`ovito.data`

This Python module defines various data object types, which are produced and processed within OVITO’s data pipeline system. It also provides the DataCollection class as a container for such data objects as well as several utility classes for computing neighbor lists and iterating over the bonds of connected to a particle.

Data containers:

DataObject (base of all data object types in OVITO)

DataCollection (a general container for data objects representing an entire dataset)

PropertyContainer (manages a set of uniform Property arrays)

Particles (a specialized PropertyContainer for particles)

Bonds (a specialized PropertyContainer for bonds)

VoxelGrid (a specialized PropertyContainer for 2d and 3d volumetric grids)

DataTable (a specialized PropertyContainer for tabulated data)

TrajectoryLines (a set of particle trajectory lines)

Data objects:

Property (holds per-data-element property values)

SimulationCell (simulation box geometry and boundary conditions)

SurfaceMesh (polyhedral mesh representing the boundaries of spatial regions)

TriangleMesh (general mesh structure made of vertices and triangular faces)

DislocationNetwork (set of discrete dislocation lines with Burgers vector information)

Auxiliary data objects:

ElementType (base class for element type definitions)

ParticleType (describes a single particle or atom type)

BondType (describes a single bond type)

DislocationSegment (a dislocation line in a DislocationNetwork)

Utility classes:

CutoffNeighborFinder (finds neighboring particles within a cutoff distance)

NearestNeighborFinder (finds N nearest neighbor particles)

BondsEnumerator (lets you efficiently iterate over the bonds connected to a particle)

class ovito.data.BondType

Base: ovito.data.ElementType

Represents a bond type. This class inherits all its fields from the ElementType base class.

You can enumerate the list of defined bond types by accessing the bond_types bond property object:

bond_type_list = data.particles.bonds.bond_types.types
for bond_type in bond_type_list:
    print(bond_type.id, bond_type.name, bond_type.color)

class ovito.data.Bonds

Base: ovito.data.PropertyContainer

Stores the list of bonds and their properties. A Bonds object is always part of a parent Particles object. You can access it as follows:

data = pipeline.compute()
print("Number of bonds:", data.particles.bonds.count)

The Bonds class inherits the count attribute from its PropertyContainer base class. This attribute returns the number of bonds.

Bond properties

Bonds can be associated with arbitrary bond properties, which are managed in the Bonds container as a set of Property data arrays. Each bond property has a unique name by which it can be looked up:

print("Bond property names:")
print(data.particles.bonds.keys())
if 'Length' in data.particles.bonds:
    length_prop = data.particles.bonds['Length']
    assert(len(length_prop) == data.particles.bonds.count)

New bond properties can be added using the PropertyContainer.create_property() method.

Bond topology

The Topology bond property, which is always present, defines the connectivity between particles in the form of a N x 2 array of indices into the Particles array. In other words, each bond is defined by a pair of particle indices.

for a,b in data.particles.bonds.topology:
    print("Bond from particle %i to particle %i" % (a,b))

Note that the bonds of a system are not stored in any particular order. If you need to enumerate all bonds connected to a certain particle, you can use the BondsEnumerator utility class for that.

Bonds visualization

The Bonds data object has a BondsVis element attached to it, which controls the visual appearance of the bonds in rendered images. It can be accessed through the vis attribute:

data.particles.bonds.vis.enabled = True
data.particles.bonds.vis.flat_shading = True
data.particles.bonds.vis.width = 0.3

Computing bond vectors

Since each bond is defined by two indices into the particles array, we can use these indices to determine the corresponding spatial bond vectors connecting the particles. They can be computed from the positions of the particles:

topology = data.particles.bonds.topology
positions = data.particles.positions
bond_vectors = positions[topology[:,1]] - positions[topology[:,0]]

Here, the first and the second column of the bonds topology array are used to index into the particle positions array. The subtraction of the two indexed arrays yields the list of bond vectors. Each vector in this list points from the first particle to the second particle of the corresponding bond.

Finally, we may have to correct for the effect of periodic boundary conditions when a bond connects two particles on opposite sides of the box. OVITO keeps track of such cases by means of the the special Periodic Image bond property. It stores a shift vector for each bond, specifying the directions in which the bond crosses periodic boundaries. We make use of this information to correct the bond vectors computed above. This is done by adding the product of the cell matrix and the shift vectors from the Periodic Image bond property:

bond_vectors += numpy.dot(data.cell[:3,:3], data.particles.bonds.pbc_vectors.T).T

The shift vectors array is transposed here to facilitate the transformation of the entire array of vectors with a single 3x3 cell matrix. To summarize: In the two code snippets above we have performed the following calculation of the unwrapped vector \(\mathbf{v}\) for every bond (a, b) in parallel:

\(\mathbf{v} = \mathbf{x}_b - \mathbf{x}_a + \mathbf{H} \cdot (n_x, n_y, n_z)^{T}\),

with \(\mathbf{H}\) denoting the simulation cell matrix and \((n_x, n_y, n_z)\) the bond’s PBC shift vector.

Standard bond properties

The following standard properties are defined for bonds:

Property name	Python attribute name	Data type	Component names
Bond Type	`bond_types`	int
Color	`colors`	float	R, G, B
Length		float
Particle Identifiers		int64	A, B
Periodic Image	`pbc_vectors`	int	X, Y, Z
Selection	`selection`	int
Topology	`topology`	int64	1, 2
Transparency		float
Width		float

add_bond(a, b, type=None, pbcvec=None)

Creates a new bond between two particles a and b, both parameters being indices into the particles list.

Parameters

a (int) – Index of first particle connected by the new bond. Particle indices start at 0.
b (int) – Index of second particle connected by the new bond.
type (int) – Optional type ID to be assigned to the new bond. This value will be stored to the bond_types array.
pbcvec (tuple) – Three integers specifying the bond’s crossings of periodic cell boundaries. The information will be stored in the pbc_vectors array.

Returns

The index of the newly created bond, i.e. (Bonds.count-1).

The method does not check if there already is an existing bond connecting the same pair of particles.

The method does not check if the particle indices a and b do exist. Thus, it is your responsibility to ensure that both indices are in the range 0 to (Particles.count-1).

In case the SimulationCell has periodic boundary conditions enabled, and the two particles connected by the bond are located in different periodic images, make sure you provide the pbcvec argument. It is required so that OVITO does not draw the bond as a direct line from particle a to particle b but as a line passing through the periodic cell faces. You can use the Particles.delta_vector() function to compute pbcvec or use the pbc_shift vector returned by the CutoffNeighborFinder utility.

property bond_types: The Property data array for the Bond Type standard bond property; or None if that property is undefined.

property colors: The Property data array for the Color standard bond property; or None if that property is undefined.

property pbc_vectors: The Property data array for the Periodic Image standard bond property; or None if that property is undefined.

property selection: The Property data array for the Selection standard bond property; or None if that property is undefined.

property topology: The Property data array for the Topology standard bond property; or None if that property is undefined.

class ovito.data.BondsEnumerator(bonds: ovito.data.Bonds)

Utility class that permits efficient iteration over the bonds connected to specific particles.

The constructor takes a Bonds object as input. From the generally unordered list of bonds, the BondsEnumerator will build a lookup table for quick enumeration of bonds of particular particles.

All bonds connected to a specific particle can be subsequently visited using the bonds_of_particle() method.

Warning: Do not modify the underlying Bonds object while the BondsEnumerator is in use. Adding or deleting bonds would render the internal lookup table of the BondsEnumerator invalid.

Usage example

from ovito.io import import_file
from ovito.data import BondsEnumerator
from ovito.modifiers import ComputePropertyModifier

# Load a dataset containing atoms and bonds.
pipeline = import_file('input/bonds.data.gz', atom_style='bond')

# For demonstration purposes, lets here define a compute modifier that calculates the length 
# of each bond, storing the results in a new bond property named 'Length'.
pipeline.modifiers.append(ComputePropertyModifier(operate_on='bonds', output_property='Length', expressions=['BondLength']))

# Obtain pipeline results.
data = pipeline.compute()
positions = data.particles.positions  # array with atomic positions
bond_topology = data.particles.bonds.topology  # array with bond topology
bond_lengths = data.particles.bonds['Length']     # array with bond lengths

# Create bonds enumerator object.
bonds_enum = BondsEnumerator(data.particles.bonds)

# Loop over atoms.
for particle_index in range(data.particles.count):
    # Loop over bonds of current atom.
    for bond_index in bonds_enum.bonds_of_particle(particle_index):
        # Obtain the indices of the two particles connected by the bond:
        a = bond_topology[bond_index, 0]
        b = bond_topology[bond_index, 1]
        
        # Bond directions can be arbitrary (a->b or b->a):
        assert(a == particle_index or b == particle_index)
        
        # Obtain the length of the bond from the 'Length' bond property:
        length = bond_lengths[bond_index]

        print("Bond from atom %i to atom %i has length %f" % (a, b, length))

bonds_of_particle(index: int) → Iterator[int]: Returns an iterator yielding the indices of the bonds connected to the given particle. The indices can be used to index into the Property arrays of the Bonds object.

class ovito.data.CutoffNeighborFinder(cutoff, data_collection)

A utility class that computes particle neighbor lists.

This class lets you iterate over all neighbors of a particle that are located within a specified spherical cutoff. You can use it to build neighbors lists or perform computations that require neighbor vector information.

The constructor takes a positive cutoff radius and a DataCollection providing the input particles and the SimulationCell (needed for periodic systems).

Once the CutoffNeighborFinder has been constructed, you can call its find() method to iterate over the neighbors of a particle, for example:

from ovito.io import import_file
from ovito.data import CutoffNeighborFinder

# Load input simulation file.
pipeline = import_file("input/simulation.dump")
data = pipeline.compute()

# Initialize neighbor finder object:
cutoff = 3.5
finder = CutoffNeighborFinder(cutoff, data)

# Prefetch the property array containing the particle type information:
ptypes = data.particles.particle_types

# Loop over all particles:
for index in range(data.particles.count):
    print("Neighbors of particle %i:" % index)

    # Iterate over the neighbors of the current particle:
    for neigh in finder.find(index):
        print(neigh.index, neigh.distance, neigh.delta, neigh.pbc_shift)

        # The index can be used to access properties of the current neighbor, e.g.
        type_of_neighbor = ptypes[neigh.index]

Note: In case you rather want to determine the N nearest neighbors of a particle, use the NearestNeighborFinder class instead.

Parameters

cutoff (float) –
data_collection (DataCollection) –

find(index)

Returns an iterator over all neighbors of the given particle.

Parameters

index (int) – The zero-based index of the central particle whose neighbors should be enumerated.

