Introduction

The Python programming interface gives you programmatic access to most of OVITO’s program features. Using Python scripts, you can perform many of the things you know from the interactive user interface (and even more):

  • Import from and export data to simulation files
  • Use modifiers to set up a data processing pipeline
  • Render images or movies of the three-dimensional scene
  • Access result data computed by an analysis pipeline
  • Write your own data modification functions

OVITO’s data pipeline architecture

If you have worked with OVITO’s graphical user interface before, you should already be familiar with the data pipeline concept: After loading a simulation file into the program, you typically build up a data pipeline by applying one or more modifiers that act on the data in some way. The result of this sequence of modifiers is dynamically computed and displayed in the interactive viewports of OVITO.

The Python interface of OVITO let’s you build data pipelines in the same way by setting up a sequence of modifiers and configuring their parameters. The data pipeline is represented by an instance of the Pipeline Python class, which manages the list of applied modifiers. Furthermore, a data pipeline is always associated with some kind of data source, which is a separate object providing or producing the input data that will be passed to the pipeline’s first modifier. Typically, the data source is an instance of the FileSource class, which is responsible for loading the data from an external file on disk.

../_images/Pipeline_overview.svg

If you would like to visualize the output of a Pipeline, you may place the pipeline into the scene, i.e. the three-dimensional space that is visible in rendered images. Only pipelines that have explicitly been inserted into the current scene by calling their add_to_scene() method will show up in rendered images or in OVITO’s interactive viewports. While this happens automatically in the graphical user interface of OVITO, you need to do it explicitly when using the Python scripting interface, because visualization of data is an optional feature and may not always be desired.

The scene, including all pipelines and other state information that would get saved along in a .ovito file, is represented by the Scene Python class. A script always runs in the context of a current Scene instance, which is accessible through the ovito.scene global variable.

Importing data from disk

You typically create a new Pipeline by importing an input data file from disk using the ovito.io.import_file() function:

>>> from ovito.io import import_file
>>> pipeline = import_file("simulation.dump")

This high-level function will create a new Pipeline instance and wire it to a FileSource, which will take care of loading the data from the given input file. This data source object is accessible through the pipeline’s source field:

>>> print(pipeline.source)
<FileSource at 0x7f9ea70aefb0>

If desired, the FileSource can be reconfigured to load a different input file, allowing you to replace the original input of the pipeline. This is useful if you intend to batch process a number of simulation files, reusing the same processing pipeline. The File I/O section provides more information on importing and exporting data using the scripting interface.

Applying modifiers

Initially, the Pipeline created by the import_file() function contains no modifiers. That means it will simply output the original input data loaded by the FileSource. We can change this by inserting some modifiers into the pipeline’s modifiers list:

>>> from ovito.modifiers import *
>>> pipeline.modifiers.append(ColorCodingModifier(property = 'Potential Energy'))
>>> pipeline.modifiers.append(SliceModifier(normal = (0,0,1)))

Modifiers are constructed by instantiating one of the built-in modifier classes, which are all found in the ovito.modifiers Python module. Note how a modifier’s parameters can be initialized in two different ways:

Note

When creating a new object such as an OVITO modifier, it is possible to directly initialize its parameters by passing keyword arguments to the constructor function. Thus

pipeline.modifiers.append(CommonNeighborAnalysisModifier(cutoff=3.2, only_selected=True))

is equivalent to assigning the parameter values one by one after constructing the object:

modifier = CommonNeighborAnalysisModifier()
modifier.cutoff = 3.2
modifier.only_selected = True
pipeline.modifiers.append(modifier)

Obviously, the first method of initializing the parameters is more convenient and is the recommended way whenever the parameter values are known at construction time of the object.

Keep in mind that it is possible to change the parameters of modifiers in a pipeline at any time, or to remove modifiers from a pipeline again. This feature is useful if you want to sequentially process the input data in multiple different ways. Alternatively, you can also set up multiple data pipelines, all sharing the same data source or even some of the modifiers. The Data pipelines section provides more information on working with data pipelines and modifiers.

Exporting data to disk

Once a Pipeline has been set up, you can pass it to the ovito.io.export_file() function to let OVITO compute the result of the pipeline and write it to an output file in the given format:

>>> from ovito.io import export_file
>>> export_file(pipeline, "outputdata.dump", "lammps/dump",
...    columns = ["Position.X", "Position.Y", "Position.Z", "Structure Type"])

The export_file() function takes the output filename and the desired format as arguments, in addition to the Pipeline generating the data to be exported. Furthermore, depending on the selected format, additional keyword arguments such as the list of particle properties to export must be provided. See the documentation of the export_file() function and this section for more information.

Accessing computation results

Instead of directly piping the computation results to an output file, you can also request the pipeline to return a DataCollection object, which represents the output data leaving the pipeline:

>>> data = pipeline.compute()

The compute() method performs two things: It first requests the input data from the pipeline’s data source. Then, it let’s all modifiers of the pipeline act on the data, one by one. The final data state is returned to the caller as a DataCollection, which essentially is a heterogeneous container storing a set of data objects that each represent different parts of a dataset:

>>> data.objects
[SimulationCell(), Particles(), AttributeDataObject(), AttributeDataObject()]

In the example above, the DataCollection.objects list contains a SimulationCell object, a Particles object and several global attribute objects, which were either loaded from the source data file of the pipeline or which were generated on the fly by modifiers in the pipeline.

The DataCollection class provides various fields for accessing particular kinds of data objects, for example the cell field returns the SimulationCell object storing the simulation cell vectors and position of the cell origin as a matrix:

>>> print(data.cell[...])
[[ 148.147995      0.            0.          -74.0739975 ]
 [   0.          148.07200623    0.          -74.03600311]
 [   0.            0.          148.0756073   -74.03780365]]

The particles field returns the Particles container object, which manages all particle properties:

>>> print(data.particles.positions[...])
[[ 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 ]]

The Particles section provides more information on this topic.

Accessing a pipeline’s input data

Sometimes it may be necessary to access the original data that enters a pipeline. The input data is read from the input file by the pipeline’s FileSource. This object also provides a compute() method returning a DataCollection:

>>> input_data  = pipeline.source.compute()
>>> output_data = pipeline.compute()

Rendering images and movies

As mentioned earlier, to visualize data it is necessary to add the Pipeline to the three-dimensional scene. This is done by invoking its add_to_scene() method, typically right after creating a new pipeline:

>>> pipeline = import_file("simulation.dump")
>>> pipeline.add_to_scene()

Furthermore, to render an image or a movie, a Viewport object is required, which defines the viewpoint from which the scene is seen:

>>> from ovito.vis import Viewport
>>> 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)

The Viewport’s parameters control the position and orientation of the virtual camera, the type of projection (perspective or parallel), and the field of view (FOV) angle. To automatically position the camera such that all objects in the scene are fully visible, you may call the viewport’s zoom_all() method. Finally, the Viewport.render_image() method must be called to render an image and save it to disk:

>>> vp.render_image(filename="myimage.png", size=(800,600))

Note that as part of the rendering process, all pipelines that have been inserted into the current scene will automatically be evaluated.

Further reading

The following sections provide more information on various aspects of OVITO’s Python scripting interface: