`ovito.io`

This module provides two high-level functions for reading and data files:

import_file()

export_file()

ovito.io.export_file(data, file, format, **params)

Writes data to an output file. See section Data export for an overview.

Parameters

data – The object to be exported. See available options below.
file (str|os.PathLike) – The output file path.
format (str) – The kind of file to write. See available options below.
params (Any) – Optional keyword arguments depending on the selected format.

Exportable objects

The following kinds of Python objects can be passed to this function:

Pipeline: If you provide a data pipeline, the dynamically computed output of the pipeline gets exported. Since pipelines support evaluation at different animation times, a sequence of frames can be exported by passing the extra keyword argument multiple_frames=True, see below.
DataCollection: If you provide a data collection, the static snapshot stored in the collection is exported. Note that it depends on the selected file format which objects from the data collection will be exported - or you may have to provide extra function arguments to specify which data object(s) to export.
DataObject: If you provide a single data object, e.g. a DataTable or SurfaceMesh, just that one object gets exported. The behavior is similar to providing a DataCollection containing a single data object.
None: This exports the entire visualization scene, i.e. the output of all data pipelines in the current scene (see ovito.Scene.pipelines list). This option is currently supported only by the the POV-Ray exporter, which generates a scene description file.

Additional keyword parameters, as documented below, let you control which aspects of a dataset will be written to the output file. For instance, for some file formats the columns keyword controls the set of particle properties to be exported.

Output filename

The parameter file specifies the path of the output file. If the filename ends with the suffix .gz, the output file will be compressed using the zlib library to save disk space (works only for text-based file formats).

If a wildcard “*” character appears in the filename, then one file per animation frame is written and the “*” character is replaced with the frame number. This feature is typically used in conjunction with the multiple_frames=True option, see below.

Output formats

The parameter format selects the kind of file to write:

Format string	Description
`"txt/attr"`	Export global attributes to a text file (see below)
`"txt/table"`	Export a `DataTable` to a text file
`"lammps/dump"`	LAMMPS text-based dump format
`"lammps/data"`	LAMMPS data format
`"imd"`	IMD format
`"vasp"`	POSCAR format
`"xyz"`	XYZ format
`"fhi-aims"`	FHI-aims format
`"gsd/hoomd"`	GSD format used by the HOOMD simulation code
`"netcdf/amber"`	Binary format for MD data following the AMBER format convention
`"vtk/trimesh"`	ParaView VTK format for exporting `SurfaceMesh` objects
`"vtk/disloc"`	ParaView VTK format for exporting `DislocationNetwork` objects
`"vtk/grid"`	ParaView VTK format for exporting `VoxelGrid` objects
`"ca"`	Text-based format for storing dislocation lines
`"povray"`	POV-Ray scene format

Depending on the selected output format, additional keyword arguments may be passed to export_file() as documented in the following sections.

File columns

For the output formats lammps/dump, xyz, imd, and netcdf/amber you must specify the set of particle properties to export by providing the columns keyword parameter:

export_file(pipeline, "output.xyz", "xyz", columns =
  ["Particle Identifier", "Particle Type", "Position.X", "Position.Y", "Position.Z"])

In case of vector properties, one of the property’s component_names must be appended to the base name, e.g. Position.Z or Atomic Strain.XY. See the list of standard particle properties.

Tip

If you are not sure which particle properties are available for export, you can print the list of properties produced by your current data pipeline to the console:

print(pipeline.compute().particles)

Exporting several simulation frames

By default, only the current animation frame (frame 0 by default) is exported. To export a different trajectory frame, pass the frame keyword parameter to the export_file() function. Alternatively, you can export all frames of the animation sequence at once by specifying multiple_frames=True. More refined control is possible through the keyword arguments start_frame, end_frame, and every_nth_frame.

Some file formats such as lammps/dump, xyz, gsd/hoomd or netcdf/amber can store all frames of the exported trajectory in a single output file. For other formats, or if you prefer one file per frame, you must pass a filename pattern to export_file(). The specified output filename must contain a * wildcard character as in the following example, which will be replaced with the animation frame number during export:

export_file(pipeline, "output.*.dump", "lammps/dump", multiple_frames=True)

This is equivalent to an explicit for-loop that exports the frames one by one to a series of files:

for i in range(pipeline.source.num_frames):
    export_file(pipeline, f"output.{i}.dump", "lammps/dump", frame=i)

Floating-point number precision

For text-based file formats, you can specify the desired formatting precision for floating-point values using the precision keyword parameter. The default output precision is 10 digits; the maximum is 17.

LAMMPS atom style

When writing files in the lammps/data format, the LAMMPS atom style “atomic” is used by default. To generate a data file with a different LAMMPS atom style, specify it using the atom_style keyword parameter:

export_file(pipeline, "output.data", "lammps/data", atom_style="bond")
export_file(pipeline, "output.data", "lammps/data", atom_style="hybrid", atom_substyles=("template", "charge"))

If at least one ParticleType of the exported model has a non-zero mass value, OVITO writes a Masses section to the LAMMPS data file. You can suppress it by passing omit_masses=True to the export function.

The option ignore_identifiers=True replaces any existing atom IDs (particle property Particle Identifier) with a new contiguous sequence of numeric IDs during export. The option consecutive_type_ids=True replaces existing numeric type IDs of particle/bond/angle/dihedral/improper types with new values during export. The option export_type_names=True writes the names of OVITO particle/bond/angle/dihedral/improper types to the data file as LAMMPS type maps.

VASP (POSCAR) format

When exporting to the vasp file format, OVITO will output atomic positions and velocities in Cartesian coordinates by default. You can request output in reduced cell coordinates by specifying the reduced keyword parameter:

export_file(pipeline, "structure.poscar", "vasp", reduced=True)

Global attributes

The txt/attr file format allows you to write global quantities computed by the data pipeline to a text file. For example, here is how you export the number of FCC atoms identified by a CommonNeighborAnalysisModifier as a function of simulation time to a simple text file:

export_file(pipeline, "data.txt", "txt/attr",
    columns=["Timestep", "CommonNeighborAnalysis.counts.FCC"],
    multiple_frames=True)

Tip

If you are not sure which global attributes are available for export, you can print the list of attributes produced by your current data pipeline to the console:

print(pipeline.compute().attributes)

ovito.io.import_file(location, **params)

Imports data from an external file.

This Python function corresponds to the Load File menu command in OVITO’s user interface. The format of the imported file is automatically detected (see list of supported formats). Depending on the file’s format, additional keyword parameters may be required to specify how the data should be interpreted. These keyword parameters are documented below.

Parameters

location (str|os.PathLike|Sequence[str]) – The file(s) to import. This can be a local file path or a remote sftp:// or https:// URL.
params (Any) – Additional keyword parameters to be passed to the file reader. See below and the documentation of individual file readers for format-specific options.

Returns

The new Pipeline that has been created for the imported data.

Return type

Pipeline

The function creates and returns a new Pipeline object, which uses the contents of the external data file as input. The pipeline will be wired to a FileSource, which reads the input data from the external file and passes it on to the pipeline. You can access the data by calling the Pipeline.compute() method or, alternatively, FileSource.compute() on the data source. As long as the new Pipeline contains no modifiers yet, both methods will return the same data.

Note that the Pipeline is not automatically inserted into the three-dimensional scene. That means the loaded data won’t appear in rendered images or the interactive viewports of OVITO by default. For that to happen, you need to explicitly insert the pipeline into the scene by calling its add_to_scene() method if desired.

Furthermore, note that you can re-use the returned Pipeline if you want to load a different data file later on. Instead of calling import_file() again to load another file, you can use the pipeline.source.load(...) method to replace the input file of the already existing pipeline.

File data columns

When importing simple-format XYZ files or legacy binary LAMMPS dump files, the mapping of file columns to particle properties in OVITO must be specified using the columns keyword parameter:

pipeline = import_file('file.xyz', columns =
    ['Particle Identifier', 'Particle Type', 'Position.X', 'Position.Y', 'Position.Z'])

The number of column strings must match the actual number of data columns in the input file. See this table for standard particle property names. Alternatively, you can specify user-defined names for file columns that should be read as custom particle properties by OVITO. For vector properties, the component name must be appended to the property’s base name as demonstrated for the Position property in the example above. To ignore a file column during import, use None as entry in the columns list.