Returns

A Python iterator that visits all neighbors of the central particle within the cutoff distance. For each neighbor the iterator returns an object with the following property fields:

index: The zero-based global index of the current neighbor particle.

distance: The distance of the current neighbor from the central particle.

distance_squared: The squared neighbor distance.

delta: The three-dimensional vector connecting the central particle with the current neighbor (taking into account periodicity).

pbc_shift: The periodic shift vector, which specifies how often each periodic boundary of the simulation cell is crossed when going from the central particle to the current neighbor.

The index value returned by the iterator can be used to look up properties of the neighbor particle as demonstrated in the example above.

Note that all periodic images of particles within the cutoff radius are visited. Thus, the same particle index may appear multiple times in the neighbor list of the central particle. In fact, the central particle may be among its own neighbors in a small periodic simulation cell. However, the computed vector (delta) and PBC shift (pbc_shift) will be unique for each visited image of the neighbor particle.

find_at(coords)

Returns an iterator over all particles located within the spherical range of the given center position. In contrast to find() this method can search for neighbors around arbitrary spatial locations, which don’t have to coincide with any physical particle position.

Parameters

coords – A (x,y,z) coordinate triplet specifying the center location around which to search for particles.

Returns

A Python iterator enumerating all particles within the cutoff distance. For each neighbor the iterator returns an object with the following properties:

index: The zero-based global index of the current neighbor particle.

distance: The distance of the current particle from the center position.

distance_squared: The squared distance.

delta: The three-dimensional vector from the center to the current neighbor (taking into account periodicity).

pbc_shift: The periodic shift vector, which specifies how often each periodic boundary of the simulation cell is crossed when going from the center point to the current neighbor.

The index value returned by the iterator can be used to look up properties of the neighbor particle as demonstrated in the example above.

Note that all periodic images of particles within the cutoff radius are visited. Thus, the same particle index may appear multiple times in the neighbor list. However, the computed vector (delta) and image offset (pbc_shift) will be unique for each visited image of a neighbor particle.

neighbor_distances(index)

Returns the list of distances between some central particle and all its neighbors within the cutoff range.

Parameters: index (int) – The 0-based index of the central particle whose neighbors should be enumerated.
Returns: NumPy array containing the radial distances to all neighbor particles within the cutoff range (in arbitrary order).

This method is equivalent to the following code, but performance is typically a lot better:

def neighbor_distances(index):
    distances = []
    for neigh in finder.find(index):
        distances.append(neigh.distance)
    return numpy.asarray(distances)

neighbor_vectors(index)

Returns the list of vectors from some central particle to all its neighbors within the cutoff range.

Parameters: index (int) – The 0-based index of the central particle whose neighbors should be enumerated.
Returns: Two-dimensional NumPy array containing the vectors to all neighbor particles within the cutoff range (in arbitrary order).

The method is equivalent to the following code, but performance is typically a lot better:

def neighbor_vectors(index):
    vecs = []
    for neigh in finder.find(index):
        vecs.append(neigh.delta)
    return numpy.asarray(vecs)

class ovito.data.DataCollection

Base: ovito.data.DataObject

A DataCollection is a container class holding together individual data objects, each representing different fragments of a dataset. For example, a dataset loaded from a simulation data file may consist of particles, the simulation cell information and additional auxiliary data such as the current timestep number of the snapshots, etc. All this information is contained in one DataCollection, which exposes the individual pieces of information as sub-objects, for example via the DataCollection.particles, DataCollection.cell and DataCollection.attributes fields.

Data collections are the elementary entities that get processed within a data Pipeline. Each modifier receives a data collection from the preceding modifier, alters it in some way, and passes it on to the next modifier. The output data collection of the last modifier in the pipeline is returned by the Pipeline.compute() method.

A data collection essentially consists of a bunch of DataObjects, which are all stored in the DataCollection.objects list. Typically, you don’t access the data objects through this list directly but rather use on of the special accessor fields provided by the DataCollection class, which give more convenient access to data objects of a particular kind. For example, the surfaces dictionary provides key-based access to all the SurfaceMesh instances currently in the data collection.

You can programmatically add or remove data objects from a data collection by manipulating its objects list. For instance, to populate a new data collection instance that is initially empty with a new SimulationCell object:

data = DataCollection()
cell = SimulationCell()
data.objects.append(cell)
assert(data.cell is cell)

apply(modifier, frame=None)

This method applies a Modifier function to the data stored in this collection to modify it in place.

Parameters

modifier (ovito.pipeline.Modifier) – The modifier object that should alter the contents of this data collection in place.
frame (int) – Optional animation frame number to be passed to the modifier function, which may use it for time-dependent modifications.

The method allows modifying a data collection with one of Ovito’s built-in modifiers directly without the need to build up a complete Pipeline first. In contrast to a data pipeline, the apply() method executes the modifier function immediately and alters the data in place. In other words, the original data in this DataCollection gets replaced by the output produced by the invoked modifier function. Note that it is possible to first create a copy of the original data using the clone() method if needed. The following code example demonstrates how to use the apply() method to successively modify a dataset:

from ovito.io import import_file
from ovito.modifiers import *

data = import_file("input/simulation.dump").compute()
data.apply(CoordinationAnalysisModifier(cutoff=2.9))
data.apply(ExpressionSelectionModifier(expression="Coordination<9"))
data.apply(DeleteSelectedModifier())

Note that it is typically possible to achieve the same result by first populating a Pipeline with the modifiers and then calling its compute() method at the very end:

pipeline = import_file("input/simulation.dump")
pipeline.modifiers.append(CoordinationAnalysisModifier(cutoff=2.9))
pipeline.modifiers.append(ExpressionSelectionModifier(expression="Coordination<9"))
pipeline.modifiers.append(DeleteSelectedModifier())
data = pipeline.compute()

Furthermore, it is possible to use the apply() in the implementation of a user-defined modifier function to invoke the built-in modifiers of OVITO as sub-routine:

# A user-defined modifier function that calls the built-in ColorCodingModifier
# as a sub-routine to assign a color to each atom based on some property
# created within the function itself:
def modify(frame, data):
    data.particles_.create_property('idx', data=numpy.arange(data.particles.count))
    data.apply(ColorCodingModifier(property='idx'), frame)

# Set up a data pipeline that uses the user-defined modifier function:
pipeline = import_file("input/simulation.dump")
pipeline.modifiers.append(modify)
data = pipeline.compute()

property attributes

This field contains a dictionary view with all the global attributes currently associated with this data collection. Global attributes are key-value pairs that represent small tokens of information, typically simple value types such as int, float or str. Every attribute has a unique identifier such as 'Timestep' or 'ConstructSurfaceMesh.surface_area'. This identifier serves as look-up key in the attributes dictionary. Attributes are dynamically generated by modifiers in a data pipeline or come from the data source. For example, if the input simulation file contains timestep information, the timestep number is made available by the FileSource as the 'Timestep' attribute. It can be retrieved from pipeline’s output data collection:

>>> pipeline = import_file('snapshot_140000.dump')
>>> pipeline.compute().attributes['Timestep']
140000

Some modifiers report their calculation results by adding new attributes to the data collection. See each modifier’s reference documentation for the list of attributes it generates. For example, the number of clusters identified by the ClusterAnalysisModifier is available in the pipeline output as an attribute named ClusterAnalysis.cluster_count:

pipeline.modifiers.append(ClusterAnalysisModifier(cutoff = 3.1))
data = pipeline.compute()
nclusters = data.attributes["ClusterAnalysis.cluster_count"]

The ovito.io.export_file() function can be used to output dynamically computed attributes to a text file, possibly as functions of time:

export_file(pipeline, "data.txt", "txt/attr",
    columns = ["Timestep", "ClusterAnalysis.cluster_count"],
    multiple_frames = True)

If you are writing your own modifier function, you let it add new attributes to a data collection. In the following example, the CommonNeighborAnalysisModifier first inserted into the pipeline generates the 'CommonNeighborAnalysis.counts.FCC' attribute to report the number of atoms that have an FCC-like coordination. To compute an atomic fraction from that, we need to divide the count by the total number of atoms in the system. To this end, we append a user-defined modifier function to the pipeline, which computes the fraction and outputs the value as a new attribute named 'fcc_fraction'.

pipeline.modifiers.append(CommonNeighborAnalysisModifier())
            
def compute_fcc_fraction(frame, data):
    n_fcc = data.attributes['CommonNeighborAnalysis.counts.FCC']
    data.attributes['fcc_fraction'] = n_fcc / data.particles.count

pipeline.modifiers.append(compute_fcc_fraction)
print(pipeline.compute().attributes['fcc_fraction'])

property cell

Returns the SimulationCell data object describing the cell vectors and periodic boundary condition flags. It may be None.

Important

The SimulationCell data object returned by this attribute may be marked as read-only, which means your attempts to modify the cell object will raise a Python error. This is typically the case if the data collection was produced by a pipeline and its objects are owned by the system.

If you intend to modify the SimulationCell data object within this data collection, use the cell_ attribute instead to explicitly request a mutable version of the cell object. See topic Announcing object modification for more information. Use cell for read access and cell_ for write access, e.g.

print(data.cell.volume)
data.cell_.pbc = (True,True,False)

To create a SimulationCell in a data collection that might not have a simulation cell yet, use the create_cell() method or simply assign a new instance of the SimulationCell class to the cell attribute.

clone()

Returns a copy of this DataCollection containing the same data objects as the original.

The method may be used to retain a copy of the original data before modifying a data collection in place, for example using the apply() method:

original = data.clone()
data.apply(ExpressionSelectionModifier(expression="Position.Z < 0"))
data.apply(DeleteSelectedModifier())
print("Number of atoms before:", original.particles.count)
print("Number of atoms after:", data.particles.count)

Note that the clone() method performs an inexpensive, shallow copy, meaning that the newly created collection will still share the data objects with the original collection. Data objects that are shared by two or more data collections are protected against modification by default to avoid unwanted side effects. Thus, in order to subsequently modify the data objects in either the original collection or its copy, you will have to use the underscore notation or the DataObject.make_mutable() method to explicitly request a deep copy of the particular data object(s) you want to modify. For example:

copy = data.clone()
# Data objects are shared by original and copy:
assert(copy.cell is data.cell)

# In order to modify the SimulationCell in the dataset copy, we must request
# a mutable version of the SimulationCell using the 'cell_' accessor:
copy.cell_.pbc = (False, False, False)

# As a result, the cell object in the second data collection has been replaced
# with a deep copy and the two data collections no longer share the same
# simulation cell object:
assert(copy.cell is not data.cell)

create_cell(matrix, pbc=(True, True, True), vis_params=None)

This convenience method conditionally creates a new SimulationCell object and stores it in this data collection. If a simulation cell already existed in the collection (cell is not None), then that cell object is replaced with a modifiable copy if necessary and the matrix and PBC flags are set to the given values. The attached SimulationCellVis element is maintained in this case.

