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A frequent use case for trimesh rendering is to plot a time-varying mesh where the mesh coordinates are static but the node or triangle values vary over time. Using the current approach the canvas.trimesh has to recompute the intermediate datastructure each time which is very inefficient when you are trying to plot hundreds or even thousands of different timesteps of the same mesh. We either need to rethink the intermediate datastructure or we should find a way to efficiently update the mesh datastructure with the time-varying values.
The text was updated successfully, but these errors were encountered:
Is there a premise here that we can't put all of the feature columns (one per timestep) in the initial tris dataframe that is then converted into the mesh dataframe?
Is there a premise here that we can't put all of the feature columns (one per timestep) in the initial tris dataframe that is then converted into the mesh dataframe?
That could be a solution but isn't necessarily possible since there can be hundreds or even thousands of time steps.
Also, the incoming data can come in batches -- the initial mesh is defined, visualized and adjusted, then given to a simulator that generates arbitrarily many Z values over time for each mesh location. E.g. it could produce a new file every 1000 time steps, which we could stream in continuously using the same mesh object.
A frequent use case for trimesh rendering is to plot a time-varying mesh where the mesh coordinates are static but the node or triangle values vary over time. Using the current approach the canvas.trimesh has to recompute the intermediate datastructure each time which is very inefficient when you are trying to plot hundreds or even thousands of different timesteps of the same mesh. We either need to rethink the intermediate datastructure or we should find a way to efficiently update the mesh datastructure with the time-varying values.
The text was updated successfully, but these errors were encountered: