the analysis.
            bonding_dict (Optional[List[Tuple[str, str]]]): A list of allowed bonds, e.g., [('Si', 'O')].
        """
        super().__init__()
        self.bonding_dict = bonding_dict
        atoms = atoms[[atom.symbol in included_atoms for atom in atoms]]
        self.atoms = GlassAtoms(atoms)
        self.num_atoms = len(self.atoms)
        self.atom_symbols = np.array(self.atoms.get_chemical_symbols())
        self.atom_types = np.unique(self.atom_symbols).tolist()
        self.atom_ids = [np.where(self.atom_symbols == atom_type)[0] for atom_type in self.atom_types]
        self.rings = None


    def calculate(
        self,
        radii_factor: float = 1.3,
        repeat: Tuple[int, int, int] = (1, 1, 1),
        max_size: float = np.inf
    ) -> List[Ring]:
        """
        Calculate the rings in the system.

        Args:
            radii_factor (float): Factor to multiply covalent radii for neighbor search.
            repeat (Tuple[int, int, int]): Repeat unit cell.
            max_size (float): Maximum ring size.

        Returns:
            List[Ring]: A list of Ring objects representing the rings in the system.
        """
        bonds = self.bonding_dict

        rings = find_rings(ats=self.atoms, radii_factor=radii_factor, repeat=repeat, bonds=bonds, limit=max_size)

        self.rings = [Ring(self.atoms[list(r)], list(r)) for r in rings]
        return self.rings

    def write_rings(self, filename: str, format: str = 'extxyz'):
        """
        Write the rings to a file.

        Args:
            filename (str): The name of the file to write the rings to.
            format (str): The format of the file.
        """
        if self.rings is None:
            raise ValueError("Rings have not been calculated yet.")
        write(filename, [r.atoms for r in self.rings], format=format)

    def get_ring_size_distribution(self) -> Dict[int, int]:
        """
        Get the distribution of ring sizes.

        Returns:
            Dict[int, int]: A dictionary where keys are ring sizes and values are counts.
        """
        if self.rings is None:
            raise ValueError("Rings have not been calculated yet.")
        ring_sizes = [len(r.atoms) for r in self.rings]
        return dict(Counter(ring_sizes))

    def plot_ring_size_distribution(self, ax=None):
            """
            Plots the distribution of ring sizes using matplotlib.
            """
            import matplotlib.pyplot as plt

            dist = self.get_ring_size_distribution()
            if not dist:
                print("No rings found. Ensure you have run .calculate() first.")
                return

            sizes = sorted(dist.keys())
            counts = np.array([dist[size] for size in sizes])
            frequency = counts / np.sum(counts)

            if ax is None:
                fig, ax = plt.subplots(figsize=(9, 6))
                ax.set_xlabel('Ring Size', fontsize=12)
                ax.set_ylabel('Frequency', fontsize=12)
                ax.set_xticks(sizes)  

            ax.plot(sizes, frequency)

            return ax

__init__(atoms, included_atoms, bonding_dict=None)

Initialize the RingAnalysis class.

Parameters:

Name	Type	Description	Default
atoms	Atoms	An Atoms object representing the atoms in the system.	required
included_atoms	List[str]	A list of strings representing the chemical symbols of the atoms to include in the analysis.	required
bonding_dict	Optional[List[Tuple[str, str]]]	A list of allowed bonds, e.g., [('Si', 'O')].	None

Source code in src/vitrum/rings.py

def __init__(self, atoms: Atoms, included_atoms: List[str], bonding_dict: Optional[List[Tuple[str, str]]] = None):
    """
    Initialize the RingAnalysis class.

    Args:
        atoms (Atoms): An Atoms object representing the atoms in the system.
        included_atoms (List[str]): A list of strings representing the chemical symbols of the atoms to include in the analysis.
        bonding_dict (Optional[List[Tuple[str, str]]]): A list of allowed bonds, e.g., [('Si', 'O')].
    """
    super().__init__()
    self.bonding_dict = bonding_dict
    atoms = atoms[[atom.symbol in included_atoms for atom in atoms]]
    self.atoms = GlassAtoms(atoms)
    self.num_atoms = len(self.atoms)
    self.atom_symbols = np.array(self.atoms.get_chemical_symbols())
    self.atom_types = np.unique(self.atom_symbols).tolist()
    self.atom_ids = [np.where(self.atom_symbols == atom_type)[0] for atom_type in self.atom_types]
    self.rings = None

calculate(radii_factor=1.3, repeat=(1, 1, 1), max_size=np.inf)

Calculate the rings in the system.