Parameters

matrix – A 3x4 array to initialize the cell matrix with. It specifies the three cell vectors and the origin.
pbc – A tuple of three Booleans specifying the cell’s pbc flags.
vis_params (Mapping[str, Any]) – Optional dictionary to initialize attributes of the attached SimulationCellVis element (only used if the cell object is newly created by the method).

Return type

ovito.data.SimulationCell

The logic of this method is roughly equivalent to the following code:

def create_cell(data: DataCollection, matrix, pbc, vis_params=None) -> SimulationCell:
    if data.cell is None:
        data.cell = SimulationCell(pbc=pbc)
        data.cell[...] = matrix
        data.cell.vis.line_width = <...> # Some value that scales with the cell's size
        if vis_params:
            for name, value in vis_params.items(): setattr(data.cell.vis, name, value)
    else:
        data.cell_[...] = matrix
        data.cell_.pbc = pbc
    return data.cell_

create_particles(vis_params=None, **params)

This convenience method conditionally creates a new Particles container object and stores it in this data collection. If the data collection already contains an existing particles object (particles is not None), then that particles object is replaced with a modifiable copy if necessary. The associated ParticlesVis element is preserved.

Parameters

params – Key/value pairs passed to the method as keyword arguments are used to set attributes of the Particles object (even if the particles object already existed).
vis_params (Mapping[str, Any]) – Optional dictionary to initialize attributes of the attached ParticlesVis element (only used if the particles object is newly created by the method).

Return type

ovito.data.Particles

The logic of this method is roughly equivalent to the following code:

def create_particles(data: DataCollection, vis_params=None, **params) -> Particles:
    if data.particles is None:
        data.particles = Particles()
        if vis_params:
            for name, value in vis_params.items(): setattr(data.particles.vis, name, value)
    for name, value in params.items(): setattr(data.particles_, name, value)
    return data.particles_

Usage example:

coords = [(-0.06,  1.83,  0.81), # xyz coordinates of the 3 particle system to create
          ( 1.79, -0.88, -0.11),
          (-1.73, -0.77, -0.61)]
particles = data.create_particles(count=len(coords), vis_params={'radius': 1.4})
particles.create_property('Position', data=coords)

property dislocations: Returns the DislocationNetwork data object; or None if there is no object of this type in the collection. Typically, the DislocationNetwork is created by a pipeline containing the DislocationAnalysisModifier.

property grids

Returns a dictionary view providing key-based access to all VoxelGrids in this data collection. Each VoxelGrid has a unique identifier key, which allows you to look it up in this dictionary. To find out which voxel grids exist in the data collection and what their identifiers are, use

print(data.grids)

Then retrieve the desired VoxelGrid from the collection using its identifier key, e.g.

charge_density_grid = data.grids['charge-density']
print(charge_density_grid.shape)

property objects

The flat list of all top-level DataObjects currently stored in this data collection. You can add or remove data objects in this mutable list as needed.

Typically you don’t have to work with this list directly, because the DataCollection class provides several convenience accessor attributes for the different flavors of data objects. For example, the DataCollection.particles field returns the Particles object from this data objects list. Dictionary views such as DataCollection.tables and DataCollection.surfaces provide key-based access to a particular class of data objects from this list.

property particles

Returns the Particles object, which manages all per-particle properties. It may be None if the data collection contains no particle model at all.

Important

The Particles data object returned by this attribute may be marked as read-only, which means attempts to modify its contents will raise a Python error. This is typically the case if the data collection was produced by a pipeline and all data objects are owned by the system.

If you intend to modify the contents of the Particles object in some way, use the particles_ attribute instead to explicitly request a mutable version of the particles object. See topic Announcing object modification for more information. Use particles for read access and particles_ for write access, e.g.

print(data.particles.positions[0])
data.particles_.positions_[0] += (0.0, 0.0, 2.0)

To create a new Particles object in a data collection that might not have particles yet, use the create_particles() method or simply assign a new instance of the Particles class to the particles attribute.

property surfaces

Returns a dictionary view providing key-based access to all SurfaceMesh objects in this data collection. Each SurfaceMesh has a unique identifier key, which can be used to look it up in the dictionary. See the documentation of the modifier producing the surface mesh to find out what the right key is, or use

print(data.surfaces)

to see which identifier keys exist. Then retrieve the desired SurfaceMesh object from the collection using its identifier key, e.g.

surface = data.surfaces['surface']
print(surface.get_vertices())

property tables

A dictionary view of all DataTable objects in this data collection. Each DataTable has a unique identifier key, which can be used to look it up in this dictionary. You can use

print(data.tables)

to find out which table identifiers exist in the dictionary. Modifiers that generate a data table typically assign a predefined identifier, which can be found in their documentation. Use the key string to retrieve the desired DataTable from the dictionary, e.g.

rdf = data.tables['coordination-rdf']
print(rdf.xy())

property trajectories: Returns the TrajectoryLines object, which holds the continuous particle trajectories traced by the GenerateTrajectoryLinesModifier. None is returned if the data collection does not contain a TrajectoryLines object.

property triangle_meshes: This is a dictionary view providing key-based access to all TriangleMesh objects currently stored in this data collection. Each TriangleMesh has a unique identifier key, which can be used to look it up in the dictionary.

class ovito.data.DataObject

Abstract base class for all data object types in OVITO.

A DataObject represents a fragment of data processed in or by a data pipeline. See the ovito.data module for a list of different concrete data object types in OVITO. Data objects are typically contained in a DataCollection, which represents a whole data set. Furthermore, data objects can be nested into hierarchy. For example, the Bonds data object is part of the parent Particles data object.

Data objects by themselves are non-visual objects. Visualizing the information stored in a data object in images is the responsibility of so-called visual elements. A data object may be associated with a DataVis element by assigning it to the data object’s vis field. Each type of visual element exposes a set of parameters that allow you to configure the appearance of the data visualization in rendered images and animations.

See also

To find all neighbors within a spherical cutoff region around another particle, use CutoffNeighborFinder instead.

The constructor takes the requested number of nearest neighbors, N, and a DataCollection containing the input particles and the optional simulation cell. N must be a positive integer not greater than 64, which is the built-in maximum of neighbors supported by this class.

Note

Keep in mind that, if the system contains only N particles or less, and if the simulation does not use periodic boundary conditions, then the neighbor finder will return less than the requested number of nearest neighbors.

Once the NearestNeighborFinder has been initialized, you can call its find() method to iterate over the sorted list of nearest neighbors of a given central particle:

# Set up a neighbor finder for visiting the 12 closest neighbors of each particle.
finder = NearestNeighborFinder(12, data)

# Loop over all input particles:
for index in range(data.particles.count):
    print("Nearest neighbors of particle %i:" % index)
    # Iterate over the neighbors of the current particle, starting with the closest:
    for neigh in finder.find(index):
        print(neigh.index, neigh.distance, neigh.delta)
        # The index can be used to access properties of the current neighbor, e.g.
        type_of_neighbor = data.particles.particle_types[neigh.index]

In addition, the class provides the find_at() method, which determines the N nearest particles around some arbitrary spatial location:

# Visit particles closest to some spatial point (x,y,z):
xyz_coords = (0.0, 0.0, 0.0)
for neigh in finder.find_at(xyz_coords):
    print(neigh.index, neigh.distance, neigh.delta)

find(index)

Returns an iterator that visits the N nearest neighbors of the given particle in order of ascending distance.

Parameters

index (int) – The zero-based index of the central particle whose neighbors should be determined.

Returns

A Python iterator that visits the N nearest neighbors of the central particle in order of ascending distance. For each neighbor being visited, the iterator returns an object having the following attributes:

index: The global index of the current neighbor particle.

distance: The distance of the current neighbor from the central particle.

distance_squared: The squared neighbor distance.

delta: The three-dimensional vector connecting the central particle with the current neighbor (correctly taking into account periodic boundary conditions).

The index can be used to look up properties of the neighbor particle as demonstrated in the first example code above.

Note that several periodic images of the same particle may be visited if the periodic simulation cell is sufficiently small. Then the same particle index will appear more than once in the neighbor list. In fact, the central particle may be among its own neighbors in a sufficiently small periodic simulation cell. However, the computed neighbor vector (delta) will be unique for each image of a neighboring particle.

The number of neighbors actually visited may be smaller than the requested number, N, if the system contains too few particles and is non-periodic.

Note that the find() method will not find other particles located exactly at the same spatial position as the central particle for technical reasons. To find such particles too, which are positioned exactly on top of each other, use find_at() instead.

find_all(indices=None)

Finds the N nearest neighbors of each particle in the system or of the subset of particles specified by indices. This is the batch-processing version of find(), allowing you to efficiently compute the neighbor lists and neighbor vectors of several particles at once, without explicit for-loop and by making use of all parallel processor cores.

The method returns two NumPy arrays:

neigh_idx : NumPy array of shape (M, N) storing the indices of neighbor particles, with M equal to len(indices) or, if indices is None, the total number of particles in the system. N refers to the number of nearest neighbors requested in the NearestNeighborFinder constructor. The computed indices in this array can be used to look up properties of neighbor particles in the global Particles object.

neigh_vec : NumPy array of shape (M, N, 3) storing the xyz components of the three-dimensional neighbor vectors (“delta”), which connect the M central particles with their N respective nearest neighbors.

Parameters: indices – List of zero-based particle indices for which the neighbor lists should be computed. If left unspecified, neighbor lists will be computed for every particle in the system.
Returns: (neigh_idx, neigh_vec)

Tip

To compute all pair-wise distances in one go, i.e. the 2-norms of the neighbor vectors, you can do:

distances = numpy.linalg.norm(neigh_vec, axis=2)   # Yields (M,N) array of neighbor distances

find_at(coords)

Returns an iterator that visits the N nearest particles around a spatial point given by coords in order of ascending distance. Unlike the find() method, which queries the nearest neighbors of a physical particle, find_at() allows searching for nearby particles at arbitrary locations in space.

Parameters

coords – A coordinate triplet (x,y,z) specifying the spatial location where the N nearest particles should be queried.

Returns

A Python iterator that visits the N nearest neighbors in order of ascending distance. For each visited particle the iterator returns an object with the following attributes:

index: The index of the current particle (starting at 0).

distance: The distance of the current neighbor from the query location.

distance_squared: The squared distance to the query location.

delta: The three-dimensional vector from the query point to the current particle (correctly taking into account periodic boundary conditions).

If there is a particle located exactly at the query location coords, it will be among the returned neighbors. This is in contrast to the find() function, which skips the central particle itself.

The number of neighbors actually visited may be smaller than the requested number, N, if the system contains too few particles and is non-periodic.

class ovito.data.ParticleType

Base: ovito.data.ElementType

This data object describes one particle or atom type. In atomistic simulations, each chemical element is typically represented by an instance of the ParticleType class. The property fields of the class control how the particles of that type get visualized in terms of e.g. color, particle radius, shape, etc.

