Download paraview/engine/task_description.txt from SciVisAgentBench/SciVisAgentBench-tasks: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
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https://huggingface.co/datasets/SciVisAgentBench/SciVisAgentBench-tasks/resolve/main/paraview/engine/task_description.txt
- Command line
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hf download hf://datasets/SciVisAgentBench/SciVisAgentBench-tasks/paraview/engine/task_description.txt
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curl -L -o task_description.txt https://huggingface.co/datasets/SciVisAgentBench/SciVisAgentBench-tasks/resolve/main/paraview/engine/task_description.txt
1.3 kB
| Task: | |
| Load the vortex dataset from "engine/data/engine_256x256x128_uint8.raw", the information about this dataset: | |
| engine (Scalar) | |
| Data Scalar Type: float | |
| Data Byte Order: little Endian | |
| Data Extent: 256x256x128 | |
| Number of Scalar Components: 1 | |
| Instructions: | |
| 1. Load the dataset into ParaView. | |
| 2. Apply the volume rendering to visualize the engine dataset | |
| 3. Adjust the transfer function, let the outer part more transparent and the inner part more solid. Use light blue for the outer part and orange for the inner part. | |
| 4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes. | |
| 5. Set the viewpoint parameters as: [-184.58, 109.48, -431.72] to position; [134.05, 105.62, 88.92] to focal point; [0.01, 1.0, -0.001] to camera up direction. | |
| 6. Save your work: | |
| Save the visualization image as "engine/results/{agent_mode}/engine.png". | |
| (Optional, but must save if use paraview) Save the paraview state as "engine/results/{agent_mode}/engine.pvsm". | |
| (Optional, but must save if use pvpython script) Save the python script as "engine/results/{agent_mode}/engine.py". | |
| (Optional, but must save if use VTK) Save the cxx code script as "engine/results/{agent_mode}/engine.cxx" | |
| Do not save any other files, and always save the visualization image. | |