diff --git a/.gitignore b/.gitignore
index ec15bafb42851b66c86a1d802afd6155cfae97af..69af362c375a47a564154b58f7f2708c54da16c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,4 +5,6 @@ upload_huggingface.py
# sci_volume_data/**/*.raw
statistics/
.cache/
-.claude/
\ No newline at end of file
+.claude/
+.vscode/
+eval_cases/paraview/main_cases_old.yaml
\ No newline at end of file
diff --git a/eval_cases/paraview/main_cases.yaml b/eval_cases/paraview/main_cases.yaml
index c2e600e9d2b1475cc0614e57848da6506848cc5f..f1a79bc1f9917e8377625dedc927fa45dc467c69 100644
--- a/eval_cases/paraview/main_cases.yaml
+++ b/eval_cases/paraview/main_cases.yaml
@@ -2,7 +2,101 @@
# This test evaluates the ability to complete specific visualization tasks
# with detailed requirements and evaluation criteria
-# 1. Bonsai Dataset
+# Case 1: ABC
+- vars:
+ question: |
+ Load the ABC (Arnold-Beltrami-Childress) flow vector field from "ABC/data/ABC_128x128x128_float32_scalar3.raw", the information about this dataset:
+ ABC Flow (Vector)
+ Data Scalar Type: float
+ Data Byte Order: Little Endian
+ Data Extent: 128x128x128
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
+
+ Create streamlines using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [73.77, 63.25, 71.65], with 150 seed points and a radius of 75.0. Set integration direction to "BOTH" and maximum streamline length to 150.0.
+
+ Add a "Tube" filter on the stream tracer to enhance visualization. Set tube radius to 0.57 with 12 sides.
+
+ Color the tubes by Vorticity magnitude using the 'Cool to Warm (Diverging)' colormap.
+
+ Show the dataset bounding box as an outline.
+
+ Use a white background. Render at 1024x1024.
+
+ Save the visualization image as "ABC/results/{agent_mode}/ABC.png".
+ (Optional, but must save if use paraview) Save the paraview state as "ABC/results/{agent_mode}/ABC.pvsm".
+ (Optional, but must save if use python script) Save the python script as "ABC/results/{agent_mode}/ABC.py".
+ Do not save any other files, and always save the visualization image.
+ assert:
+ - type: llm-rubric
+ subtype: vision
+ value: |
+ 1. Streamline Density: Are the streamline tubes densely distributed throughout the volume, similar to the ground truth?
+ 2. Color Mapping: Are the tubes colored by vorticity magnitude using a blue-white-red diverging colormap, with a similar color distribution as the ground truth?
+ 3. Tube Appearance: Do the streamline tubes have a similar thickness and smooth appearance as the ground truth?
+
+# Case 2: argon-bubble
+- vars:
+ question: |
+ Task:
+ Load the Argon Bubble dataset from "argon-bubble/data/argon-bubble_128x128x256_float32.vtk".
+ Generate a visualization image of the Argon Bubble scalar field dataset with the following visualization settings:
+ 1) Create volume rendering
+ 2) Set the opacity transfer function as a ramp function across values of the volumetric data, assigning opacity 0 to value 0 and assigning opacity 1 to value 1.
+ 3) Set the color transfer function to assign a warm red color [0.71, 0.02, 0.15] to the highest value, a cool color [0.23, 0.29, 0.75] to the lowest value, and a grey color[0.87, 0.87, 0.87] to the midrange value
+ 4) Set the viewpoint parameters as: [0, 450, 0] to position; [0, 0, -15] to focal point; [0, 0, -1] to camera up direction
+ 5) Visualization image resolution is 1024x1024. White background. Shade turned off. Volume rendering ray casting sample distance is 0.1
+ 6) Don't show color/scalar bar or coordinate axes.
+
+ Save the visualization image as "argon-bubble/results/{agent_mode}/argon-bubble.png".
+ (Optional, but must save if use paraview) Save the paraview state as "argon-bubble/results/{agent_mode}/argon-bubble.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "argon-bubble/results/{agent_mode}/argon-bubble.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "argon-bubble/results/{agent_mode}/argon-bubble.cxx"
+ Do not save any other files, and always save the visualization image.
+ assert:
+ - type: llm-rubric
+ subtype: vision
+ value: |
+ 1. Does the visualization image clearly show the regions of cool, warm, and mild regions?
+
+ 2. Does the blueish region show areas with low opacity?
+
+ 3. Does the reddish region show areas with high opacity?
+
+# Case 3: Bernard
+- vars:
+ question: |
+ Load the Rayleigh-Benard convection vector field from "Bernard/data/Bernard_128x32x64_float32_scalar3.raw", the information about this dataset:
+ Rayleigh-Benard Convection (Vector)
+ Data Scalar Type: float
+ Data Byte Order: Little Endian
+ Data Extent: 128x32x64
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
+
+ Create four streamline sets using "Stream Tracer" filters with "Point Cloud" seed type, each with 100 seed points and radius 12.7:
+ - Streamline 1: Seed center at [30.69, 14.61, 47.99]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid blue (RGB: 0.0, 0.67, 1.0).
+ - Streamline 2: Seed center at [91.10, 14.65, 45.70]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid orange (RGB: 1.0, 0.33, 0.0).
+ - Streamline 3: Seed center at [31.87, 12.76, 15.89]. Apply a "Tube" filter (radius 0.3, 12 sides). Color by velocity magnitude using the 'Cool to Warm (Diverging)' colormap.
+ - Streamline 4: Seed center at [92.09, 10.50, 15.32]. Apply a "Tube" filter (radius 0.3, 12 sides). Color with solid green (RGB: 0.33, 0.67, 0.0).
+
+ In the pipeline browser panel, hide all stream tracers and only show the tube filters.
+
+ Use a gray-blue background (RGB: 0.329, 0.349, 0.427). Render at 1280x1280. Do not show a color bar.
+
+ Save the visualization image as "Bernard/results/{agent_mode}/Bernard.png".
+ (Optional, but must save if use paraview) Save the paraview state as "Bernard/results/{agent_mode}/Bernard.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "Bernard/results/{agent_mode}/Bernard.py".
+ Do not save any other files, and always save the visualization image.
+ assert:
+ - type: llm-rubric
+ subtype: vision
+ value: |
+ 1. Streamline Grouping: Are there four visually separate streamline clusters arranged in a 2x2 grid pattern, similar to the ground truth?
+ 2. Color Assignment: Are the four streamline groups colored in distinct colors (blue, orange, magnitude-mapped, and green), matching the ground truth color scheme?
+ 3. Convection Cell Structure: Do the streamlines within each group show circular or helical looping patterns characteristic of convection cells?
+
+# Case 4: bonsai
- vars:
question: |
Task:
@@ -18,18 +112,25 @@
Please think step by step and make sure to fulfill all the visualization goals mentioned above.
- Finally, save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm"
+ Use a white background. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ Save the visualization image as "bonsai/results/{agent_mode}/bonsai.png".
+ (Optional, but must save if use paraview) Save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "bonsai/results/{agent_mode}/bonsai.py".
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Brown Pot Visualization: Does the result show the pot portion in brown color?
+ 1. Overall Visualization Goal: How well does the result achieve the overall goal of showing a potted tree with the specified colors?
- 2. Silver Branch Visualization: Does the result show the branch/trunk portion in silver color?
+ 2. Brown Pot Visualization: Does the result show the pot portion in brown color?
- 3. Golden Leaves Visualization: Does the result show the leaves portion in golden color?
+ 3. Silver Branch Visualization: Does the result show the branch/trunk portion in silver color?
-# 2. Carp Dataset
+ 4. Golden Leaves Visualization: Does the result show the leaves portion in golden color?
+
+# Case 5: carp
- vars:
question: |
Task:
@@ -65,9 +166,15 @@
C. ~30%
D. ~40%
- 6. Save your work:
- Save the ParaView state as "carp/results/{agent_mode}/carp.pvsm".
+ 6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ 7. Save your work:
+ Save the visualization image as "carp/results/{agent_mode}/carp.png".
Save the answers to the analysis questions in plain text as "carp/results/{agent_mode}/answers.txt".
+ (Optional, but must save if use paraview) Save the paraview state as "carp/results/{agent_mode}/carp.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "carp/results/{agent_mode}/carp.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "carp/results/{agent_mode}/carp.cxx"
+ Do not save any other files, and always save the visualization image and the text file.
assert:
- type: llm-rubric
subtype: vision
@@ -86,242 +193,113 @@
2. Q2 correct answer: B. ~22%
-# 3. Chameleon Dataset (VolVis)
+# Case 6: chameleon_isosurface
- vars:
question: |
Task:
+ Load the chameleon dataset from "chameleon_isosurface/data/chameleon_isosurface_256x256x256_float32.vtk".
+ Generate a visualization image of 2 isosurfaces of the Chameleon scalar field dataset with the following visualization settings:
+ 1) Create isosurfaces of Iso_1 with a value of 0.12 and Iso_2 with a value of 0.45
+ 2) Assign RGB color of [0.0, 1.0, 0.0] to Iso_1, and color of [1.0, 1.0, 1.0] to Iso_2
+ 3) Assign opacity of 0.1 to Iso_1, and opacity of 0.99 to Iso_2
+ 4) Set the lighting parameter as: 0.1 to Ambient; 0.7 to Diffuse; 0.6 to Specular
+ 5) Set the viewpoint parameters as: [600, 0, 0] to position; [0, 0, 0] to focal point; [0, -1, 0] to camera up direction
+ 6) White background
+ 7) Visualization image resolution is 1024x1024
+ 8) Don't show color/scalar bar or coordinate axes.
- Load the chameleon dataset from "chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw", the information about this dataset:
- chameleon (Scalar)
- Data Scalar Type: float
- Data Byte Order: little Endian
- Data Extent: 256x256x270
- Number of Scalar Components: 1
- Data loading is very important, make sure you correctly load the dataset according to their features.
-
- Apply the volume rendering to visualize the chameleon dataset
-
- Adjust the transfer function to highlight the bony structures and skin in an X-ray style.
-
- Adjust the camera position and focus on the head part of the chameleon
-
- Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-
- Finally, save the paraview state as "chameleon_volvis/results/{agent_mode}/chameleon_volvis.pvsm"
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth X-ray-style volume rendering of the chameleon?
-
- 2. Bone Structure Visibility: Are the chameleon's bony structures clearly visible and similar to the ground truth, with the skull, jaw, and spine clearly distinguishable?
-
- 3. Soft Tissue Rendering: Is the soft tissue faintly visible and transparent, similar to the ground truth appearance?
-
- 4. Depth and Contrast: Does the visualization show similar depth cues and contrast between bones and soft tissue as the ground truth?
-
-# 4. Engine Dataset
-- vars:
- question: |
- 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. Save your work:
- Save the ParaView state as "engine/results/{agent_mode}/engine.pvsm".
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: How well does the result use volume rendering to clearly present the internal and external structures of the engine dataset?
-
- 2. Structural Clarity: Does the visualization emphasize depth so that the outer layers do not obscure the inner structures?
-
- 3. Transfer Function Transparency: Is the outer region rendered with higher transparency and the inner region more solid, achieving a clear layering effect?