The ParticleType class inherits several general data fields from its base class ElementType, e.g. the color, name and id fields. It adds specific fields for particles: radius and shape. Furthermore, the class has additional fields controlling the visual appearance of particles with user-defined shapes.

The ParticleType instances are all stored in the Property object with the name 'Particle Type', which also stores for each particle what its type is. The association between particles and particle types is established via a unique numeric type id. The following code shows how to iterate over all particle types in a dataset, which are listed in the Property.types field of the particle_types property:

# Access the property with the name 'Particle Type':
prop = data.particles.particle_types

# Print list of particle types (their numeric IDs and names)
for t in prop.types: print(f'ID {t.id} -> {t.name}')

# Print the numeric type ID of each particle:
for tid in prop[...]: print(tid)

The order in which the particle types are stored in the Property.types list is arbitrary, and the unique numeric IDs of particle types have no specific meaning in general. A common operation is to find the ParticleType in the list corresponding to a given numeric ID. For this look up operation, the Property class provides the type_by_id() method:

# Look up the particle type with unique ID 2:
t = prop.type_by_id(2)
print(t.name, t.color, t.radius)

# Iterate over all particles and print their type's name:
for tid in prop[...]:
    print(prop.type_by_id(tid).name)

Another common operation is to look up a particle type by name, for example the type representing a certain chemical element. For this kind of look up operation, the type_by_name() method may be used, which assumes that each type has a unique name (which may not always be true):

# Print numeric ID of particle type 'Si':
print(prop.type_by_name('Si').id)

property backface_culling

Activates back-face culling for the user-defined particle shape mesh to speed up rendering. If turned on, polygonal sides of the shape mesh facing away from the viewer will not be rendered. You can turn this option off if the particle’s shape is not closed and two-sided rendering is required. This option only has an effect if a user-defined shape has been assigned to the particle type using the load_shape() method.

Default: True

property highlight_edges

Activates the highlighting of the polygonal edges of the user-defined particle shape during rendering. This option only has an effect if a user-defined shape has been assigned to the particle type using the load_shape() method.

Default: False

load_defaults(): Given the type’s chemical name, which must have been set before calling this method, initializes the type’s color and radius fields with default values from OVITO’s internal database of chemical elements. This method is useful when creating new atom types while building up a molecule structure.

load_shape(filepath: str)

Assigns a user-defined shape to the particle type. Particles of this type will subsequently be rendered using the polyhedral mesh loaded from the given file. The method will automatically detect the format of the geometry file and supports standard file formats such as OBJ, STL and VTK that contain triangle meshes, see this table.

The shape loaded from the geometry file will be scaled with the radius value set for this particle type or the per-particle value stored in the Radius particle property if present. The shape of each particle will be rendered such that its origin is located at the coordinates of the particle.

The following example script demonstrates how to load a user-defined shape for the first particle type (index 0) loaded from a LAMMPS dump file, which can be accessed through the Property.types list of the Particle Type particle property.

pipeline = import_file("input/simulation.dump")
pipeline.add_to_scene()

types = pipeline.source.data.particles_.particle_types_
types.type_by_id_(1).load_shape("input/tetrahedron.vtk")
types.type_by_id_(1).highlight_edges = True

property mass

The mass of this particle type.

Default: 0.0

property radius

This property controls the display radius of the particles of this type.

When set to zero, particles of this type will be rendered using the standard size specified by the ParticlesVis.radius parameter. Furthermore, precedence is given to any per-particle sizes assigned to the Radius particle property if that property has been defined.

Default: 0.0

The following example script demonstrates how to set the display radii of two particle types loaded from a simulation file, which can be accessed through the Property.types list of the Particle Type particle property.

pipeline = import_file("input/simulation.dump")
pipeline.add_to_scene()

def setup_particle_types(frame, data):
    types = data.particles_.particle_types_
    types.type_by_id_(1).name = "Cu"
    types.type_by_id_(1).radius = 1.35
    types.type_by_id_(2).name = "Zr"
    types.type_by_id_(2).radius = 1.55
pipeline.modifiers.append(setup_particle_types)

property shape

Selects the geometric shape used when rendering particles of this type. Supported modes are:

ParticlesVis.Shape.Unspecified (default)

ParticlesVis.Shape.Sphere

ParticlesVis.Shape.Box

ParticlesVis.Shape.Circle

ParticlesVis.Shape.Square

ParticlesVis.Shape.Cylinder

ParticlesVis.Shape.Spherocylinder

ParticlesVis.Shape.Mesh

By default, the standard particle shape that is set in the ParticlesVis visual element is used to render particles of this type. Parameter values other than Unspecified allow you to control the rendering shape on a per-type basis. Mode Sphere includes ellipsoid and superquadric particle shapes, which are enabled by the presence of the Aspherical Shape and Superquadric Roundness particle properties.

The load_shape() method lets you specify a user-defined mesh geometry for this particle type. Calling this method automatically switches the shape parameter to mode Mesh.

Setting the shapes of particle types permanently, i.e., for all frames of a loaded simulation trajectory, typically requires a user-defined modifier function. This function is inserted into the Pipeline to make the necessary changes to the ParticleType objects associated with the Property named Particle Type:

from ovito.io import import_file
from ovito.vis import *

# Load a simulation file containing numeric particle types 1, 2, 3, ...
pipeline = import_file("input/nylon.data")
pipeline.add_to_scene()

# Set the default particle shape in the ParticlesVis visual element, 
# which will be used by all particle types for which we do not specify a different shape below.
pipeline.compute().particles.vis.shape = ParticlesVis.Shape.Box
pipeline.compute().particles.vis.radius = 1.0

# A user-defined modifier function that configures the shapes of particle types 1 and 2:
def setup_particle_types(frame, data): 
    # Write access to property 'Particle Type':
    types = data.particles_.particle_types_  
    # Write access to numeric ParticleTypes, which are sub-objects of the Property object:
    types.type_by_id_(1).radius = 0.5
    types.type_by_id_(1).shape = ParticlesVis.Shape.Cylinder
    types.type_by_id_(2).radius = 1.2
    types.type_by_id_(2).shape = ParticlesVis.Shape.Sphere
pipeline.modifiers.append(setup_particle_types)

# Render a picture of the 3d scene:
vp = Viewport(camera_dir = (-2,1,-1))
vp.zoom_all()
vp.render_image(filename='output/particles.png', size=(320,240), renderer=TachyonRenderer())

property use_mesh_color

Use the intrinsic mesh color(s) instead of the particle color when rendering particles of this type. This option only has an effect if a user-defined shape has been assigned to the particle type using the load_shape() method.

Default: False

property vdw_radius

The van der Waals radius of the particle type. This value is used by the CreateBondsModifier to decide which pairs of particles are close enough to be connected by a bond. In contrast to the radius parameter, the van der Waals radius does not affect the visual appearance of the particles of this type.

Default: 0.0

class ovito.data.Particles

Base: ovito.data.PropertyContainer

This container object stores the information associated with a system of particles. It is typically accessed through the DataCollection.particles field of a data collection. The current number of particles is given by the count attribute that is inherited from the PropertyContainer base class. The particles may be associated with a set of properties. Each property is represented by a Property data object, that is stored in this property container and is basically an array of numeric values of length N, where N is the number of particles in the system. Each property array has a unique name, by which it can be looked up through the dictionary interface of the PropertyContainer base class. While the user is free to define arbitrary particle properties, OVITO predefines a set of standard properties that each have a fixed data layout, meaning and name. They are listed in the table below.

Standard property name	Data type	Component names
Angular Momentum	float	X, Y, Z
Angular Velocity	float	X, Y, Z
Aspherical Shape	float	X, Y, Z
Centrosymmetry	float
Charge	float
Cluster	int64
Color	float	R, G, B
Coordination	int
Deformation Gradient	float	XX, YX, ZX, XY, YY, ZY, XZ, YZ, ZZ
Dipole Magnitude	float
Dipole Orientation	float	X, Y, Z
Displacement Magnitude	float
Displacement	float	X, Y, Z
DNA Strand	int
Elastic Deformation Gradient	float	XX, YX, ZX, XY, YY, ZY, XZ, YZ, ZZ
Elastic Strain	float	XX, YY, ZZ, XY, XZ, YZ
Force	float	X, Y, Z
Kinetic Energy	float
Mass	float
Molecule Identifier	int64
Molecule Type	int
Nucleobase	int
Nucleotide Axis	float	X, Y, Z
Nucleotide Normal	float	X, Y, Z
Orientation	float	X, Y, Z, W
Particle Identifier	int64
Particle Type	int
Periodic Image	int	X, Y, Z
Position	float	X, Y, Z
Potential Energy	float
Radius	float
Rotation	float	X, Y, Z, W
Selection	int
Spin	float
Strain Tensor	float	XX, YY, ZZ, XY, XZ, YZ
Stress Tensor	float	XX, YY, ZZ, XY, XZ, YZ
Stretch Tensor	float	XX, YY, ZZ, XY, XZ, YZ
Structure Type	int
Superquadric Roundness	float	Phi, Theta
Torque	float	X, Y, Z
Total Energy	float
Transparency	float
Vector Color	float	R, G, B
Velocity Magnitude	float
Velocity	float	X, Y, Z

add_particle(position)

Adds a new particle to the model. The particle count will be incremented by one. The method assigns position to the Position property of the new particle. The values of all other properties are initialized to zero.

Parameters: position (array-like) – The xyz coordinates for the new particle.
Returns: The index of the newly created particle, i.e. (Particles.count-1).

property angles: A PropertyContainer storing the list of angles defined for the molecular model (may be None).

property bonds: The Bonds data object storing the list of bonds and their properties (may be None).

property colors: The Property data array for the Color standard particle property; or None if that property is undefined.

create_bonds(vis_params=None, **params)

This convenience method conditionally creates and associates a Bonds object with this Particles parent object. If there is already an existing bonds object (bonds is not None), then that bonds object is replaced with a modifiable copy if necessary. The attached BondsVis element is preserved.

Parameters

params – Key/value pairs passed to the method as keyword arguments are used to set attributes of the Bonds object (even if the bonds object already existed).
vis_params (Mapping[str, Any]) – Optional dictionary to initialize attributes of the attached BondsVis element (only used if the bonds object is newly created by the method).

