OVITO scripting interface provides full access to most of OVITO’s program features. Using Python scripts, you can do many things that are already familiar from the graphical user interface (and even a few more):
- Import data from external files
- Apply modifiers to a dataset and configure them
- Set up a camera and render pictures or movies of the scene
- Control the visual appearance of particles and other objects
- Access per-particle properties and other analysis results computed by OVITO
- Implement new types of modifiers
- Export data to a file
The following sections will introduce the essential concepts and walk you through different parts of OVITO’s scripting framework.
OVITO’s data pipeline architecture¶
If you have worked with OVITO’s graphical user interface before, you should already be familiar with its key workflow concept: After loading a simulation file into OVITO you typically apply one or more modifiers that act on the input data. The result of this sequence of modifiers (modification pipeline) is computed by OVITO and displayed in the interactive viewports.
To access this capability from a script, we first need to understand the basics of OVITO’s underlying data model. In general, there are two different groups of objects that participate in the described system: Objects that constitute the modification pipeline (the modifiers and the data source) and data objects, which carry the data that is being processed by the modifier. The data objects enter the modification pipeline, get modified by a modifier, or are newly produced (e.g. computed particle properties). We start by discussing the objects that consitute a modification pipeline.
Data sources, modifiers, and more¶
A modification pipeline is fed by a data source, which is an object
that provides or generates the input data entering a modification pipeline. OVITO currently knows two types of
FileSource class is the data source typically used. It is responsible for loading data
from an external file and passing it on to the modification pipeline.
The data source and the modification pipeline together form an
ObjectNode. This class
orchestrates the data flow from the source into the modification pipeline and caches the pipeline’s output.
As we will see later, the
ObjectNode is also responsible for displaying the output
data in the three-dimensional scene. The data source is stored in the
property. The modification pipeline is simply a list of
Modifier objects and is
is accessible through the
ObjectNode is usually placed in the scene, i.e. the three-dimensional world that is visible
through OVITO’s viewports. All objects in the scene, and all other information that would get saved along in
.ovito file (e.g. current render settings, viewport cameras, etc.), comprise the so-called
A Python script always runs in the context of one global
DataSet instance. This
instance can be accessed through the
ovito.dataset global variable. The
DataSet provides access to the
list of object nodes in the scene (
the current animation settings (
dataset.anim), the four
viewports in OVITO’s main window (
dataset.viewports), and more.
Loading data and applying modifiers¶
>>> from ovito.io import * >>> node = import_file("simulation.dump")
This high-level function creates an
ObjectNode with an empty modification pipeline
and sets up a
FileSource (which will subsequently load the data
from the given file) and assigns it to the
We can now start populating the node’s modification pipeline with some modifiers by appending them
>>> from ovito.modifiers import * >>> node.modifiers.append(SelectExpressionModifier(expression="PotentialEnergy < -3.9")) >>> node.modifiers.append(DeleteSelectedParticlesModifier())
Modifiers are constructed by calling the constructor of the corresponding classes, which are
all found in the
ovito.modifiers module. Note how a modifier’s parameters can be initialized in two ways:
When constructing a new object (such as a modifier, but also most other OVITO objects) it is possible to directly initialize its properties by passing keyword arguments to the constructor function. Thus
node.modifiers.append(CommonNeighborAnalysisModifier(cutoff = 3.2, only_selected = True))
is equivalent to setting the properties one by one after constructing the object:
modifier = CommonNeighborAnalysisModifier() modifier.cutoff = 3.2 modifier.only_selected = True node.modifiers.append(modifier)
After the modification pipeline has been populated with modifiers, we can do three different things: (i) write the results to a file, (ii) render an image of the data, (iii) or directly work with the pipeline data and read out particle properties and other results. Keep reading.
Exporting data to a file¶
Exporting the data that has left the modification pipeline to a file is simple;
we use the
ovito.io.export_file() function for this:
>>> export_file(node, "outputdata.dump", "lammps_dump", ... columns = ["Position.X", "Position.Y", "Position.Z", "Structure Type"])
The first argument of this high-level function is the node whose pipeline results should be exported.
It is followed by the name of the output file and the desired output format.
Depending on the selected file format, additional keyword arguments such as the list of particle properties to
be exported must be provided. See the documentation of the
export_file() function for more information.
To render an image, we first need a viewport that defines the view on the three-dimensional scene.
We can either use one of the four predefined viewports of OVITO for this, or simply create an ad hoc
Viewport instance in Python:
>>> from ovito.vis import * >>> vp = Viewport() >>> vp.type = Viewport.Type.PERSPECTIVE >>> vp.camera_pos = (-100, -150, 150) >>> vp.camera_dir = (2, 3, -3) >>> vp.fov = math.radians(60.0)
As you can see, the
Viewport class has several parameters that control the
position and orientation of the camera, the projection type, and the field of view (FOV) angle. Note that this
viewport will not be visible in OVITO’s main window, because it is not part of the current
it is only a temporary object used within the script.
In addition we need to create a
RenderSettings object, which controls the rendering
process (These are the parameters you normally set on the Render tab in OVITO’s main window):
>>> settings = RenderSettings() >>> settings.filename = "myimage.png" >>> settings.size = (800, 600)
Now we have specified the output filename and the size of the image in pixels.
We should not forget to also add the
ObjectNode to the scene by calling:
Because only object nodes that are part of the scene are visible in the viewports and in rendered images. Finally, we can let OVITO render an image of the viewport:
As a final remark, note how we could have used the more compact notation for object initialization introduced above.
We can configure the newly created
RenderSettings by passing the parameter values directly to the class constructors:
vp = Viewport( type = Viewport.Type.PERSPECTIVE, camera_pos = (-100, -150, 150), camera_dir = (2, 3, -3), fov = math.radians(60.0) ) vp.render(RenderSettings(filename = "myimage.png", size = (800, 600)))
Accessing computation results¶
OVITO’s scripting interface allows you to directly access the output data leaving the modification pipeline. But before doing so, we first have to ask OVITO to compute the results of the modification pipeline:
compute() method ensures that all modifiers in the pipeline of the node
have been successfully evaluated. Note that the
export_file() functions implicitly call
for us. But now, since we want to directly access the pipeline results, we have to explicitly request
an evaluation of the modification pipeline.
>>> node.output DataCollection(['Simulation cell', 'Particle Identifier', 'Position', 'Potential Energy', 'Color', 'Structure Type'])
It contains all the data objects that were processed or produced
by the modification pipeline. For example, to access the
simulation cell we would write:
>>> node.output.cell.matrix [[ 148.147995 0. 0. -74.0739975 ] [ 0. 148.07200623 0. -74.03600311] [ 0. 0. 148.0756073 -74.03780365]] >>> node.output.cell.pbc (True, True, True)
Similarly, the data of individual
particle properties may be accessed as NumPy arrays:
>>> import numpy >>> node.output.particle_properties.position.array [[ 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 ]]
Sometimes we might also be interested in the data that enters the modification pipeline.
The input data, which was read from the external file, is cached by the
which is itself a
>>> node.source DataCollection(['Simulation cell', 'Particle Identifier', 'Position'])
Controlling the visual appearance of objects¶
So far we have only looked at objects that represent data, e.g. particle properties or the simulation cell. Let’s see how this data is displayed and how we can control its visual appearance.
>>> cell = node.source.cell >>> cell # This is the SimulationCell data object <SimulationCell at 0x7f9a414c8060> >>> cell.display # This is its attached display object <SimulationCellDisplay at 0x7fc3650a1c20>
SimulationCellDisplay is responsible for rendering the simulation
cell in the viewports and provides parameters that allow us to configure the visual appearance. For example, to change the
display color of the simulation box:
>>> cell.display.rendering_color = (1.0, 0.0, 1.0)
We can also turn off the display of any object entirely by setting the
attribute of the display to
>>> cell.display.enabled = False
Particles are rendered by a
ParticleDisplay object. It is always attached to the
ParticleProperty object storing the particle positions (which is the only mandatory particle
property that is always defined). Thus, to change the visual appearance of particles,
we have to access the
Positions particle property in the
>>> pos_prop = node.source.particle_properties.position >>> pos_prop <ParticleProperty at 0x7ff5fc868b30> >>> pos_prop.display <ParticleDisplay at 0x7ff5fc868c40> >>> pos_prop.display.shading = ParticleDisplay.Shading.Flat >>> pos_prop.display.radius = 1.4