Parameters:

Name	Type	Description	Default
radii_factor	float	Factor to multiply covalent radii for neighbor search.	1.3
repeat	Tuple[int, int, int]	Repeat unit cell.	(1, 1, 1)
max_size	float	Maximum ring size.	inf

Returns:

Type	Description
List[Ring]	List[Ring]: A list of Ring objects representing the rings in the system.

Source code in src/vitrum/rings.py

def calculate(
    self,
    radii_factor: float = 1.3,
    repeat: Tuple[int, int, int] = (1, 1, 1),
    max_size: float = np.inf
) -> List[Ring]:
    """
    Calculate the rings in the system.

    Args:
        radii_factor (float): Factor to multiply covalent radii for neighbor search.
        repeat (Tuple[int, int, int]): Repeat unit cell.
        max_size (float): Maximum ring size.

    Returns:
        List[Ring]: A list of Ring objects representing the rings in the system.
    """
    bonds = self.bonding_dict

    rings = find_rings(ats=self.atoms, radii_factor=radii_factor, repeat=repeat, bonds=bonds, limit=max_size)

    self.rings = [Ring(self.atoms[list(r)], list(r)) for r in rings]
    return self.rings

get_ring_size_distribution()

Get the distribution of ring sizes.

Returns:

Type	Description
Dict[int, int]	Dict[int, int]: A dictionary where keys are ring sizes and values are counts.

Source code in src/vitrum/rings.py

def get_ring_size_distribution(self) -> Dict[int, int]:
    """
    Get the distribution of ring sizes.

    Returns:
        Dict[int, int]: A dictionary where keys are ring sizes and values are counts.
    """
    if self.rings is None:
        raise ValueError("Rings have not been calculated yet.")
    ring_sizes = [len(r.atoms) for r in self.rings]
    return dict(Counter(ring_sizes))

plot_ring_size_distribution(ax=None)

Plots the distribution of ring sizes using matplotlib.

Source code in src/vitrum/rings.py

def plot_ring_size_distribution(self, ax=None):
        """
        Plots the distribution of ring sizes using matplotlib.
        """
        import matplotlib.pyplot as plt

        dist = self.get_ring_size_distribution()
        if not dist:
            print("No rings found. Ensure you have run .calculate() first.")
            return

        sizes = sorted(dist.keys())
        counts = np.array([dist[size] for size in sizes])
        frequency = counts / np.sum(counts)

        if ax is None:
            fig, ax = plt.subplots(figsize=(9, 6))
            ax.set_xlabel('Ring Size', fontsize=12)
            ax.set_ylabel('Frequency', fontsize=12)
            ax.set_xticks(sizes)  

        ax.plot(sizes, frequency)

        return ax

write_rings(filename, format='extxyz')

Write the rings to a file.

Parameters:

Name	Type	Description	Default
filename	str	The name of the file to write the rings to.	required
format	str	The format of the file.	'extxyz'

Source code in src/vitrum/rings.py

def write_rings(self, filename: str, format: str = 'extxyz'):
    """
    Write the rings to a file.

    Args:
        filename (str): The name of the file to write the rings to.
        format (str): The format of the file.
    """
    if self.rings is None:
        raise ValueError("Rings have not been calculated yet.")
    write(filename, [r.atoms for r in self.rings], format=format)

check_ring_is_periodic(ring, offsets)

Check if the ring wraps around the period cell, i.e., is not a true ring.

Parameters:

Name	Type	Description	Default
ring	List[int]	Atom indices of the ring.	required
offsets	Dict[Tuple[int, int], ndarray]	Unit cell offsets for all atoms pairs.	required

Returns:

Name	Type	Description
bool	bool	True if the ring is periodic (wraps around), False otherwise (contained within cell without wrapping sum).

Source code in src/vitrum/rings.py

def check_ring_is_periodic(ring: List[int], offsets: Dict[Tuple[int, int], np.ndarray]) -> bool:
    ''' 
    Check if the ring wraps around the period cell, i.e., is not a true ring.

    Args:
        ring (List[int]): Atom indices of the ring.
        offsets (Dict[Tuple[int, int], np.ndarray]): Unit cell offsets for all atoms pairs.

    Returns:
        bool: True if the ring is periodic (wraps around), False otherwise (contained within cell without wrapping sum).
    '''
    total_offset = np.zeros(3)
    for i in range(len(ring) - 1):
        total_offset += offsets[(ring[i], ring[i+1])]
    total_offset += offsets[(ring[-1], ring[0])]
    return np.all(total_offset == 0)

find_rings(ats, radii_factor=1.3, repeat=(1, 1, 1), bonds=None, limit=np.inf)

Find rings in the unit cell. Rings are found according to the definition of L. Guttman, J. Non-Cryst. Solids 1990, 116.

