# `ovito.io`

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

> - `import_file()`

> - `export_file()`

Furthermore, it contains the base class for custom file readers:

> - `FileReaderInterface`

class ovito.io.FileReaderInterface

  Base: `traits.has_traits.HasStrictTraits`

  Abstract base class for Python-based file readers.

  When deriving from this class, you must implement the methods `detect()` and `parse()`. Implementing the `scan()` method is only necessary for file formats that can store more than one trajectory frame per file.

  New in version 3.9.0.

  abstract detect(filename)

    This method is called by the system to let the file reader inspect the given file and determine whether it is in a format that can be read by the class’ `parse()` method.

    For best performance, your implementation of this method should try to determine as efficiently as possible whether the given file uses a supported format by reading and inspecting just the file’s header – not the entire file.

    Parameters:

      filename (str) – The local filesystem path of the input file.

    Returns:

      `True` if this file reader would like to load the given file. The system will invoke `scan()` or `parse()` next. `False` if this class cannot handle the file’s format.

  abstract parse(data, *, filename, url, frame_index, frame_info, is_new_file, **kwargs)

    The main work function, which is called by OVITO to have the file reader parse a dataset or one trajectory frame from the given file.

    Parameters:

      - data (DataCollection) – Container in which the file reader should store any data it loads from the file.

      - filename (str) – The local filesystem path the system is requesting to load.

      - url (str) – The URL the file originally came from. This may be a remote location (e.g. https:// or sftp:// URL). In any case, the file reader should use filename to access the local copy of the file.

      - frame_index (int) – The zero-based index of the frame to load from the trajectory.

      - frame_info (Any) – If the input file contains multiple trajectory frames, this argument is the parser-specific indexing information obtained by your `scan()` method implementation, helping `parse()` seek to the requested frame in the file (e.g. a byte offset or line number).

      - is_new_file (bool) – Indicates that the user newly opened this trajectory in the OVITO application and the file reader should discard any existing objects in the data collection (e.g. leftovers from another file reader). Will be `True` only during the first call to `parse()` after your file reader was newly associated with a `FileSource`.

      - kwargs (Any) – Any further arguments that may be passed in by the system. This parameter should always be part of the function signature for forward compatibility with future versions of OVITO, which may provide additional keyword arguments.

  abstract scan(filename, register_frame)

    Optional method called by the system to let the file reader discover and index the trajectory frames stored in the given file. Only file readers for formats that allow storing multiple frames per file need to implement this method.

    Parameters:

      - filename (str) – The local filesystem path of the input file to scan.

      - register_frame (Callable) – A callback function provided by the system, which must be called exactly once for every trajectory frame found in the input file during the scan process.

    The register_frame() callback function has the following signature:

        register_frame(frame_info: Any = None, label: Optional[str] = None)

    The frame_info value and the label text describe one trajectory frame discovered by your scan method and the system stores the information until later when `parse()` is invoked by the system to request loading of an individual frame. Then the frame_info value will be available for the file reader to quickly seek to the requested frame in the input file and load its contents.

    See Example FR1: Custom file reader loading particle properties and the simulation cell for a simple implementation of the `scan()` method. See Example FR2: Optimizing the performance of Example FR1 for a well-behaved implementation yielding best performance.

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 output formats lammps/dump, xyz, imd, and netcdf/amber you must specify the list of particle properties to be exported by providing the `columns` keyword argument:

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

  When exporting a vectorial property, you can specify a particular vector component by appending it as a suffix to the base name, e.g. `"Position.Z"` or `"Atomic Strain.XY"` (see `Property.component_names`). If you do not specify a component, all components of the vector property will be exported in the form of several consecutive data columns (since OVITO 3.8).

  Tip

  If you are not sure which particle properties are available for export, you can print the list of particle properties to the console as follows:

      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 - including registered user-defined file readers). 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 using the optional `input_format` keyword parameter. It must one of the following supported format identifiers: `"ca"`, `"castep/cell"`, `"castep/md"`, `"cfg"`, `"cif"`, `"dcd"`, `"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"`, `"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"`. Alternatively, `input_format` may specify a `FileReaderInterface` class or object.

  See also

  - ASE database reader

  - ASE trajectory reader

  - CFG (AtomEye) file reader

  - Gaussian Cube file reader

  - GSD/HOOMD file reader

  - LAMMPS data file reader

  - LAMMPS dump file reader

  - LAMMPS dump grid file reader

  - LAMMPS dump local file reader

  - ReaxFF file reader

  - XYZ file reader