Overview¶
OVITO’s scripting interface provides access to most of program features. Using Python scripting, 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
- Export data to a file
- 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
But first let’s take a look at some essential concepts of OVITO’s data model and the 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 (i.e. the modifiers and a data source) and data objects, which carry the data that is being processed by the modifiers. The data objects enter the modification pipeline, get modified by one or more modifiers, or are newly produced (e.g. as a result of a computation). We first discuss the objects that constitute the modification pipeline.
Data sources, modifiers, and more¶
A modification pipeline is always fed by some data source, which is an object
that provides or generates the input data entering a modification pipeline. OVITO currently knows two types of
data sources: FileSource
and DataCollection
.
The FileSource
class is the data source type commonly 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 ObjectNode.source
property. The modification pipeline is simply a list of Modifier
objects and is
is accessible through the ObjectNode.modifiers
property.
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
a .ovito
file (e.g. current render settings, viewport cameras, etc.), comprise the so-called DataSet
.
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 (dataset.scene_nodes
),
the current animation settings (dataset.anim
), the four
viewports in OVITO’s main window (dataset.viewports
), and more.
Loading data and applying modifiers¶
A new instance of the ObjectNode
class is automatically created whenever you import a file
using the ovito.io.import_file()
function:
>>> 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 actual data
from the given file) and assigns it to the ObjectNode.source
property.
We can now start populating the node’s modification pipeline with some modifiers by appending them
to the ObjectNode.modifiers
list:
>>> from ovito.modifiers import *
>>> node.modifiers.append(SelectExpressionModifier(expression="PotentialEnergy<-3.9"))
>>> node.modifiers.append(DeleteSelectedParticlesModifier())
A modifier is constructed by calling the constructor of one of the modifier classes, which are
all found in the ovito.modifiers
module. Note how a modifier’s parameters can be initialized in two different ways:
Note
When constructing a new object (e.g. 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)
Obviously the first way of initializing the object’s parameters is more convenient and should be used whenever the parameter values are known at construction time.
After the input data has been loaded and the modification pipeline is populated with some modifiers, we can basically do three different things: (i) export the computation results to a file, (ii) render an image of the data, (iii) or directly access the pipeline output from the script. Keep reading, we’ll now give a quick overview on these tasks and go into details in the later sections.
Exporting data to a file¶
Exporting the data to a file that is produced by the modification pipeline is simple;
we call 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 ObjectNode
whose pipeline results are to be exported.
It is followed by the output filename and the desired output format.
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
of the manual for more information on the supported output formats and additional options.
Rendering images¶
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 DataSet
;
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:
>>> node.add_to_scene()
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:
>>> vp.render(settings)
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 Viewport
and 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:
>>> node.compute()
The compute()
method ensures that all modifiers in the pipeline of the node
have been successfully evaluated. Note that the render()
and
export_file()
functions implicitly call compute()
for us. But now, since we want to directly access the pipeline results, we have to explicitly request
an evaluation of the modification pipeline.
The node caches the results of the last pipeline evaluation in the ObjectNode.output
field
in the form of a DataCollection
:
>>> 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 ]]
See the ovito.data
module for a list of data object types that may occur in a DataCollection
.
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 FileSource
,
which is itself a DataCollection
:
>>> 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.
Every data object with a visual representation in OVITO is associated with a matching Display
object. The display object is stored in the data object’s display
property. For example:
>>> 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>
The 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 enabled
attribute of the display to False
:
>>> 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 DataCollection
:
>>> 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