For LAMMPS dump files or extended-format XYZ files, OVITO automatically determines a reasonable column-to-property mapping, but you may override it using the columns keyword. This can make sense, for example, if the file columns containing the particle coordinates do not follow the standard naming scheme x, y, and z (as is the case when importing time-averaged atomic positions computed by LAMMPS, for example).

Frame sequences

OVITO automatically detects if the imported file contains multiple data frames (timesteps). Alternatively (and additionally), it is possible to load a sequence of files in the same directory by using the * wildcard character in the filename. Note that * may appear only once, only in the filename component of the path, and only in place of numeric digits. Furthermore, it is possible to pass an explicit list of file paths to the import_file() function, which will be loaded as an animatable sequence. All variants can be combined. For example, to load two file sets from different directories as one consecutive sequence:

import_file('sim.xyz')     # Load all frames contained in the given file
import_file('sim.*.xyz')   # Load 'sim.0.xyz', 'sim.100.xyz', 'sim.200.xyz', etc.
import_file(['sim_a.xyz', 'sim_b.xyz'])  # Load an explicit list of snapshot files
import_file(['dir_a/sim.*.xyz',
             'dir_b/sim.*.xyz']) # Load several file sequences from different directories

The number of frames found in the input file(s) is reported by the num_frames attribute of the pipeline’s FileSource You can step through the frames with a for-loop as follows:

from ovito.io import import_file

# Import a sequence of files.
pipeline = import_file('input/simulation.*.dump')

# Loop over all frames of the sequence.
for frame_index in range(pipeline.source.num_frames):    

    # Calling FileSource.compute() loads the requested frame
    # from the sequence into memory and returns the data as a new
    # DataCollection:
    data = pipeline.source.compute(frame_index)

    # The source path and the index of the current frame
    # are attached as attributes to the data collection:
    print('Frame source:', data.attributes['SourceFile'])
    print('Frame index:', data.attributes['SourceFrame'])
    
    # Accessing the loaded frame data, e.g the particle positions:
    print(data.particles.positions[...])

LAMMPS atom style

When loading a LAMMPS data file, the atom style may have to be specified using the atom_style keyword parameter unless the file contains a hint string, which allows OVITO to detect the style automatically. Data files written by the LAMMPS write_data command or by OVITO contain such a hint, for example. For data files not containing a hint, the atom style must be specified explicitly as in these examples:

import_file('full_model.data', atom_style = 'full')
import_file('hybrid_model.data', atom_style = 'hybrid', atom_substyles = ('template', 'charge'))

Particle ordering

Particles are read and stored by OVITO in the same order as they are listed in the input file. Some file formats contain unique particle identifiers or tags which allow OVITO to track individual particles over time even if the storage order changes from frame to frame. OVITO will automatically make use of that information where appropriate without touching the original storage order. However, in some situations it may be desirable to explicitly have the particles sorted with respect to the IDs. You can request this reordering by passing the sort_particles=True option to import_file(). Note that this option is without effect if the input file contains no particle identifiers.

Topology and trajectory files

Some simulation codes write a topology file and separate trajectory file. The former contains only static information like the bonding between atoms, the atom types, etc., which do not change during a simulation run, while the latter stores the varying data (primarily the atomic trajectories). To load such a topology-trajectory pair of files, first read the topology file with the import_file() function, then insert a LoadTrajectoryModifier into the returned Pipeline to also load the trajectory data.

Explicit file format specification

Normally, OVITO detects the format of the imported file(s) automatically. In rare cases, however, the auto-detection mechanism may fail. Then you can explicitly specify the file format to assume using the input_format keyword parameter. It must be set to one of the following supported format identifiers: "ca", "castep/cell", "castep/md", "cfg", "cif", "dlpoly", "fhi-aims", "fhi-aims/log", "galamost", "gaussian/cube", "gro", "gsd/hoomd", "imd", "lammps/data", "lammps/dump", "lammps/dump/bin", "lammps/dump/grid", "lammps/dump/local", "mmcif", "netcdf/amber", "obj", "oxdna", "paradis", "parcas", "pdb", "quantumespresso", "reaxff/bonds", "stl", "vasp", "vtk/legacy/mesh", "vtk/pvd", "vtk/vti/grid", "vtk/vtm", "vtk/vtp/mesh", "vtk/vtp/particles", "vtk/vts/grid", "xsf", "xtc", "xyz".

ovito.io

`ovito.io`