Visualization of the simulation without visual interface

Question

Hello

I have a question, is it possible to visualize the simulation of electrophysiology without using the visual interface of the meshalyzer?

I want to create a pipeline that will automatically run a wave propagation calculation and then output the result as a series of images. This can easily be done in the visual interface of the meshalyzer, but I would like to do without this intermediate step. Perhaps this can be implemented using some python library, but I couldn't find out how.

Thank you in advance!

Answer 1

meshalyzer supports offscreen rendering, which is described in chapter 8 of its manual:

https://opencarp.org/documentation/meshalyzer-manual

Together with interactively prepared state files, this could be an option for you.

Alternatively, you can also convert the output to VTK and use ParaView's offscreen rendering options.

Comments

Thank you so much, after some efforts everything worked out!

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Hi Axel,

Can you please elaborate how I can convert the output to vtk format or get the output immediately in the vtk format? I cannot find any tutorial on this online.

Best regards,

Marie

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Meshtool is a good option for this: https://bitbucket.org/aneic/meshtool/src/master/

meshtool convert 

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I managed to get the mesh in .vtk format for visualization in Paraview. However, how can I visualize the .igb data file on this mesh using Paraview? Would there be a tutorial on this?

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There is one being developed as an outcome of last month's user meeting:

https://git.opencarp.org/openCARP/experiments/-/merge_requests/53/diffs

Not rendered nicely on the web page yet but I hope it's useful already.

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That worked, thank you Axel!

In the meantime I also found the class carputils.carpio.igb.IGBFile with which an .igb file can be loaded in Python. The simulation data is then in the IGBFile.data() property, and after resizing using

IGBFile.data().reshape(IGBFile.header()['t'], IGBFile.header()['x'], IGBFile.header()['y'], IGBFile.header()['z'])

the data can be visualized using matplotlib. I hope this might be useful additional information to people with a similar question.