-
- 4. Transfer Function Color Mapping: Are colors correctly assigned so that the outer part is light blue and the inner part is orange, enhancing structural contrast?
-
-# 5. Solar Plume Dataset
-- vars:
- question: |
- Task:
-
- Load the tornado dataset from "solar-plume/data/solar-plume_126x126x512_float32_scalar3.raw", the information about this dataset:
- solar-plume (Vector)
- Data Scalar Type: float
- Data Byte Order: little Endian
- Data Extent: 126x126x512
- Number of Scalar Components: 3
- Data loading is very important, make sure you correctly load the dataset according to their features.
-
- Add a "stream tracer" filter under the tornado data to display streamline, set the "Seed type" to "Point Cloud" and set the center of point cloud to 3D position [50, 50, 320] with a radius 30, then hide the point cloud sphere.
-
- Add a "tube" filter under the "stream tracer" filter to enhance the streamline visualization. Set the radius to 0.5. In the pipeline browser panel, hide everything except the "tube" filter.
-
-
- Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-
- Finally, save the paraview state as "solar-plume/results/{agent_mode}/solar-plume.pvsm"
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth streamline visualization of solar-plume flow structures?
-
- 2. Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth, particularly in the plume region?
-
- 3. Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?
-
- 4. Visual Appearance: Do the streamline tubes appear similar in thickness and visibility to the ground truth?
-
-# 6. Supernova Dataset
-- vars:
- question: |
- Task:
-
- Load the supernova dataset from "supernova/data/supernova_256x256x256_float32.raw", the information about this dataset:
- Supernova (Scalar)
- Data Scalar Type: float
- Data Byte Order: little Endian
- Data Spacing: 1x1x1
- Data Extent: 256x256x256
- Data loading is very important, make sure you correctly load the dataset according to their features.
-
- Then visualize it and extract two isosurfaces. One of them use color red, showing areas with low density (isovalue 40 and opacity 0.4), while the other use color blue, showing areas with high density (isovalue 150 and opacity 0.8).
-
- Please think step by step and make sure to fulfill all the visualization goals mentioned above. Only make the two isosurfaces visible.
-
- Finally, save the paraview state as "supernova/results/{agent_mode}/supernova.pvsm"
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: How well does the result achieve the overall goal of showing the supernova structure with two distinct isosurfaces representing different density regions?
-
- 2. Does the red isosurface show low density areas (outside regions) with lower opacity?
-
- 3. Does the blue isosurface show high density areas (inside regions) with higher opacity?
-
-# 7. Tangaroa Dataset
-- vars:
- question: |
- Task:
-
- Load the tangaroa dataset from "tangaroa_300x180x120_float32_scalar3.raw", the information about this dataset:
- tangaroa (Vector)
- Data Scalar Type: float
- Data Byte Order: little Endian
- Data Extent: 300x180x120
- Number of Scalar Components: 3
- Data loading is very important, make sure you correctly load the dataset according to their features.
-
- Apply "streamline tracer" filter, set the "Seed Type" to point cloud, turn off the "show sphere", set the center to [81.6814, 80.708, 23.5093], and radius to 29.9
-
- Add "Ribbon" filter to the streamline tracer results and set width to 0.3, set the Display representation to Surface.
-
- In pipeline browser panel, hide everything except the ribbon filter results.
-
- Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-
- Finally, save the paraview state as "tangaroa/results/{agent_mode}/tangaroa.pvsm"
+ Save the visualization image as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.png".
+ (Optional, but must save if use paraview) Save the paraview state as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth visualization of tangaroa flow structures using ribbon surfaces?
-
- 2. Flow Surface Patterns: Do the ribbon surfaces show similar flow patterns and structures as the ground truth?
+ 1. Does the result present 2 isosurfaces, one showing the inner part of the chameleon and one showing the outer part of the chameleon?
- 3. Surface Coverage: Is the spatial distribution and coverage of the flow surfaces similar to the ground truth?
+ 2. Is the skin of the Chameleon object of green color?
- 4. Visual Appearance: Do the ribbon surfaces appear similar in width and structure to the ground truth?
+ 3. Is the bone of the Chameleon object of white color?
-# 8. Tornado Dataset
+# Case 7: crayfish_streamline
- vars:
question: |
- Task:
-
- Load the tornado dataset from "tornado/data/tornado_64x64x64_float32_scalar3.raw", the information about this dataset:
- Tornado (Vector)
+ Load the Crayfish flow vector field from "crayfish_streamline/data/crayfish_streamline_322x162x119_float32_scalar3.raw", the information about this dataset:
+ Crayfish Flow (Vector)
Data Scalar Type: float
- Data Byte Order: little Endian
- Data Extent: 64x64x64
+ Data Byte Order: Little Endian
+ Data Extent: 322x162x119
Number of Scalar Components: 3
Data loading is very important, make sure you correctly load the dataset according to their features.
- Add a "glyph" filter under the tornado data to display velocity glyph, set an appropriate "Scale Factor" so the glyphs are visible.
+ Create two streamline sets using "Stream Tracer" filters with "Point Cloud" seed type, each with 100 seed points and radius 32.2:
+ - Streamline 1: Seed center at [107.33, 81.0, 59.5]. Apply a "Tube" filter (radius 0.5, 12 sides). Color by Vorticity magnitude using a diverging colormap with the following RGB control points:
+ - Value 0.0 -> RGB(0.231, 0.298, 0.753) (blue)
+ - Value 0.02 -> RGB(0.865, 0.865, 0.865) (white)
+ - Value 0.15 -> RGB(0.706, 0.016, 0.149) (red)
+ - Streamline 2: Seed center at [214.67, 81.0, 59.5]. Apply a "Tube" filter (radius 0.5, 12 sides). Color by Vorticity magnitude using the same colormap.
