1438779 : Data visualization on MAXWELL

Created: 2024-10-22T15:16:16Z - current status: open

Summary

The user is seeking tools and methods for efficiently visualizing large 3D arrays (2000x2000x2000 voxels) generated from x-ray tomography data on the Maxwell cluster. The current tools used, such as napari and holoviews, have limitations in terms of responsiveness and ease of use on remote machines without a graphical user interface. The user is looking for documentation or recommendations on established data viewers or tools that can handle large datasets efficiently on the Maxwell cluster.

Solution

For visualizing large 3D arrays on the Maxwell cluster, consider the following tools and methods:

  1. ParaView: ParaView is a powerful open-source data analysis and visualization application. It is designed to handle large datasets and can be used remotely. ParaView can be accessed via the Jupyter notebooks on the Maxwell cluster, which might provide a more responsive experience compared to other tools.

  2. VisIt: VisIt is another open-source visualization tool that supports large datasets and can be used on remote machines. It offers a wide range of visualization techniques and can be integrated with Jupyter notebooks.

  3. VMD (Visual Molecular Dynamics): VMD is a molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3D graphics and built-in scripting.

  4. Remote Desktop Solutions: Consider using remote desktop solutions like X2Go or VNC to access a graphical interface on the Maxwell cluster. This can allow you to use tools like napari more effectively.

  5. Data Compression and Preprocessing: Before visualizing, consider compressing or preprocessing the data to reduce its size. Tools like zarr or h5py can help manage large datasets more efficiently.

  6. Cluster-Specific Documentation: Check the Maxwell cluster documentation for any specific tools or methods recommended for visualization. The cluster might have optimized setups or scripts for handling large datasets.

References

None