Data model

The data processed by OVITO is organized into data objects, each representing specific fragments of the entire dataset. For example, a dataset may be composed of a SimulationCell object holding the cell vector information, a Particles object holding the particle information, a Bonds object holding the bond information and several Property objects storing the particle/bond property data. For each data object type, you will find a corresponding Python class in the module. All of them are subclasses of the abstract DataObject base class.

Data objects can contain other data objects. For example, the Particles object holds a number of Property objects, one for each particle property that is defined. It also holds a Bonds object, which in turn is a container for several Property objects that represent the bond property arrays:


The top-level object, which contains all other data objects, is the DataCollection. It is unit that a Pipeline processes, i.e., which gets loaded from an input file, flows down a data pipeline and gets processed by the modifiers. Modifiers alter data objects within the DataCollection, add new data objects to the collection, or insert additional sub-objects into nested containers.

When you call the Pipeline.compute() method, you receive back a DataCollection holding the computation results of the pipeline. The DataCollection class provides fields for accessing the various kinds of sub-objects it can contain.

It is important to note that a DataCollection object only holds the data of a single animation frame and not an entire animation sequence. In OVITO’s data model, a simulation trajectory is rather represented by a series of DataCollection instances.


The Particles data object, which is accessible through the DataCollection.particles field, holds all particle-related data. OVITO uses a property-centered representation of particles, where information is stored in a set of uniform memory arrays, all of the same length. Each array represents one particle property such as position, type, mass, color, etc., and stores the values for all N particles in the system. A property data array is an instance of the Property data object class, which is not only used by OVITO for storing particle properties but also bond properties, voxel grid properties and data point properties, for example.

A particle system is nothing else than a loose collection of Property objects, which are held together by a container, the Particles object, which is a specialization of the generic PropertyContainer base class. Each particle property has a unique name that identifies the meaning of the property. OVITO defines a set of standard property names, which have a specific meaning and data layout. The Position standard property, for example, holds the XYZ coordinates of all particles and is always present. Other standard properties, such as Color or Mass, are optional and may or may not be present in a Particles container. Furthermore, Property objects with non-standard names are supported, which represent user-defined particle properties.


The Particles container object mimics the interface of a Python dictionary, which lets you look up properties by name. To find out which properties are present, you can query the dictionary for its keys:

>>> data = pipeline.compute()
>>> list(data.particles.keys())
['Particle Identifier', 'Particle Type', 'Position', 'Color']

Individual particle properties can be looked up by their name:

>>> color_property = data.particles['Color']

Some standard properties can also be accessed through convenient getter attributes defined by the Particles class:

>>> color_property = data.particles.colors

The Particles container type is a sub-class of the more general PropertyContainer base type. OVITO defines more property containers such as the Bonds, DataSeries and VoxelGrid types, which all work similar to the Particles type. They all have in common that they represent an array of data elements, which can possess a variable sets of properties.

Property objects

As mentioned above a Property is a uniform storage array holding the values of one property. It behaves pretty much like a standard NumPy array:

>>> coordinates = data.particles.positions
>>> print(coordinates[...])
[[ 73.24230194  -5.77583981  -0.87618297]
 [-49.00170135 -35.47610092 -27.92519951]
 [-50.36349869 -39.02569962 -25.61310005]
 [ 42.71210098  59.44919968  38.6432991 ]
 [ 42.9917984   63.53770065  36.33330154]
 [ 44.17670059  61.49860001  37.5401001 ]]

Property arrays can be one-dimensional (in case of scalar properties) or two-dimensional (in case of vector properties). The size of the first dimension is always equal to the number of data elements (e.g. particles) stored by the parent PropertyContainer. The container reports the current number of elements via its count attribute:

>>> data.particles.count
>>> data.particles['Mass'].shape   # 1-dim. array
>>> data.particles['Color'].shape  # 2-dim. array
(28655, 3)
>>> data.particles['Color'].dtype  # Property data type

OVITO currently supports three different numeric data types for property arrays: float64, int32 and int64. For built-in standard properties the data type and the dimensionality are prescribed by OVITO. For user-defined properties they can be chosen by the user when creating a new property.

© 2019, Alexander Stukowski.
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