- Then add a "stream tracer" filter under the tornado data to generate streamlines. Choose "Point Cloud" as "Seed Type", and do not show sphere.
+ Show the dataset bounding box as an outline (black).
- Add a "tube" filter under the stream tracer you just created to generate tubes for visualizing the streamlines. Set an appropriate radius. Make the stream tracer invisible and the tube visible. At last, render the streamlines as tubes.
+ In the pipeline browser panel, hide all stream tracers and only show the tube filters and the outline.
- Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+ Use a white background. Render at 1280x1280.
- Finally, save the paraview state as "tornado/results/{agent_mode}/tornado.pvsm"
+ Save the paraview state as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.pvsm".
+ Save the visualization image as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.png".
+ (Optional, if use python script) Save the python script as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.py".
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: How well does the result achieve the overall goal of showing tornado flow patterns with glyphs and streamlines?
+ 1. Overall Visualization Goal: Does the result show streamline tubes colored by vorticity magnitude within a rectangular bounding box, similar to the ground truth?
+ 2. Streamline Clusters: Are there two distinct clusters matches the ground truth layout?
+ 3. Color Mapping: Are the tubes colored by vorticity magnitude using a blue-white-red diverging colormap, with a similar distribution as the ground truth?
- 2. Glyph Visualization: Does the result show velocity glyphs that are appropriately sized and visible?
-
- 3. Streamline Visualization: Does the result show streamlines that follow the flow patterns effectively?
-
- 4. Tube Rendering: Are the streamlines rendered as tubes with appropriate thickness?
-
-# 9. Vortex Dataset
+# Case 8: engine
- vars:
question: |
Task:
- Load the vortex dataset from "vortex/data/vortex_128x128x128_float32.raw", the information about this dataset:
- vortex (Scalar)
+ 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: 128x128x128
+ Data Extent: 256x256x128
Number of Scalar Components: 1
Instructions:
1. Load the dataset into ParaView.
- 2. Leverage "contour" filter to achieve iso-surface rendering. In pipeline browser panel, hide everything except the "contour" fileter.
-
- 3. In properties panel of "contour" filter, set isosurface value to -0.2, use Solid Color and set the color as beige.
+ 2. Apply the volume rendering to visualize the engine dataset
- 4. Enable Ambient occlusion by toggle the "Use Ambient Occlusion" button in the Render Passes.
+ 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.
- 5. Add head light with light inspector, set "Coords" as Camera, "Intentsity" to 0.2, Type to "Directional".
+ 4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
- 6. Save your work:
- Save the ParaView state as "vortex/results/{agent_mode}/vortex.pvsm".
+ 5. 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.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth isosurface rendering of the vortex scalar field?
+ 1. Overall Visualization Goal: How well does the result use volume rendering to clearly present the internal and external structures of the engine dataset?
- 2. Isosurface Structure: Does the isosurface show the same vortex structure and topology as the ground truth?
+ 2. Structural Clarity: Does the visualization emphasize depth so that the outer layers do not obscure the inner structures?
- 3. Surface Appearance: Does the surface color and shading appear similar to the ground truth?
+ 3. Transfer Function Transparency: Is the outer region rendered with higher transparency and the inner region more solid, achieving a clear layering effect?
- 4. Visualization Clarity: Are the vortex features clearly visible and comparable to the ground truth?
+ 4. Transfer Function Color Mapping: Are colors correctly assigned so that the outer part is light blue and the inner part is orange, enhancing structural contrast?
-# 10. Foot Dataset
+# Case 9: foot
- vars:
question: |
Task:
@@ -346,9 +324,15 @@
C. The metatarsals are fully visible, but the phalanges are only partially visible
D. Neither the phalanges nor the metatarsals are clearly visible
- 3. Save your work:
- Save the ParaView state as "foot/results/{agent_mode}/foot.pvsm".
+ 3. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ 4. Save your work:
+ Save the visualization image as "foot/results/{agent_mode}/foot.png".
Save the answers to the analysis questions in plain text as "foot/results/{agent_mode}/answers.txt".
+ (Optional, but must save if use paraview) Save the paraview state as "foot/results/{agent_mode}/foot.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "foot/results/{agent_mode}/foot.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "foot/results/{agent_mode}/foot.cxx"
+ Do not save any other files, and always save the visualization image and the text file.
assert:
- type: llm-rubric
subtype: vision
@@ -356,13 +340,12 @@
1. Overall Goal: Does the visualization effectively distinguish between different tissue types in the foot dataset?
2. X-ray Appearance: Does the visualization resemble an X-ray (monochrome or grayscale, transparent look, consistent lighting)?
-
- type: llm-rubric
subtype: text
value: |
1. Q1 correct answer: B. The phalanges are fully visible, but the metatarsals are only partially visible
-# 11. Lobster Dataset
+# Case 10: lobster
- vars:
question: |
Task:
@@ -389,9 +372,15 @@
C. 8 walking legs
D. 10 walking legs
- 4. Save your work:
- Save the ParaView state as "lobster/results/{agent_mode}/lobster.pvsm".
+ 4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ 5. Save your work:
+ Save the visualization image as "lobster/results/{agent_mode}/lobster.png".
Save the answers to the analysis questions in plain text as "lobster/results/{agent_mode}/answers.txt".