Return type

ovito.data.Bonds

The logic of this method is roughly equivalent to the following code:

def create_bonds(particles: Particles, vis_params=None, **params) -> Bonds:
    if particles.bonds is None:
        particles.bonds = Bonds()
        if vis_params:
            for name, value in vis_params.items(): setattr(particles.bonds.vis, name, value)
    for name, value in params.items(): setattr(particles.bonds_, name, value)
    return particles.bonds_

Usage example:

pairs = [(0, 1), (1, 2), (2, 0)] # Pairs of particle indices to connect by bonds
bonds = data.particles_.create_bonds(count=len(pairs), vis_params={'width': 0.6})
bonds.create_property('Topology', data=pairs)

delta_vector(a, b, cell, return_pbcvec=False)

Computes the vector connecting two particles a and b in a periodic simulation cell by applying the minimum image convention.

This is a convenience wrapper for the SimulationCell.delta_vector() method, which computes the vector between two arbitrary spatial locations \(r_a\) and \(r_b\) taking into account periodic boundary conditions. The version of the method described here takes two particle indices a and b as input, computing the shortest vector \({\Delta} = (r_b - r_a)\) between them using the minimum image convention. Please see the SimulationCell.delta_vector() method for further information.

Parameters

a – Zero-based index of the first input particle. This may also be an array of particle indices.
b – Zero-based index of the second input particle. This may also be an array of particle indices with the same length as a.
cell (SimulationCell) – The periodic domain. Typically, DataCollection.cell is used as argument here.
return_pbcvec (bool) – If True, also returns the vector \(n\), which specifies how often the computed particle-to-particle vector crosses the cell’s face.

Returns

The delta vector and, optionally, the vector \(n\).

property dihedrals: A PropertyContainer storing the list of dihedrals defined for the molecular model (may be None).

property forces: The Property data array for the Force standard particle property; or None if that property is undefined.

property identifiers: The Property data array for the Particle Identifier standard particle property; or None if that property is undefined.

property impropers: A PropertyContainer storing the list of impropers defined for the molecular model (may be None).

property masses: The Property data array for the Mass standard particle property; or None if that property is undefined.

property orientations: The Property data array for the Orientation standard particle property; or None if that property is undefined.

property particle_types: The Property data array for the Particle Type standard particle property; or None if that property is undefined.

property positions: The Property data array for the Position standard particle property; or None if that property is undefined.

remap_indices(particles: Particles, indices: Sequence[int] = None) → numpy.ndarray | slice

In case the storage order of atoms or particles changes during the course of a simulation, this method can determine the mapping of particles from one snapshot of the trajectory to another. It uses the unique identifiers of the particles to do that.

Given two data collections A and B containing the same set of particles but in different order, remap_indices() determines for each particle in B the zero-based index at which the same particle is found in A. For instance:

>>> A = pipeline.compute(frame=0)
>>> B = pipeline.compute(frame=1)
>>> A.particles.identifiers[...]
[8 101  5 30 99]
>>> B.particles.identifiers[...]
[5 101 30 99  8]
>>> A.particles.remap_indices(B.particles)
[2 1 3 4 0]

The index mapping generated by remap_indices() allows you to retrieve property values of particles in A in the same order in which they appear in B, making it easy to perform computations involving property values at different trajectory timesteps, e.g.:

mapping = A.particles.remap_indices(B.particles)
displacements = B.particles.positions - A.particles.positions[mapping]

remap_indices() compares the unique IDs stored in the Particle Identifier property arrays of both snapshots to compute the index permutation map. If this property is not defined, which may be the case if the imported trajectory file did not contain atom IDs, the remap_indices() method simply assumes that both snapshots use the same constant storage order and returns the identity mapping - as a Python slice object for optimal performance when being used for NumPy indexing. A slice object is also returned in case the ordering of particle IDs turns out to be the same in both snapshots and no remapping is necessary.

Note

An error will be raised if particles with duplicate IDs occur in snapshot A - but it is okay if B contains duplicate IDs. Furthermore, it is not an error if A contains additional particles that are not present in B - as long as all particles from B are found in A.

The default behavior of the method is to look up all particles of B in A. But the index mapping can also be established just for a subset of particles from B by supplying the optional parameter indices. The method expects an array of zero-based indices specifying which particles from snapshot B should be looked up in snapshot A. The returned mapping will have the same length as indices. Example:

# The numeric ID of atom type 'H':
hydrogen_type = B.particles.particle_types.type_by_name('H').id
# Determine the indices of all H atoms in data collection B:
hydrogen_indices = numpy.flatnonzero(B.particles.particle_types == hydrogen_type)

# Determine the corresponding indices of the same atoms in data collection A:
mapping = A.particles.remap_indices(B.particles, hydrogen_indices)

# In snapshot A the same particles are all H atoms too:
assert numpy.all(A.particles.particle_types[mapping] == hydrogen_type)

property selection: The Property data array for the Selection standard particle property; or None if that property is undefined.

property structure_types: The Property data array for the Structure Type standard particle property; or None if that property is undefined.

property velocities: The Property data array for the Velocity standard particle property; or None if that property is undefined.

class ovito.data.Property

Base: ovito.data.DataObject

Stores the property values for an array of data elements (e.g. particles, bonds or voxels).

Each property, for example a particle property, is represented by one Property object storing the property values for all particles. Thus, a Property object is basically an array of values whose length matches the number of data elements.

All Property objects belonging to the same class of data elements, for example all particle properties, are managed by a PropertyContainer. In the case of particle properties, the corresponding container class is the Particles class, which is a specialization of the generic PropertyContainer base class.

Data access

A Property object behaves like a Numpy array. For example, you can access the property value for the i-th data element using indexing:

positions = data.particles['Position']
print('Position of first particle:', positions[0])
print('Z-coordinate of second particle:', positions[1,2])
for xyz in positions: 
    print(xyz)

Element indices start at zero. Properties can be either vectorial (e.g. velocity vectors are stored as an N x 3 array) or scalar (1-d array of length N). The length of the first array dimension is in both cases equal to the number of data elements (number of particles in the example above). Array elements can either be of data type float64, int32 or int64.

If you want to modify the per-element values in a property array, make sure you are working with a modifiable version of the array by employing the underscore notation, e.g.:

modifiable_positions = data.particles_['Position_']
modifiable_positions[0] = (0.1, 2.3, 0.5)

Typed properties

The standard particle property 'Particle Type' stores the types of particles encoded as integer values, e.g.:

>>> data = node.compute()
>>> tprop = data.particles['Particle Type']
>>> print(tprop[...])
[2 1 3 ..., 2 1 2]

Here, each number in the property array refers to one of the particle types (e.g. 1=Cu, 2=Ni, 3=Fe, etc.). The defined particle types, each one represented by an instance of the ParticleType auxiliary class, are stored in the types array of the Property. Each type has a unique id, a human-readable name and other attributes like color and radius that control the visual appearance of particles belonging to the type:

>>> for type in tprop.types:
...     print(type.id, type.name, type.color, type.radius)
...
1 Cu (0.188 0.313 0.972) 0.74
2 Ni (0.564 0.564 0.564) 0.77
3 Fe (1 0.050 0.050) 0.74

IDs of types typically start at 1 and form a consecutive sequence as in the example above. Note, however, that the types list may store the ParticleType objects in an arbitrary order. Thus, in general, it is not valid to directly use a type ID as an index into the types array. Instead, the type_by_id() method should be used to look up the ParticleType:

>>> for i,t in enumerate(tprop): # (loop over the type ID of each particle)
...     print('Atom', i, 'is of type', tprop.type_by_id(t).name)
...
Atom 0 is of type Ni
Atom 1 is of type Cu
Atom 2 is of type Fe
Atom 3 is of type Cu

Similarly, a type_by_name() method exists that looks up a ParticleType by name. For example, to count the number of Fe atoms in a system:

>>> Fe_type_id = tprop.type_by_name('Fe').id   # Determine ID of the 'Fe' type
>>> numpy.count_nonzero(tprop == Fe_type_id)   # Count particles having that type ID
957

Note that OVITO supports multiple type classifications. For example, in addition to the 'Particle Type' standard particle property, which stores the chemical types of atoms (e.g. C, H, Fe, …), the 'Structure Type' property may hold the structural types computed for atoms (e.g. FCC, BCC, …) maintaining its own list of known structure types in the types array.

property component_count: The number of vector components if this is a vector property; or 1 if this is a scalar property.

property component_names: The list of names of the vector components if this is a vector property. For example, for the Position particle property this field contains ['X', 'Y', 'Z'].

property name: The name of the property.

type_by_id(id, raise_error=True)

Looks up and returns the ElementType with the given unique numeric ID in this property’s types list. Depending on raise_error, raises a KeyError or returns None if no type with the numeric ID exists.

Usage example:

# Iterate over the numeric per-particle types stored in the 'Structure Type' 
# particle property array and print the corresponding human-readable type names:
for index, type_id in enumerate(data.particles.structure_types):
    print("Atom {} is a {} atom".format(
        index, data.particles.structure_types.type_by_id(type_id).name))

An “underscore” version of the method exists, which should be used whenever you intend to modify the returned type object. type_by_id_() implicitly calls make_mutable() on the ElementType to make sure it can be changed without unexpected side effects:

# Give the numeric atom types from a LAMMPS simulation some names:
data.particles_.particle_types_.type_by_id_(1).name = 'C' 
data.particles_.particle_types_.type_by_id_(2).name = 'H' 

type_by_name(name, raise_error=True)

Looks up and returns the ElementType with the given name in this property’s types list. If multiple types exists with the same name, the first type is returned. Depending on raise_error, raises a KeyError or returns None if there isn’t a type with that name.

Usage example:

# Look up the numeric ID of atom type Si and count how many times it appears in the 'Particle Type' array
id_Si = data.particles.particle_types.type_by_name('Si').id
Si_atom_count = numpy.count_nonzero(data.particles.particle_types == id_Si)

An “underscore” version of the method exists, which should be used whenever you intend to modify the returned type object. type_by_name_() implicitly calls make_mutable() on the ElementType to make sure it can be changed without unexpected side effects:

# Rename a structure type created by the PTM modifier:
data.particles_.structure_types_.type_by_name_('Hexagonal diamond').name = 'Wurtzite'

property types

The list of ElementType instances attached to this property.

Note that the element types may be stored in arbitrary order in this list. Thus, it is not valid to use a numeric type ID as an index into this list.

class ovito.data.PropertyContainer

Base: ovito.data.DataObject

A dictionary-like object storing a set of Property objects.

It implements the collections.abc.Mapping interface. That means it can be used like a standard read-only Python dict object to access the properties by name, e.g.:

data = pipeline.compute()

positions = data.particles['Position']
has_selection = 'Selection' in data.particles
name_list = data.particles.keys()

New properties are typically added to a container with a call to create_property() as described here. To remove an existing property from a container, you can use Python’s del statement:

del data.particles_['Selection']

OVITO has several concrete implementations of the abstract PropertyContainer interface:

Particles

Bonds

VoxelGrid

DataTable

TrajectoryLines

SurfaceMesh.vertices

SurfaceMesh.faces

SurfaceMesh.regions

property count: The number of data elements in this container, e.g. the number of particles. This value is always equal to the lengths of the Property arrays managed by this container.

create_property(name, dtype=None, components=None, data=None)

Adds a new property to the container and optionally initializes it with the per-element data provided by the data parameter. The method returns the new Property instance.

