To load a simulation file from your local computer, select Load File from the menu or use the corresponding button in the toolbar.
OVITO detects the format of the file automatically (see list of supported formats).
Compressed text-based files with a
.gz suffix can be read directly by OVITO.
The imported dataset will appear in the viewports as a new three-dimensional object and also as an entry in the “Data source” section of the pipeline editor, as indicated in the screenshot on the right.
If you select this item in the list, the External File panel appears below the pipeline editor. The tool buttons at the top of that panel let you reload the imported input file in case it has been changed or rewritten outside of OVITO, or you can pick a different file as data source of the data pipeline. Switching the data source file can be useful if you have set up a data analysis pipeline and now would like to apply it to a different simulation dataset.
When launching OVITO from a terminal, you can directly specify a file to load. This works for local and remote files:
ovito /path/filename ovito sftp://hostname/path/filename ovito https://www.website.org/path/filename
You can import several files at once by specifying multiple filenames on the command line. If they all have the same file format, they will be concatenated into a sequence forming an animatable trajectory. If they have different formats, OVITO will detect whether they represent a pair of topology/trajectory files (see next section). If not, they will be inserted as several independent objects into the scene.
OVITO can load trajectories consisting of a series of simulation snapshots. Various scenarios are supported by the software:
- A series of snapshot files:
By default, whenever you import a new simulation file, OVITO tries to detect if the file is part of a numbered sequence of files with similar names in the same directory. To this end, the last number (if any) in the filename you’ve picked is replaced with the wildcard character
*to generate a search pattern, which will subsequently be used to look in the directory for more files belonging to the sequence. For instance, if you imported a file named
anim1c_5000.dump, OVITO will generate the search pattern
anim1c_*.dumpto find more snapshots (e.g.
anim1c_2000.dump, etc). It is possible to manually override the generated file pattern in the input field highlighted in the screenshot or to turn off the automatic search by deactivating the auto-generate option.
- One file containing all trajectory frames:
OVITO automatically detects whether the imported file contains more than one simulation frame and loads all of them as an animation sequence. For some file types, e.g. XYZ and LAMMPS dump, this is indicated by the Contains multiple timesteps checkbox highlighted in the screenshot. Note that OVITO typically keeps only the data of a single frame in memory at a time. Subsequent frames are loaded into memory only when needed, for example if you play back the animation or move the time slider.
- A pair of topology and trajectory files:
Some MD simulation codes use separate files for the topology and the trajectory of a molecular structure. The topology file contains the static definition of atoms, bonds, etc. while the trajectory file contains the computed trajectories and other time-dependent data generated in the MD simulation. In such a case you should pick both files in the file selection dialog and import them simultaneously. OVITO recognizes automatically which of the file is the topology file and which one is the trajectory file based on the following table:
any other supported format
The topology file will be loaded first (e.g. a LAMMPS data file) and a Load trajectory modifier will be inserted into the data pipeline to load the time-dependent atomic positions from the trajectory file (e.g. a LAMMPS dump file). This modifier merges both pieces of information -the static topology and the dynamic trajectory data- into a single animated dataset.
OVITO will display a timeline and a time slider at the bottom of main window if a simulation sequence with more than one frame was loaded. Learn more about OVITO’s animation functions in this section of the manual.
Remote data access¶
OVITO comes with built-in SSH and HTTP(S) clients for accessing files on remote machines. This feature can save you from having to transfer files stored in remote locations, for example on HPC clusters, to your local desktop computer first. To open a file located on a remote host, selectfrom the menu.
The current version of OVITO does not provide a way to browse directories on remote machines. You have to directly specify the full path to the remote file as an URL of the form:
In this URL, replace user with the SSH login name for your remote machine, hostname with the host name of the remote machine, and /path/filename with the full path to the simulation file to load. Similarly, you can let OVITO retrieve a data file from a web server by specifying an URL of the form:
When connecting to the remote machine, OVITO will ask for the login password or the passphrase for the private key to be used for authentication. Once established, the SSH connection is kept alive until the program session ends. OVITO creates a temporary copy of the remote file on the local computer before loading the data into memory to speed up subsequent accesses to all simulation frames. The local data copies are cached until you close OVITO or until you hit the Reload button in the External File panel.
If it exists, OVITO will parse the
~/.ssh/config configuration file in your home directory to
configure the SSH connection.
When running OVITO from the terminal, you can set the environment variable
OVITO_SSH_LOG=1 to activate log output
for the built-in SSH client and diagnose possible connection problems.
Visualizing multiple datasets¶
OVITO has the capability to manage several objects in the same three-dimensional scene. This enables you to import and visualize several datasets together in a single picture as shown in the example on the right. You can also visualize a dataset in several different ways, either side by side or superimposed on each other, using branched data pipelines, which dynamically duplicate the imported data and process each copy in a slightly different way.
The simplest way to visualize multiple datasets in one picture is to invoke the Add to scene option here in order to insert it as an additional object into the existing scene.function from the menu several times to import all datasets into the same scene. When importing the second dataset, OVITO will ask you whether to replace the already loaded dataset or not. Pick the
The pipeline selector widget, located in the toolbar of the window (see screenshot), lists all datasets and other objects that are part of the current scene. Each imported dataset is associated with its own data pipeline. Thus, you can apply different modifiers to each dataset. The data pipeline of the currently selected dataset is the one being displayed and edited in the pipeline editor in the panel on the right.
Positioning datasets in the scene¶
OVITO places imported datasets in a default position relative to the scene’s global coordinate system. Thus, when loading the second dataset into the same scene, it will appear superimposed in the same spatial location as the first dataset, which may not be what you want.
In order to correct this, you can move the individual objects around in the scene and arrange them as needed for your visualization. In the example picture at the top of this page the second dataset has been translated along the x-axis to place it next to the first dataset. To do this, use the Translate mode, which is found in the top toolbar above the viewports:
While the Translate mode is active, you can move objects around in the viewports using the mouse. Alternatively, you can enter the desired position of the selected object numerically using the input fields displayed in the status bar while the Translate mode is selected.
Instead of importing several data files into OVITO, you can also duplicate a dataset within OVITO in order to visualize the same data in different ways, for example by applying different sets of modifiers to each replica of the dataset. See the Clone Pipeline function for more information.