+ (Optional, but must save if use paraview) Save the paraview state as "lobster/results/{agent_mode}/lobster.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "lobster/results/{agent_mode}/lobster.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "lobster/results/{agent_mode}/lobster.cxx"
+ Do not save any other files, and always save the visualization image and the text file.
assert:
- type: llm-rubric
subtype: vision
@@ -401,103 +390,111 @@
2. Boundary Clearity: Are surface details and boundaries of the lobster well-defined?
3. Correct Color: Is the color of the lobster mimic a real one? (red-orange)
-
- type: llm-rubric
subtype: text
value: |
1. Q1 correct answer: B. 7 walking legs
-# 12. Chameleon Dataset (IsoSurface)
+# Case 11: mhd-magfield_streamribbon
- vars:
question: |
- Task:
- Load the chameleon dataset from "chameleon_isosurface/data/chameleon_isosurface_256x256x256_float32.vtk".
- Generate a visualization image of 2 isosurfaces of the Chameleon scalar field dataset with the following visualization settings:
- 1) Create isosurfaces of Iso_1 with a value of 0.12 and Iso_2 with a value of 0.45
- 2) Assign RGB color of [0.0, 1.0, 0.0] to Iso_1, and color of [1.0, 1.0, 1.0] to Iso_2
- 3) Assign opacity of 0.1 to Iso_1, and opacity of 0.99 to Iso_2
- 4) Set the lighting parameter as: 0.1 to Ambient; 0.7 to Diffuse; 0.6 to Specular
- 5) Set the viewpoint parameters as: [600, 0, 0] to position; [0, 0, 0] to focal point; [0, -1, 0] to camera up direction
- 6) White background
- 7) Visualization image resolution is 1024x1024
- 8) Don't show color/scalar bar or coordinate axes.
- 9) Save the visualization image as a png file "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.png"
- 10) (Option 1) Save the paraview state as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.pvsm" if you are using ParaView as the visualization tool
- 11) (Option 2) Save the cxx code script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.cxx" if you are using VTK as the visualization tool
- You should only choose one of Option 1 or Option 2 to save your work. Do not save any other files, and always save the visualization image.
+ Load the MHD magnetic field dataset from "mhd-magfield_streamribbon/data/mhd-magfield_streamribbon.vti" (VTI format, 128x128x128 grid with components bx, by, bz).
+ Generate a stream ribbon seeded from a line source along the y-axis at x=64, z=64 (from y=20 to y=108), with 30 seed points.
+ The stream ribbon should be traced along the magnetic field lines.
+ Color the stream ribbon by magnetic field magnitude using the 'Cool to Warm' colormap. Enable surface lighting with specular reflection for better 3D perception.
+ Add a color bar labeled 'Magnetic Field Magnitude'.
+ Use a white background. Set an isometric camera view. Render at 1024x1024 resolution.
+ Save the visualization image as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.png".
+ (Optional, but must save if use paraview) Save the paraview state as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Does the result present 2 isosurfaces, one showing the inner part of the chameleon and one showing the outer part of the chameleon?
+ 1. Overall Visualization Goal: Does the result match the ground truth stream ribbon visualization of the MHD magnetic field?
- 2. Is the skin of the Chameleon object of green color?
+ 2. Surface Patterns: Does the stream ribbon show similar flow patterns and structures as the ground truth?
- 3. Is the bone of the Chameleon object of white color?
+ 3. Surface Coverage: Is the spatial extent and shape of the stream ribbon similar to the ground truth?
+ 4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
-# 13. Argon Bubble
-# A CFD simulation of a bubble of argon gas immersed in air and being hit by a shockwave.
-# This dataset is a time-varying volume dataset created by the Center for Computational Sciences and Engineering (CCSE) at Lawrence Berkeley National Laboratory.
-# The volumetric used for the SciVis benchmark is one frame of the time sequence.
+# Case 12: mhd-turbulence_pathline
- vars:
question: |
- Task:
- Load the Argon Bubble dataset from "argon-bubble/data/argon-bubble_128x128x256_float32.vtk".
- Generate a visualization image of the Argon Bubble scalar field dataset with the following visualization settings:
- 1) Create volume rendering
- 2) Set the opacity transfer function as a ramp function across values of the volumetric data, assigning opacity 0 to value 0 and assigning opacity 1 to value 1.
- 3) Set the color transfer function to assign a warm red color [0.71, 0.02, 0.15] to the highest value, a cool color [0.23, 0.29, 0.75] to the lowest value, and a grey color[0.87, 0.87, 0.87] to the midrange value
- 4) Set the viewpoint parameters as: [0, 450, 0] to position; [0, 0, -15] to focal point; [0, 0, -1] to camera up direction
- 5) Visualization image resolution is 1024x1024. White background. Shade turned off. Volume rendering ray casting sample distance is 0.1
- 6) Don't show color/scalar bar or coordinate axes.
- 7) Save the visualization image as a png file "argon-bubble/results/{agent_mode}/argon-bubble.png"
- 8) (Option 1) Save the paraview state as "argon-bubble/results/{agent_mode}/argon-bubble.pvsm" if you are using ParaView as the visualization tool
- 9) (Option 2) Save the cxx code script as "argon-bubble/results/{agent_mode}/argon-bubble.cxx" if you are using VTK as the visualization tool
- You should only choose one of Option 1 or Option 2 to save your work. Do not save any other files, and always save the visualization image.
+ Load the MHD turbulence velocity field time series "mhd-turbulence_pathline/data/mhd-turbulence_pathline_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
+ Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.5) to extend particle travel and smooth trajectories.
+ Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80.
+ Render pathlines as tubes with radius 0.3. Color by velocity magnitude using the 'Viridis (matplotlib)' colormap.
+ Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view. Render at 1024x1024.
+ Save the visualization image as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.png".
+ (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.cxx"
+ Do not save any other files, and always save the visualization image.
+ assert:
+ - type: llm-rubric
+ subtype: vision
+ value: |
+ 1. Overall Visualization Goal: Does the result match the ground truth pathline visualization of the MHD turbulence velocity field?
