Jing, I'm having a little trouble conceptualizing exactly how you are planning to use it, but I suspect you are going to run into several problems because it sounds like you are breaking some of the assumptions of VTK and ParaView.
First, I'm not sure how you could attach an array describing 2x2x2 values to 4x4x4 blocks. There is no convention in VTK to specify a field arrangement other than 1 to 1. All filters will assume the array is 4x4x4, which will cause bad data access and could potentially crash ParaView. Second, trying to modify an array interactively in the view and then access it through filters in the pipeline is not going to work very well. The pipeline will not catch the changes and the changed values will not appear in the visualization. ParaView additionally has update suppressors to prevent updates that were not modified by the ParaView system. I suspect that for whatever it is you are trying to do to work right you will need to create a custom view or representation that internally handles this numpy array. -Ken From: jlih <[email protected]<mailto:[email protected]>> Date: Monday, December 1, 2014 at 9:03 AM To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Subject: [EXTERNAL] [Paraview] Data updates in Paraview+MPI Dear all, I am using the Paraview with MPI enabled to visualize a NetCDF dataset and need some suggestions on developing the following application. If I want to modify the NetCDF array based on the data values from another numpy array (not in NetCDF format), do I need to store the numpy array in a NetCDF in every computing node with MPI installed? The dimensions of the numpy array are not the same as the NetCDF array (e.g., numpy 2*2*2; NetCDF 4*4*4, one element from the numpy array corresponds to 8 elements in the NetCDF array ). Also, the numpy array is updated during the interactive manipulation of the view. Whenever the view changes (e.g., zoom), the values are updated. If I store the numpy array as files, I am not sure how much communication overhead will be introduced. Is it possible to store the numpy array in memory and update the array on the fly? I'm not sure how the shared memory works in this case.. Any suggestions are highly appreciated. Thanks in advance. Jing
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