The method allows to create standard as well as user-defined properties. To create a standard property, one of the standard property names must be provided as name argument:

colors = numpy.random.random_sample(size = (data.particles.count, 3))
data.particles_.create_property('Color', data=colors)

The length of the provided data array must match the number of existing elements in the container, which is given by the count attribute. You can alternatively assign the per-element values to the property after its construction:

prop = data.particles_.create_property('Color')
prop[...] = numpy.random.random_sample(size = prop.shape)

To create a user-defined property, use a non-standard property name:

values = numpy.arange(0, data.particles.count, dtype=int)
data.particles_.create_property('myint', data=values)

In this case the data type and the number of vector components of the new property are inferred from the provided data Numpy array. Providing a one-dimensional array creates a scalar property while a two-dimensional array creates a vectorial property. Alternatively, the dtype and components parameters can be specified explicitly if you are going to assign the property values at a later time:

prop = data.particles_.create_property('myvector', dtype=float, components=3)
prop[...] = numpy.random.random_sample(size = prop.shape)

If the property to be created already exists in the container, it is replaced with a modifiable copy if necessary. The existing per-element data from the old property is retained if data is None.

Note: If the container contains no properties yet, then the number of elements (e.g. particles or bonds) is still undefined. In this case the create_property() method lets you define the number of elements when inserting the very first property by specifying a data array of the desired length. For example, to create a new Particles container from scratch with 10 particles, a Numpy array of length 10 is used to initialize the Position particle property:

# An empty Particles container to begin with:
particles = Particles()

# Create 10 particles with random xyz coordinates:
xyz = numpy.random.random_sample(size = (10,3))
particles.create_property('Position', data=xyz)

After the initial Positions property has been created, the number of particles in the container is now determined and any subsequently added properties must have the exact same length.

Parameters

name – Either a standard property type constant or a name string.
data – An optional data array with per-element values for initializing the new property. The size of the array must match the element count of the container and the shape must be consistent with the number of components of the property to be created.
dtype – The element data type when creating a user-defined property. Must be either int or float.
components (int) – The number of vector components when creating a user-defined property.

Returns

The newly created Property object.

delete_elements(mask)

Deletes a subset of the elements from this container. The elements to be deleted must be specified in terms of a 1-dimensional mask array having the same length as the container (see count). The method will delete those elements whose corresponding mask value is non-zero, i.e., the i-th element will be deleted if mask[i]!=0.

For example, to delete all currently selected particles, i.e., the subset of particles whose Selection property is non-zero, one would simply write:

data.particles_.delete_elements(data.particles['Selection'])

The effect of this statement is the same as for applying the DeleteSelectedModifier to the particles list.

delete_indices(indices)

Deletes a subset of the elements from this container. The elements to be deleted must be specified in terms of a sequence of indices, all in the range 0 to count-1. The method accepts any type of iterable object, including sequence types and generators.

For example, to delete every other particle, one could use Python’s range() function to generate all even indices up to the length of the particle container:

data.particles_.delete_indices(range(0, data.particles.count, 2))

property title: The title of the data object under which it appears in the user interface of OVITO.

class ovito.data.SimulationCell

Base: ovito.data.DataObject

This object stores the geometric shape and boundary conditions of the simulation box. Typically there is exactly one SimulationCell object in a DataCollection, which is accessible through the cell field:

data = pipeline.compute()
print(data.cell[...])   # Use [...] to cast SimulationCell object to a NumPy array

The cell matrix

The geometry of the simulation cell is encoded as a 3x4 matrix \(\mathbf{M}\). The first three columns \(\mathbf{a}\), \(\mathbf{b}\), \(\mathbf{c}\) of the matrix are the vectors spanning the three-dimensional parallelepiped in Cartesian space. The fourth column specifies the Cartesian coordinates of the cell’s origin \(\mathbf{o}\) within the global simulation coordinate system:

\[\begin{split}\mathbf{M} = \begin{pmatrix} a_x & b_x & c_x & o_x \\ a_y & b_y & c_y & o_y \\ a_z & b_z & c_z & o_z \\ \end{pmatrix}\end{split}\]

The cell matrix is represented by a two-dimensional NumPy array of shape (3,4) using row-major storage order:

a = data.cell[:,0]
b = data.cell[:,1]
c = data.cell[:,2]
o = data.cell[:,3]

The is2D flag of the simulation cell indicates whether the system is two-dimensional. The cell matrix of a 2d system also has the 3x4 shape, but the cell vector \(\mathbf{c}\) and the last row of the cell matrix are ignored by many computations in OVITO if the system is marked as 2d.

Periodic boundary conditions

The pbc field stores a tuple of three Boolean flags that indicate for each cell vector whether the system is periodic in that direction or not. OVITO uses that information in various computations. If the system is two-dimensional, the value of the third pbc flag is ignored.

Modifying the simulation cell

When you modify the entries of the cell matrix, make sure you use the underscore notation to request a modifiable version of the SimulationCell object:

# Make cell twice as large along the Y direction by scaling the second cell vector:
data.cell_[:,1] *= 2.0

Reset the simulation cell to an orthogonal box \((L_x, L_y, L_z)\) centered at the origin:

lx = 20.0; ly = 10.0; lz = 8.0
data.cell_[:,0] = (lx, 0, 0)
data.cell_[:,1] = (0, ly, 0)
data.cell_[:,2] = (0, 0, lz)
data.cell_[:,3] = numpy.dot((-0.5, -0.5, -0.5), data.cell[:3,:3])
data.cell_.pbc = (True, True, True)

Visual representation

Every SimulationCell has an attached SimulationCellVis element, which controls the visual appearance of the wireframe box in rendered images. It can be accessed via the vis attribute inherited from the DataObject base class:

# Change display color of simulation cell to red:
data.cell.vis.rendering_color = (1.0, 0.0, 0.0)

delta_vector(ra, rb, return_pbcvec=False)

Computes the vector connecting two points \(r_a\) and \(r_b\) in a periodic simulation cell by applying the minimum image convention.

The method starts by computing the 3d vector \({\Delta} = r_b - r_a\) for two points \(r_a\) and \(r_b\), which may be located in different images of the periodic simulation cell. The minimum image convention is then applied to obtain the new vector \({\Delta'} = r_b' - r_a\), where the original point \(r_b\) has been replaced by the periodic image \(r_b'\) that is closest to \(r_a\), making the vector \({\Delta'}\) as short as possible (in reduced coordinate space). \(r_b'\) is obtained by translating \(r_b\) an integer number of times along each of the three cell directions: \(r_b' = r_b - H*n\), with \(H\) being the 3x3 cell matrix and \(n\) being a vector of three integers that are chosen by the method such that \(r_b'\) is as close to \(r_a\) as possible.

Note that the periodic image convention is applied only along those cell directions for which periodic boundary conditions are enabled (see pbc property). For other directions no shifting is performed, i.e., the corresponding components of \(n = (n_x,n_y,n_z)\) will always be zero.

The method is able to compute the results for either an individual pair of input points or for two arrays of input points. In the latter case, i.e. if the input parameters ra and rb are both 2-D arrays of shape Nx3, the method returns a 2-D array containing N output vectors. This allows applying the minimum image convention to a large number of point pairs in one function call.

The option return_pbcvec lets the method return the vector \(n\) introduced above as an additional output. The components of this vector specify the number of times the image point \(r_b'\) needs to be shifted along each of the three cell directions in order to bring it onto the original input point \(r_b\). In other words, it specifies the number of times the computed vector \({\Delta} = r_b - r_a\) crosses a periodic boundary of the cell (either in positive or negative direction). For example, the PBC shift vector \(n = (1,0,-2)\) would indicate that, in order to get from input point \(r_a\) to input point \(r_b\), one has to cross the cell boundaries once in the positive x-direction and twice in the negative z-direction. If return_pbcvec is True, the method returns the tuple (\({\Delta'}\), \(n\)); otherwise it returns just \({\Delta'}\). Note that the vector \(n\) computed by this method can be used, for instance, to correctly initialize the Bonds.pbc_vectors property for newly created bonds that cross a periodic cell boundary.

Parameters

ra – The Cartesian xyz coordinates of the first input point(s). Either a 1-D array of length 3 or a 2-D array of shape (N,3).
rb – The Cartesian xyz coordinates of the second input point(s). Must have the same shape as ra.
return_pbcvec (bool) – If True, also returns the vector \(n\), which specifies how often the vector \((r_b' - r_a)\) crosses the periodic cell boundaries.

Returns

The vector \({\Delta'}\) and, optionally, the vector \(n\).

Note that there exists also a convenience method Particles.delta_vector(), which should be used in situations where \(r_a\) and \(r_b\) are the coordinates of two particles in the simulation cell.

property inverse

Read-only property returning the reciprocal cell matrix \(\mathbf{M}^*\) - an array of shape (3,4):

\[\mathbf{M}^* = \begin{bmatrix} \mathbf{a}^* & \mathbf{b}^* & \mathbf{c}^* & \mathbf{o}^* \end{bmatrix}\]

with the real-space cell volume \(V = (\mathbf{a} \times \mathbf{b}) \cdot \mathbf{c}\) and reciprocal cell vectors given by

\[\mathbf{a}^* = \frac{\mathbf{b} \times \mathbf{c}}{V} \qquad \mathbf{b}^* = \frac{\mathbf{c} \times \mathbf{a}}{V} \qquad \mathbf{c}^* = \frac{\mathbf{a} \times \mathbf{b}}{V} \qquad \mathbf{o}^* = -\begin{pmatrix} \mathbf{a}^* \; \mathbf{b}^* \; \mathbf{c}^* \end{pmatrix} \mathbf{o} \mathrm{.}\]

property is2D

Specifies whether the system is two-dimensional (instead of three-dimensional). For two-dimensional systems, the third pbc flag and the cell vector \(\mathbf{c}\) are typically ignored.

Default: False

property pbc

A tuple of three Boolean flags specifying whether periodic boundary conditions are enabled along the cell’s three spatial directions.