Parameters:

Name	Type	Description	Default
ats	Atoms	Atoms object containing the structure	required
radii_factor	float	Factor to multiply covalent radii for neighbor search	1.3
repeat	Tuple[int, int, int]	How often to repeat the unit cell in each direction. Increase for small cells.	(1, 1, 1)
bonds	Optional[List[Tuple[str, str]]]	List of allowed bonds, e.g., [('C', 'C'), ('C', 'O')], can be None to allow all bonds.	None
limit	float	Maximum ring size to search for.	inf

Returns:

Type	Description
List[List[int]]	List[List[int]]: A list of rings, where each ring is a list of atom indices.

Source code in src/vitrum/rings.py

def find_rings(
    ats: Atoms,
    radii_factor: float = 1.3,
    repeat: Tuple[int, int, int] = (1, 1, 1),
    bonds: Optional[List[Tuple[str, str]]] = None,
    limit: float = np.inf
) -> List[List[int]]:
    ''' 
    Find rings in the unit cell.
    Rings are found according to the definition of L. Guttman, J. Non-Cryst. Solids 1990, 116.

    Args:
        ats (ase.Atoms): Atoms object containing the structure
        radii_factor (float): Factor to multiply covalent radii for neighbor search
        repeat (Tuple[int, int, int]): How often to repeat the unit cell in each direction. Increase for small cells.
        bonds (Optional[List[Tuple[str, str]]]): List of allowed bonds, e.g., [('C', 'C'), ('C', 'O')], can be None to allow all bonds.
        limit (float): Maximum ring size to search for.

    Returns:
        List[List[int]]: A list of rings, where each ring is a list of atom indices.
    '''
    s = ats.repeat(repeat)
    pos = s.get_positions()
    nat = len(s)
    lat = s.get_cell()
    els = s.get_chemical_symbols()
    radii = covalent_radii[symbols2numbers(els)]

    if bonds is not None:
        # Don't need to find neighbors for elements not included in bonds
        elements = set().union(*bonds)
        radii = [x if el in elements else 0. for el, x in zip(els, radii)]
        radii = np.array(radii, dtype=float)

    nl = NeighborList(radii * radii_factor, self_interaction=False, bothways=False, skin=0.)
    nl.update(s)

    d_idx = []
    d_val = []
    # unit cell offsets for all atoms
    all_offsets = {}

    for i in range(nat):
        indices, offsets = nl.get_neighbors(i)

        rs = pos[indices, :] + offsets @ lat - pos[i, :]
        ds = np.linalg.norm(rs, axis=1)
        for j, r, o in zip(indices, ds, offsets):
            # Ignore bonds that are not included
            if ((els[i], els[j]) in bonds or (els[j], els[i]) in bonds):
                d_idx.append((i, j))
                d_val.append(r)
                d_idx.append((j, i))
                d_val.append(r)
                all_offsets[(i, j)] = o
                all_offsets[(j, i)] = -o


    # sparse matrix of bonds, removes zero entries
    d = csr_array((d_val, np.array(d_idx, dtype=np.int32).T), shape=(nat, nat))

    # now find the rings 
    rings = {}
    for i in range(len(ats)):
        indices, offsets = nl.get_neighbors(i)
        for j, _ in zip(indices, offsets):
            if bonds is not None: 
                # skip if bond is not allowed
                if not ((els[i], els[j]) in bonds or (els[j], els[i]) in bonds):
                    continue
            # Remove the bond between the two selected neighbors
            d_tmp = d.copy()
            d_tmp[i, j] = 0
            d_tmp[j, i] = 0
            d_tmp.eliminate_zeros()
            # Now find the shortest path between i and j
            dist_matrix, predecessors = dijkstra(d_tmp, indices=i, return_predecessors=True, directed=False, unweighted=True, limit=limit)
            if dist_matrix[j] < np.inf:
                k = j
                ring = [k]
                while predecessors[k] != i:
                    k = predecessors[k]
                    ring.append(k)
                ring.append(i)

                if not check_ring_is_periodic(ring, all_offsets):
                    print('WARNING: ring is wrapping around periodic cell! Consider increasing `repeat`.')
                    continue

                ring = [x % len(ats) for x in ring]  # take it back to primary cell
                rings[tuple(sorted(ring))] = ring


    return list(rings.values())