+
+ 2. Pathline Patterns: Do the pathlines show similar particle trajectories and flow structures as the ground truth?
+
+ 3. Pathline Coverage: Is the spatial extent and distribution of pathlines similar to the ground truth?
+
+ 4. Color Mapping: Is the color distribution along pathlines visually similar to the ground truth?
+
+# Case 13: mhd-turbulence_pathribbon
+- vars:
+ question: |
+ Load the MHD turbulence velocity field time series "mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
+ Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.1) for dense, smooth trajectories.
+ Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80.
+ Create ribbon surfaces from the pathlines using the Ribbon filter with width 1.5 and a fixed default normal to prevent twisting. Apply Smooth filter (500 iterations) and generate surface normals for smooth shading.
+ Set surface opacity to 0.85. Color by velocity magnitude using the 'Cool to Warm' colormap (range 0.1-0.8). Add specular highlights (0.5).
+ Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view. Render at 1024x1024.
+ Save the visualization image as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.png".
+ (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Does the visualization image clearly show the regions of cool, warm, and mild regions?
-
- 2. Does the blueish region show areas with low opacity?
+ 1. Surface Patterns: Does the path ribbon show similar flow patterns and structures as the ground truth?
- 3. Does the reddish region show areas with high opacity?
+ 2. Surface Coverage: Is the spatial extent and shape of the path ribbon similar to the ground truth?
+ 3. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
-# 14. Richtmyer-Meshkov Instability Simulation
-# Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation
+# Case 14: mhd-turbulence_streamline
- vars:
question: |
- Task:
- Load the Richtmyer dataset from "richtmyer/data/richtmyer_256x256x240_float32.vtk".
- Generate a visualization image of the Richtmyer dataset, Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation, with the following visualization settings:
- 1) Create volume rendering
- 2) Set the opacity transfer function as a ramp function from value 0.05 to 1 of the volumetric data, assigning opacity 0 to value less than 0.05 and assigning opacity 1 to value 1.
- 3) Set the color transfer function following the 7 rainbow colors and assign a red color [1.0, 0.0, 0.0] to the highest value, a purple color [0.5, 0.0, 1.0] to the lowest value.
- 4) Visualization image resolution is 1024x1024
- 5) Set the viewpoint parameters as: [420, 420, -550] to position; [128, 128, 150] to focal point; [-1, -1, 1] to camera up direction
- 6) Turn on the shade and set the ambient, diffuse and specular as 1.0
- 7) White background. Volume rendering ray casting sample distance is 0.1
- 8) Don't show color/scalar bar or coordinate axes.
- 9) Save the visualization image as a png file "richtmyer/results/{agent_mode}/richtmyer.png"
- 10) (Option 1) Save the paraview state as "richtmyer/results/{agent_mode}/richtmyer.pvsm" if you are using ParaView as the visualization tool
- 11) (Option 2) Save the cxx code script as "richtmyer/results/{agent_mode}/richtmyer.cxx" if you are using VTK as the visualization tool
- You should only choose one of Option 1 or Option 2 to save your work. Do not save any other files, and always save the visualization image.
+ Load the MHD turbulence velocity field dataset "mhd-turbulence_streamline/data/mhd-turbulence_streamline.vti" (VTI format, 128x128x128 grid).
+ Generate 3D streamlines seeded from a line source along the z-axis at x=64, y=64 (from z=0 to z=127), with 50 seed points.
+ Color the streamlines by velocity magnitude using the 'Turbo' colormap. Set streamline tube radius to 0.3 using the Tube filter.
+ Add a color bar labeled 'Velocity Magnitude'. Use a white background. Set an isometric camera view. Render at 1024x1024.
+ Save the visualization image as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.png".
+ (Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Does the visualization show a clear surface with peaks and valleys?
+ 1. Overall Visualization Goal: Does the result match the ground truth streamline visualization of the MHD turbulence velocity field?
- 2. Are the peaks highlighted with the reddish color?
+ 2. Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth?
- 3. Are the valleys highlighted with the bluish color?
+ 3. Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?
+ 4. Color Mapping: Is the color distribution along streamlines visually similar to the ground truth?
-# 15. Rayleigh-Taylor Instability (Miranda)
-# A time step of a density field in a simulation of the mixing transition in Rayleigh-Taylor instability
+# Case 15: miranda
- vars:
question: |
Task:
@@ -511,10 +508,11 @@
6) White background
7) Visualization image resolution is 1024x1024
8) Don't show color/scalar bar or coordinate axes.
- 9) Save the visualization image as a png file "miranda/results/{agent_mode}/miranda.png"
- 10) (Option 1) Save the paraview state as "miranda/results/{agent_mode}/miranda.pvsm" if you are using ParaView as the visualization tool
- 11) (Option 2) Save the cxx code script as "miranda/results/{agent_mode}/miranda.cxx" if you are using VTK as the visualization tool
- You should only choose one of Option 1 or Option 2 to save your work. Do not save any other files, and always save the visualization image.
+ Save the visualization image as "miranda/results/{agent_mode}/miranda.png".
+ (Optional, but must save if use paraview) Save the paraview state as "miranda/results/{agent_mode}/miranda.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "miranda/results/{agent_mode}/miranda.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "miranda/results/{agent_mode}/miranda.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
@@ -525,14 +523,41 @@
3. Does the red region show areas with high opacity?
+# Case 16: richtmyer
+- vars:
+ question: |
+ Task:
+ Load the Richtmyer dataset from "richtmyer/data/richtmyer_256x256x240_float32.vtk".