Default: (False, False, False)

property volume

Read-only property computing the volume of the three-dimensional simulation cell. The returned value is equal to the absolute determinant of the 3x3 submatrix formed by the three cell vectors, i.e. the scalar triple product \(V=|(\mathbf{a} \times \mathbf{b}) \cdot \mathbf{c}|\):

assert cell.volume == abs(numpy.linalg.det(cell[0:3,0:3]))

property volume2D

Read-only property computing the area of the two-dimensional simulation cell (see is2D). The returned value is equal to the magnitude of the cross-product of the first two cell vectors, i.e. \(V_{\mathrm{2d}} = |\mathbf{a} \times \mathbf{b}|\):

assert cell.volume2D == numpy.linalg.norm(numpy.cross(cell[:,0], cell[:,1]))

class ovito.data.SurfaceMesh

Base: ovito.data.DataObject

A triangle mesh representation of a surface or, more precisely, a two-dimensional manifold that is closed and orientable. Typically, surface meshes are produced by OVITO’s modifiers such as ConstructSurfaceModifier, CreateIsosurfaceModifier, CoordinationPolyhedraModifier or VoronoiAnalysisModifier. See the user manual page on surface meshes for more information on this geometric data structure.

Periodic simulation domains

A surface mesh may be embedded in a periodic domain, i.e. in a simulation cell with periodic boundary conditions. That means faces of the surface mesh can connect vertices on opposite sides of the simulation box and will wrap around correctly. OVITO takes care of computing the intersections of such a periodic surface with the box boundaries and automatically produces a non-periodic representation of the mesh when it comes to visualization. You can explicitly request a non-periodic version of the mesh, which was clipped at the periodic box boundaries, by calling the to_triangle_mesh() method from a script.

The spatial domain the surface mesh is embedded in is specified as a SimulationCell object attached to the SurfaceMesh. It is stored in the SurfaceMesh.domain field. Note that this SimulationCell may, in some situations, not be identical with the global simulation cell set for the DataCollection.

Spatial regions

Being a closed, orientable manifold a surface mesh divides space into two or more spatial regions. For example, if the surface mesh was constructed by the ConstructSurfaceModifier from a set of input particles, then the space enclosed by the surface is the “filled” region and the exterior space is the “empty” region containing no particles.

In general, the SurfaceMesh class manages a variable list of regions, each being identified by a numeric, zero-based index. The locate_point() method allows to determine which spatial region some point in space belongs to.

It may be that a surface mesh is degenerate. In such a case there is only one spatial region filling entire space. For example, when there are no input particles, the ConstructSurfaceModifier cannot construct a regular surface mesh and the “empty” region fills the entire simulation cell. Conversely, if the periodic simulation cell is completely filled with particles, the “filled” region covers the entire simulation domain and the generated surface mesh will consist of no vertices or faces, i.e., it is degenerate. To discriminate between the two situations, the SurfaceMesh class has a space_filling_region field, which specifies the spatial region that fills entire space in cases where the mesh is degenerate.

File export

A surface mesh can be exported to a geometry file in the form of a triangle mesh using OVITO’s export_file() function. To this end, a non-periodic version is produced by truncating triangles at the domain boundaries and generating “cap polygons” filling the holes that occur at the intersection of the surface with periodic domain boundaries. The following example code writes a VTK geometry file (vtk/trimesh export format):

from ovito.io import import_file, export_file
from ovito.data import SurfaceMesh
from ovito.modifiers import ConstructSurfaceModifier

# Load a particle set and construct the surface mesh:
pipeline = import_file("input/simulation.dump")
pipeline.modifiers.append(ConstructSurfaceModifier(radius = 2.8))
mesh = pipeline.compute().surfaces['surface']

# Export the mesh to a VTK file for visualization with ParaView.
export_file(mesh, 'output/surface_mesh.vtk', 'vtk/trimesh')

Clipping planes

A set of clipping planes can be assigned to a SurfaceMesh to clip away parts of the mesh for visualization purposes. This may be useful to e.g. cut a hole into a closed surface allowing to look inside the enclosed volume. The SurfaceMesh objects manages a list of clipping planes, which is accessible through the get_clipping_planes() and set_clipping_planes() methods. Note that the cut operations are non-destructive and get performed only on the transient, non-periodic version of the mesh generated during image rendering or when exporting the mesh to a file. The original surface mesh is not affected. The SliceModifier, when applied to a SurfaceMesh, performs the slice by simply adding a corresponding clipping plane to the SurfaceMesh. The actual truncation of the mesh happens later on, during the final visualization step, when a non-periodic version is computed.

property domain: The SimulationCell describing the (possibly periodic) domain which this surface mesh is embedded in. Note that this cell generally is independent of and may be different from the cell found in the DataCollection.

property faces: The PropertyContainer storing the per-face properties of the mesh.

get_clipping_planes()

Returns a N x 4 array containing the definitions of the N clipping planes attached to this SurfaceMesh.

Each plane is defined by its unit normal vector and a signed displacement magnitude, which determines the plane’s distance from the coordinate origin along the normal, giving four numbers per plane in total. Those parts of the surface mesh which are on the positive side of the plane (in the direction the normal vector) will be cut away during rendering.

Note that the returned Numpy array is a copy of the internal data stored by the SurfaceMesh.

locate_point(pos, eps=1e-6)

Determines which spatial region of the mesh contains the given point in 3-d space.

The function returns the numeric ID of the region pos is located in. Note that region ID -1 is typically reserved for the empty exterior region, which, if it exists, is the one not containing any atoms or particles. Whether non-negative indices refer to only filled (interior) regions or also empty regions depends on the algorithm that created the surface mesh and its spatial regions.

The parameter eps is a numerical precision threshold to detect if the query point is positioned exactly on the surface boundary, i.e. on the manifold separating two spatial regions. This condition is indicated by the special return value None. Set eps to 0.0 to disable the point-on-boundary test. Then the method will never return None as a result, but the determination of the spatial region will become numerically unstable if the query point is positioned right on a boundary surface.

Parameters

pos (array-like) – The (x,y,z) coordinates of the query point
eps (float) – Numerical precision threshold for point-on-boundary test

Returns

The numeric ID of the spatial region containing pos; or None if pos is exactly on the dividing boundary between two regions

property regions: The PropertyContainer storing the properties of the spatial regions of the mesh.

set_clipping_planes(planes): Sets the clipping planes of this SurfaceMesh. The array planes must follow the same format as the one returned by get_clipping_planes().

property space_filling_region: Indicates the index of the spatial region that fills the entire domain in case the surface is degenerate, i.e. the mesh has zero faces. The invalid index -1 is typically associated with the empty (exterior) region.

to_triangle_mesh()

Converts the surface into a non-periodic TriangleMesh.

Returns: (trimesh, caps, facemap)

trimesh: A TriangleMesh representing the surface geometry after clipping it at the periodic boundaries of the domain and any attached clipping planes (see get_clipping_planes()).
caps: A TriangleMesh containing the cap polygons generated at intersections of the periodic surface mesh with boundaries of the simulation domain. Will be None if the surface mesh has no attached domain, the domain is degenerate, or the surface mesh does not represent a closed manifold.
facemap: A NumPy array of indices into the face list of this SurfaceMesh, one for each triangular face of the TriangleMesh trimesh. This map lets you look up for each face of the output mesh what the corresponding face of the input surface mesh is.

property topology: A SurfaceMeshTopology object storing the face connectivity of the mesh.

property vertices: The PropertyContainer storing all per-vertex properties of the mesh, including the vertex coordinates (Position property).

class ovito.data.SurfaceMeshTopology

Base: ovito.data.DataObject

This data structure holds the connectivity information of a SurfaceMesh, which consists of vertices, faces and halfedges. It is stored in the SurfaceMesh.topology field.

All entities (vertices, faces, and halfedges) of the mesh are identified by numeric indices ranging from 0 to (vertex_count-1), (face_count-1), and (edge_count-1), respectively. Vertices and faces of the mesh may be associated with auxiliary properties, which are stored separately from the topology in the SurfaceMesh.vertices and SurfaceMesh.faces property containers. In particular, the coordinates of the mesh’s vertices are stored in the Position property array of SurfaceMesh.vertices.

A halfedge is a directed edge \(a \to b\) connecting two vertices \(a\) and \(b\) – depicted as a half-arrow in the figure. A face is implicitly defined by a circular sequence of halfedges that bound the face. Typically, halfedges come in pairs. The halfedge \(a \to b\) and its opposite halfedge, \(b \to a\), form a pair that links two neighboring faces together. Thus, halfedge pairs are essential for forming a connected, two-dimensional surface manifold. The surface is said to be closed, i.e. it has no open boundaries, if all halfedges of the mesh are associated with corresponding opposite halfedges.

For each vertex the topology object maintains a linked-list of directed halfedges leaving that vertex. It can be accessed through the first_vertex_edge() and next_vertex_edge() methods.

For each face the topology object maintains a circular linked-list of directed halfedges bounding that face. It can be accessed through the first_face_edge() and next_face_edge()/prev_face_edge() methods.

adjacent_face(edge: int) → int: Returns the face that is bounded by the halfedge edge.

count_face_edges(face: int) → int: Returns the number of halfedges that bound face. See the code example for next_face_edge() to learn how this method works.

count_manifolds(edge: int) → int

count_vertex_edges(vertex: int) → int: Returns the number of halfedges that leave vertex. See the code example for next_vertex_edge() to learn how this method works.

property edge_count: Number of halfedges in the SurfaceMesh.

property face_count: Number of faces of the SurfaceMesh. This is always equal to the count of the SurfaceMesh.faces property container.

find_edge(face: int, vertex1: int, vertex2: int) → int

Given a face, finds the halfedge of that face which leads from vertex1 to vertex2. If no such halfedge exists, returns -1.

This method can be used to quickly find the edge connecting two vertices of a face without the need to explicitly visit and check each edge bounding the face.

first_edge_vertex(edge: int) → int: Returns the vertex the given halfedge is leaving from. To retrieve the vertex the halfedge is leading to, call second_edge_vertex().

first_face_edge(face: int) → int: Returns some halfedge bounding the given face. Given that first halfedge, all other halfedges bounding the same face can be visited using next_face_edge() or prev_face_edge().

first_face_vertex(face: int) → int: Given a face, this method returns some vertex of that face. This is equivalent to retrieving the vertex to which the first halfedge of the face is conneced to, i.e. first_edge_vertex(first_face_edge(face)).

first_vertex_edge(vertex: int) → int: Returns the head halfedge from the linked list of halfedges leaving vertex. Subsequent halfedges from the linked list can be retrieved with next_vertex_edge(). If no halfedges are connected to vertex, the method returns -1.

has_opposite_edge(edge: int) → bool: Returns whether the given halfedge edge is associated with a corresponding reverse halfedge bounding an adjacent face in the same manifold. This is equivalent to checking the return value of opposite_edge(), which returns -1 to indicate that edge does not have an opposite edge.

has_opposite_face(face: int) → bool: Returns whether face is part of a two-sided manifold. A face that is part of a two-sided manifold has a ‘partner’ face with opposite orientation, which can be retrieved through the opposite_face() method.

next_face_edge(edge: int) → int

Given the halfedge edge bounding some face, this method returns the following halfedge when going around the face in forward direction (counter-clockwise - when looking on the face from the front). All halfedges of the face form a circular sequence - without a particular beginning or end. You can loop over this circular sequence in foward or backward direction with the next_face_edge() and prev_face_edge() methods. Given some mesh face, you can obtain a first halfedge through the first_face_edge() method.

