ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaViews batch processing capabilities.
ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of exascale size as well as on laptops for smaller data.
ParaView is available via the module system on Tetralith and Sigma. For more information about available versions, please see the Tetralith and Sigma Software list.
Version | NSC Module | Version Info |
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5.11.2 | ParaView/5.11.2-hpc1-bdist | regular, interactive version with GUI |
5.11.2 | ParaView/5.11.2-osmesa-hpc1-bdist | special version for batch jobs, without GUI |
5.12.0 | ParaView/5.12.0-hpc1-bdist | regular, interactive version with GUI |
5.12.0 | ParaView/5.12.0-osmesa-hpc1-bdist | special version for batch jobs, without GUI |
The specification osmesa refers to offscreen rendering using mesa (mesa=software rendering, no GPU acceleration). Offscreen rendering means, that no X-terminal will be opened. It is intended for batch processing without any graphical user interface.
Load the paraview module corresponding to the version you want to use, e.g
module load ParaView/5.11.2-hpc1-bdist
This will add the paraview application to your search path.
The following table summarizes how to start ParaView with GPU or software (mesa) rendering.
Rendering | ParaView Version | Command |
---|---|---|
GPU | 5.11.2-hpc1-bdist | vglrun paraview |
mesa | 5.11.2-hpc1-bdist | paraview |
When running the ParaView GUI, NSC recommends using ThinLinc to access Tetralith. For more information on how to use ThinLinc, please see: Running graphical applications using ThinLinc
For complex tasks, ParaView offers the possibility to run python scripts. If you want to run python scripts in batch mode, without running the graphical user interface, you have to use a special ParaView version. This version is denoted as “osmesa”. OSMesa means “off-screen mesa”. This version does not support an interactive GUI, but it is intended for batch mode operations. The regular ParaView version does not work in batch mode, as it expects an interactive graphical user interface.
Load the correct ParaView module for batch jobs:
module load ParaView/5.11.2-osmesa-hpc1-bdist
Call your python script, using the pvbatch command:
mpprun pvbatch <your python script.py>
In this case, you do not need the option –use-offscreen-rendering, as it is a special ParaView version.
ParaView needs a *.foam file in your OpenFOAM case directory, which indicates that the data is saved in the OpenFOAM format, e.g. case.foam. Depending if your data is saved as a decomposed case, or a reconstructed case, you have to choose the correct Case Type, when opening the file case.foam: Decomposed Case or Reconstructed Case. If you try to read your decomposed data with Case Type = Reconstructed Case, then the field Cell Arrays will be empty. The solution data will not be loaded, due to the wrong Case Type.
OpenFOAM: Decomposed Case | OpenFOAM: Reconstructed Case |
Choose your variables: Under Cell Arrays, you can select the variables that you want to load. For large datasets, it is recommended only to load the necessary variables. ParaView loads your data, after you have clicked “Apply”.
Collated file format: OpenFOAM also offers the collated file format, that has been introduced in OpenFOAM 7 and OpenFOAM v1712. It reduces the number of files for parallel computations. It seems that ParaView does not support the collated file format so far. You probably have to convert it into a different format to visualize your data with ParaView.
ParaView includes a utility to record your ParaView commands and automatically generate Python scripts. The recorded Python script can be saved, edited and executed by ParaView. This way you can automate recurring tasks, or embed the Python script into more complex work flows.
To record your ParaView session, you have to start a so called Trace. In the top menu, select Tools > Start Trace. You will be prompted to select your trace options. Under “Properties To Trace On Create” you can choose “any modified properties”. After pressing “ok”, all steps of your current ParaView session will be recorded. To stop your recording, go to the top menu and select Tools > Stop Trace. A new window will open, that includes the recorded Python script. In this new window you can save the script by selecting File > Save As…
The following table shows the different ParaView menus to start, stop and save the Python trace file:
The Python trace file contains a list of variables, that ParaView reads from disk:
casefoam.CellArrays = ['T', 'U', 'p' ...]
If you did not adjust Cell Arrays, when opening your file in ParaView, the list may contain variables that you do not need for your actual visualization. This means, ParaView requires more memory to store all variables and longer time to read the variables from disk than necessary. This is particularly important when reading large data sets or if you want to make animations with a large number of timesteps/images. We recommend to modify the field CellArrays, so that it only contains the required variables. For example, if you only want to visualize the pressure ‘p’, then you can modify the Python script as following:
#casefoam.CellArrays = ['T', 'U', 'p' ...]
casefoam.CellArrays = ['p']
here we commented (#) the original variable declaration and we added a new variable declaration that only contains the variable ‘p’.
To create an animation of time dependent data, select in the main menu File > Save Animation. A new dialog box will allow you to specify the file name of the animation. We want to export each time step as an individual image. Therefore, we typically choose the png-image format. The individual frame number for each time step will be automatically added to your file name when the animation is created.
Next, the dialog box Save Animation Options will appear. By default, it will show the standard options to export an animation that contains all timesteps. This is the straight forward way to create an animation. For each timestep, ParaView reads your data from hard disk, creates the output image, and saves the image to disk.
Save Animation | Save Animation Options: Basic |
If you have large datasets and many timesteps, the basic animation workflow may take some time. It can take up to several hours, dependig on your data. Next, we will discuss how to create animations in parallel, running several instances of ParaView simultaneously.
To create animations in parallel, we record all steps of the visualization work flow as well as all steps to save the animation in a Python trace file. The recorded Python script serves as a basic template. We are then going to execute several instances of ParaView in batch mode. Each instance of ParaView will render a different range of timesteps. This way, we have a parallel workflow. Running multiple instances of ParaView, as well as the adjustment of the basic template to accomodate different time steps is automated by a script.
Next, we want to execute the Python trace file with ParaView. We are going to launch several instances of ParaView, where each instance processes a different range of animation frames. We provide the following scripts that do the parallel processing for you:
Download</th> | Description</th>
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Animation Script, interactive</td>
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Standard bash script that can be executed in an interactive session
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Animation Script, slurm batch script</td>
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Slurm batch script, submit: sbatch make_paraview_animation_slurm.sh
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