+ Generate a visualization image of the Richtmyer dataset, Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation, with the following visualization settings:
+ 1) Create volume rendering
+ 2) Set the opacity transfer function as a ramp function from value 0.05 to 1 of the volumetric data, assigning opacity 0 to value less than 0.05 and assigning opacity 1 to value 1.
+ 3) Set the color transfer function following the 7 rainbow colors and assign a red color [1.0, 0.0, 0.0] to the highest value, a purple color [0.5, 0.0, 1.0] to the lowest value.
+ 4) Visualization image resolution is 1024x1024
+ 5) Set the viewpoint parameters as: [420, 420, -550] to position; [128, 128, 150] to focal point; [-1, -1, 1] to camera up direction
+ 6) Turn on the shade and set the ambient, diffuse and specular as 1.0
+ 7) White background. Volume rendering ray casting sample distance is 0.1
+ 8) Don't show color/scalar bar or coordinate axes.
+ Save the visualization image as "richtmyer/results/{agent_mode}/richtmyer.png".
+ (Optional, but must save if use paraview) Save the paraview state as "richtmyer/results/{agent_mode}/richtmyer.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "richtmyer/results/{agent_mode}/richtmyer.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "richtmyer/results/{agent_mode}/richtmyer.cxx"
+ Do not save any other files, and always save the visualization image.
+ assert:
+ - type: llm-rubric
+ subtype: vision
+ value: |
+ 1. Does the visualization show a clear surface with peaks and valleys?
+
+ 2. Are the peaks highlighted with the reddish color?
+
+ 3. Are the valleys highlighted with the bluish color?
-# 16. Rotstrat
-# Temperature field of a direct numerical simulation of rotating stratified turbulence
+# Case 17: rotstrat
- vars:
question: |
Task:
Load the rotstrat dataset from "rotstrat/data/rotstrat_256x256x256_float32.vtk".
- Generate a visualization image of the Rotstrat dataset, temperature field of a direct numerical simulation of rotating stratified turbulence, with the following visualization settings:
+ Generate a visualization image of the Rotstrat dataset, temperature field of a direct numerical simulation of rotating stratified turbulence, with the following visualization settings:
1) Create volume rendering
2) Set the opacity transfer function as a step function jumping from 0 to 1 at value 0.12
3) Set the color transfer function to assign a warm red color [0.71, 0.02, 0.15] to the highest value, a cool color [0.23, 0.29, 0.75] to the lowest value, and a grey color[0.87, 0.87, 0.87] to the midrange value
@@ -541,10 +566,11 @@
6) White background
7) Visualization image resolution is 1024x1024
8) Don't show color/scalar bar or coordinate axes.
- 9) Save the visualization image as a png file "rotstrat/results/{agent_mode}/rotstrat.png"
- 10) (Option 1) Save the paraview state as "rotstrat/results/{agent_mode}/rotstrat.pvsm" if you are using ParaView as the visualization tool
- 11) (Option 2) Save the cxx code script as "rotstrat/results/{agent_mode}/rotstrat.cxx" if you are using VTK as the visualization tool
- You should only choose one of Option 1 or Option 2 to save your work. Do not save any other files, and always save the visualization image.
+ Save the visualization image as "rotstrat/results/{agent_mode}/rotstrat.png".
+ (Optional, but must save if use paraview) Save the paraview state as "rotstrat/results/{agent_mode}/rotstrat.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "rotstrat/results/{agent_mode}/rotstrat.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "rotstrat/results/{agent_mode}/rotstrat.cxx"
+ Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
@@ -555,56 +581,19 @@
3. Does the visualization show the shape of a vortex in the bottom left corner of the image?
-
-# Vector Field Cases
-# 17. MHD Turbulence Velocity Field (t=10) (mhd-turbulence_glyph)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector). Data source: The Well (Polymathic AI).
-- vars:
- question: |
- Load the MHD turbulence velocity field dataset from "mhd-turbulence_glyph/data/mhd-turbulence_glyph.vti" (VTI format, 128x128x128 grid).
- Create a slice at z=64 through the volume. On this slice, place arrow glyphs oriented by the velocity vector field and scaled by velocity magnitude.
- Color the arrows using the 'Cool to Warm' colormap mapped to velocity magnitude.
- Use a sampling stride of 4 to avoid overcrowding. Set the glyph scale factor to 5.0.
- Add a color bar labeled 'Velocity Magnitude'.
- Use a dark background (RGB: 0.1, 0.1, 0.15).
- Set the camera to a top-down view looking along the negative z-axis. Render at 1024x1024 resolution.
- Save the paraview state as "mhd-turbulence_glyph/results/{agent_mode}/mhd-turbulence_glyph.pvsm".
- Save the visualization image as "mhd-turbulence_glyph/results/{agent_mode}/mhd-turbulence_glyph.png".
- (Optional, if use python script) Save the python script as "mhd-turbulence_glyph/results/{agent_mode}/mhd-turbulence_glyph.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth glyph visualization of the MHD turbulence velocity field?
-
- 2. Glyph Patterns: Do the arrow glyphs show similar orientation and spatial patterns as the ground truth?
-
- 3. Glyph Scaling: Do the glyphs show similar size variations as the ground truth, reflecting the velocity magnitude patterns?
-
- 4. Color Mapping: Is the color distribution across glyphs visually similar to the ground truth?
-
-
-# 18. Rayleigh-Taylor Instability Velocity Field (t=50) (rti-velocity_glyph)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
+# Case 18: rti-velocity_glyph
- vars:
question: |
- Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_glyph/data/rti-velocity_glyph.vti" (VTI format, 128x128x128 grid).
- Create a slice at y=64 through the volume.
+ Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_glyph/data/rti-velocity_glyph.vti" (VTI format, 128x128x128 grid).