The following code example shows how to visit all halfedges of a face in order. Since the halfedges form a circular linked list, we have to remember which edge we started from to terminate the loop once we reach the first edge again:

def count_edges(mesh: SurfaceMesh, face: int) -> int:
    start_edge = mesh.topology.first_face_edge(face)
    count = 1
    edge = mesh.topology.next_face_edge(start_edge)
    while edge != start_edge:
        assert mesh.topology.adjacent_face(edge) == face
        count += 1
        edge = mesh.topology.next_face_edge(edge)
    return count

# The function defined above is equivalent to SurfaceMeshTopology.count_face_edges():
assert count_edges(mesh, 0) == mesh.topology.count_face_edges(0)

next_manifold_edge(edge: int) → int

next_vertex_edge(edge: int) → int

Returns another halfedge leaving from the same vertex as edge. Together with first_vertex_edge() this method allows you to iterate over all halfedges connected to a given vertex. Once the end of the linked list has been reached, the method returns -1.

The following example demonstrates how to visit all outgoing halfedges of some vertex and count them:

def count_edges(mesh: SurfaceMesh, vertex: int) -> int:
    count = 0
    edge = mesh.topology.first_vertex_edge(vertex)
    while edge != -1:
        assert mesh.topology.first_edge_vertex(edge) == vertex
        count += 1
        edge = mesh.topology.next_vertex_edge(edge)
    return count

# The function defined above is equivalent to SurfaceMeshTopology.count_vertex_edges():
assert count_edges(mesh, 0) == mesh.topology.count_vertex_edges(0)

opposite_edge(edge: int) → int

Given the halfedge edge, returns the reverse halfedge that bounds an adjacent face. This opposite halfedge connects the same two vertices as edge but in reverse order. You can use this method to determine whether the face bounded by edge has a neighboring face that is part of the same manifold:

def get_neighboring_face(mesh: SurfaceMesh, edge: int) -> int:
    opp_edge = mesh.topology.opposite_edge(edge)
    if opp_edge == -1: return -1
    assert mesh.topology.first_edge_vertex(edge) == mesh.topology.second_edge_vertex(opp_edge)
    assert mesh.topology.second_edge_vertex(edge) == mesh.topology.first_edge_vertex(opp_edge)
    return mesh.topology.adjacent_face(opp_edge)

You may call the convenience method has_opposite_edge() to determine whether a halfedge is associated with a corresponding opposite halfedge. If the surface mesh does not form a closed manifold, the halfedges at the boundary of the manifold do not have opposite halfedges, because there are no adjacent faces where the surface terminates.

opposite_face(face: int) → int: Returns the face on the opposite side of the two-sided manifold, or -1 if the manifold is one-sided. The returned face shares the same vertices with face but in reverse order. Note that opposite_face(opposite_face(face))==face.

prev_face_edge(edge: int) → int: Given the halfedge edge bounding some face, this method returns the previous halfedge going around that face in backward direction (clockwise - when looking on the face from the front). All halfedges of a face form a circular sequence - without a specific beginning or end. You can loop over this circular sequence in foward or backward direction with the next_face_edge() and prev_face_edge() methods.

second_edge_vertex(edge: int) → int: Returns the vertex the given halfedge is leading to. To retrieve the vertex the halfedge is leaving from, call first_edge_vertex().

property vertex_count: Number of vertices of the SurfaceMesh. This is always equal to the count of the SurfaceMesh.vertices property container.

class ovito.data.TrajectoryLines

Base: ovito.data.PropertyContainer

Data object that stores the trajectory lines of a set of particles, which have been traced by the GenerateTrajectoryLinesModifier. It is typically part of a pipeline’s output data collection, from where it can be accessed via the DataCollection.trajectories field.

A TrajectoryLines object has an associated TrajectoryVis element, which controls the visual appearance of the trajectory lines in rendered images. This visual element is accessible through the vis attribute of the base class.

property particle_ids: The Property data array storing the particle IDs of the line vertices.

property positions: The Property data array storing the XYZ coordinates of the line vertices.

property time_stamps: The Property data array storing the time stamps of the line vertices.

class ovito.data.TriangleMesh

Base: ovito.data.DataObject

This data object type stores a three-dimensional mesh made of vertices and triangular faces. Such a mesh can describing general polyhedral geometry to be visualized side by side with the particle simulation data.

Typically, triangle meshes are imported from external geometry data files (e.g. STL, OBJ, VTK formats) using the import_file() function. See also the corresponding section of the OVITO user manual. All triangle meshes in a data collection are accessible through the DataCollection.triangle_meshes dictionary view.

Note that the SurfaceMesh class is a second object type that can represent surface geometries, just like a TriangleMesh. In contrast to triangle meshes, surface meshes may be embedded in periodic simulation domains and are closed manifolds in most cases. Furthermore, surface meshes can store arbitrary per-vertex and per-face property values – something triangle meshes cannot do. A triangle mesh is a more low-level data structure, which can be sent directly to a GPU for rendering. A surface mesh, in contrast, is a more high-level data structure, which gets automatically converted to a triangle mesh for visualization.

The visual appearance of the triangle mesh is controlled through the attached TriangleMeshVis element (see DataObject.vis field of base class).

A triangle mesh consists of \(n_{\mathrm{v}}\) vertices and \(n_{\mathrm{f}}\) triangular faces. These counts are exposed by the class as attributes vertex_count and face_count. Each face connects three vertices of the mesh, and several faces may share a vertex. The faces are stored as triplets of zero-based indices into the vertex list.

property face_count

The number of triangular faces of the mesh, \(n_{\mathrm{f}}\).

Default: 0

get_faces(): Returns the list of triangles of the mesh as a NumPy array of shape \((n_{\mathrm{f}}, 3)\). The array contains for each face three zero-based indices into the mesh’s vertex list as returned by get_vertices(). The returned face array holds a copy of the internal data managed by the TriangleMesh.

get_vertices(): Returns the xyz coordinates of the vertices of the mesh as a NumPy array of shape \((n_{\mathrm{v}}, 3)\). The returned array holds a copy of the internal vertex data managed by the TriangleMesh.

set_faces(vertex_indices): Sets the faces of the mesh. vertex_indices must be an array-like object of shape \((n_{\mathrm{f}}, 3)\) containing one integer triplet per triangular face. Each integer is a zero-based index into the mesh’s vertex list. The TriangleMesh copies the data from the array into its internal storage. If necessary, the value of face_count is automatically adjusted to match the first dimension of the vertex_indices array.

set_vertices(coordinates): Sets the xyz coordinates of the vertices of the mesh. coordinates must be an array-like object of shape \((n_{\mathrm{v}}, 3)\). The TriangleMesh copies the data from the array into its internal storage. If necessary, the value of vertex_count is automatically adjusted to match the first dimension of the coordinates array.

property vertex_count

The number of vertices of the mesh, \(n_{\mathrm{v}}\).

Default: 0

class ovito.data.VoxelGrid

Base: ovito.data.PropertyContainer

A two- or three-dimensional structured grid. Each cell (voxel) of the grid is of the same size and shape. The geometry of the entire grid, its domain, is defined by an attached SimulationCell object, which specific a three-dimensional parallelepiped or a two-dimensional parallelogram. See also the corresponding user manual page for more information on this object type.

The shape property of the grid specifies the number of voxels along each domain cell vector. The size of an individual voxel is given by domain cell size divided by the number of voxels in each spatial direction.

Every voxel of the grid may be associated with one or more field values. The data for these voxel properties is stored in standard Property objects, similar to particle or bond properties. Voxel properties can be accessed by name through the dictionary interface that the VoxelGrid class inherits from its PropertyContainer base class.

Voxel grids can be loaded from input data files, e.g. a CHGCAR file containing the electron density computed by the VASP code, or they can be dynamically generated within OVITO. The SpatialBinningModifier lets you project the information associated with the unstructured particle set to a structured voxel grid.

Given a voxel grid, the CreateIsosurfaceModifier can then generate a SurfaceMesh representing an isosurface for a field quantity defined on the voxel grid.

Example

The following code example demonstrates how to create a new VoxelGrid from scratch and initialize it with data from a Numpy array:

# Starting with an empty DataCollection: 
data = DataCollection()

# Create a new SimulationCell object defining the outer spatial dimensions
# of the grid and the boundary conditions, and add it to the DataCollection: 
data.cell = SimulationCell(pbc=(True, True, True), vis=SimulationCellVis(line_width=0.03))
data.cell_[:,:3] = [[10,0,0],[0,10,0],[0,0,10]]

# Generate a three-dimensional Numpy array containing the grid cell values.
nx = 10; ny = 6; nz = 8
field_data = numpy.random.random((nx, ny, nz))

# Create the VoxelGrid object and give it a unique identifier by which it can be referred to later on.
# Link the voxel grid to the SimulationCell object created above, which defines its spatial extensions.
# Specify the shape of the grid, i.e. the number of cells in each spatial direction.
# Finally, assign a VoxelGridVis visual element to the data object to make the grid visible in the scene.
grid = VoxelGrid(
    identifier = 'field', 
    domain = data.cell, 
    shape = field_data.shape, 
    vis = VoxelGridVis(enabled=True, transparency=0.6))

# Associate a new property with the voxel grid cells and initialize it with the data from the Numpy array. 
# Note that the data must be provided as linear (1-dim.) array with the following type of memory layout:
# The first grid dimension (x) is the fasted changing index while the third grid dimension (z) is the slowest varying index.
# In this example, this corresponds to the "Fortran" memory layout of Numpy. 
grid.create_property('Field Value', data=field_data.flatten(order='F'))

# Insert the VoxelGrid object into the DataCollection.
data.objects.append(grid)
        
# For demonstration purposes, compute an isosurface on the basis of the VoxelGrid created above.
data.apply(CreateIsosurfaceModifier(operate_on='voxels:field', property='Field Value', isolevel=0.7))

property domain: The SimulationCell describing the (possibly periodic) domain which this grid is embedded in. Note that this cell generally is independent of and may be different from the cell found in the DataCollection.

property shape

A tuple with the numbers of grid cells along each of the three cell vectors of the domain.

For two-dimensional grids, for which the is2D property of the domain is set to true, the third entry of the shape tuple is always equal to 1.

Assigning new shape dimensions to the grid automatically resizes the one-dimensional data arrays of the PropertyContainer base class if necessary and updates its PropertyContainer.count property to be equal to the product of the three shape dimensions, i.e. the total number of voxel cells.

ovito.data

`ovito.data`