+ Create a slice at y=64 through the volume.
Place arrow glyphs on the slice, oriented by the velocity vector. Use uniform arrow size (no magnitude scaling, scale factor 3.0).
- Color the arrows by velocity magnitude using the 'Viridis (matplotlib)' colormap. Use a sampling stride of 3.
+ Color the arrows by velocity magnitude using the 'Viridis (matplotlib)' colormap. Use a sampling stride of 3.
Add a color bar labeled 'Velocity Magnitude'.
- Use a black background.
- Set the camera to view along the negative y-axis. Render at 1024x1024.
- Save the paraview state as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.pvsm".
+ Use a white background. Set the camera to view along the negative y-axis. Render at 1024x1024.
Save the visualization image as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.png".
- (Optional, if use python script) Save the python script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.py".
+ (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
@@ -618,372 +607,215 @@
4. Color Mapping: Is the color distribution across glyphs visually similar to the ground truth?
-
-# 19. MHD Magnetic Field (t=10) (mhd-magfield_glyph)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
+# Case 19: rti-velocity_slices
- vars:
question: |
- Load the MHD magnetic field dataset from "mhd-magfield_glyph/data/mhd-magfield_glyph.vti" (VTI format, 128x128x128 grid with components bx, by, bz).
- Create a slice at x=64 through the volume.
- Place arrow glyphs oriented by the magnetic field vector and scaled by field magnitude (scale factor 5.0).
- Color the arrows using the 'Plasma (matplotlib)' colormap mapped to magnitude. Use stride of 4.
- Add a color bar labeled 'Magnetic Field Magnitude'.
- Use a dark navy background (RGB: 0.0, 0.0, 0.15). Set camera to view along the negative x-axis. Render at 1024x1024.
- Save the paraview state as "mhd-magfield_glyph/results/{agent_mode}/mhd-magfield_glyph.pvsm".
- Save the visualization image as "mhd-magfield_glyph/results/{agent_mode}/mhd-magfield_glyph.png".
- (Optional, if use python script) Save the python script as "mhd-magfield_glyph/results/{agent_mode}/mhd-magfield_glyph.py".
+ Load the Rayleigh-Taylor instability velocity field from "rti-velocity_slices/data/rti-velocity_slices.vti" (VTI format, 128x128x128).
+ Create three orthogonal slices: at x=64 (YZ-plane), y=64 (XZ-plane), and z=64 (XY-plane).
+ Color all three slices by velocity magnitude using the 'Turbo' colormap.
+ Add a color bar labeled 'Velocity Magnitude'.
+ Use a white background. Set an isometric camera view that shows all three slices. Render at 1024x1024.
+ Save the visualization image as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.png".
+ (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth glyph visualization of the MHD magnetic field?
-
- 2. Glyph Patterns: Do the arrow glyphs show similar orientation and spatial patterns as the ground truth?
-
- 3. Glyph Scaling: Do the glyphs show similar size variations as the ground truth, reflecting the field magnitude patterns?
+ 1. Slice Count and Orientation: Are there exactly three perpendicular slices (one horizontal XY-plane and two vertical XZ and YZ planes), matching the ground truth arrangement?
+ 2. Color Mapping: Are the slices colored using a Turbo-like colormap (blue to green to yellow to red) mapped to velocity magnitude, with a similar color distribution as the ground truth?
+ 3. Mixing Zone Pattern: Does the horizontal (XY) slice show a chaotic, high-velocity-magnitude mixing pattern in its center region, similar to the ground truth?
- 4. Color Mapping: Is the color distribution across glyphs visually similar to the ground truth?
-
-
-# 20. MHD Turbulence Velocity Field (t=30) (mhd-turbulence_streamline)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
+# Case 20: rti-velocity_streakline
- vars:
question: |
- Load the MHD turbulence velocity field dataset "mhd-turbulence_streamline/data/mhd-turbulence_streamline.vti" (VTI format, 128x128x128 grid).
- Generate 3D streamlines seeded from a line source along the z-axis at x=64, y=64 (from z=0 to z=127), with 50 seed points.
- Color the streamlines by velocity magnitude using the 'Turbo' colormap. Set streamline tube radius to 0.3 using the Tube filter.
- Add a color bar labeled 'Velocity Magnitude'. Use a dark background (RGB: 0.05, 0.05, 0.1). Set an isometric camera view. Render at 1024x1024.
- Save the paraview state as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.pvsm".
- Save the visualization image as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.png".
- (Optional, if use python script) Save the python script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.py".
+ Load the Rayleigh–Taylor instability velocity field time series from "rti-velocity_streakline/data/RTI_velocity_{timestep}.nc", where "timestep" in {0030, 0031, 0032, 0033, 0034, 0035, 0036, 0037, 0038, 0039, 0040} (11 timesteps, NetCDF format, 128×128×128 grid each, with separate vx, vy, vz arrays).
+ Construct the time-varying velocity field u(x,t) by merging vx, vy, vz into a single vector field named "velocity", and compute the velocity magnitude "magnitude" = |velocity| for coloring.
+ Compute streaklines as a discrete approximation of continuous particle injection: continuously release particles from fixed seed points at every sub-timestep into the time-varying velocity field using the StreakLine filter. Apply TemporalShiftScale (scale=20) to extend particle travel time, and apply TemporalInterpolator with a sub-timestep interval of 0.25 (or smaller) to approximate continuous injection over time.
+ Seed 26 static points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use StaticSeeds=True, ForceReinjectionEveryNSteps=1 (reinjection at every sub-timestep), and set TerminationTime=200.
+ Render the resulting streaklines as tubes with radius 0.3. Color the tubes by velocity magnitude ("magnitude") using the 'Cool to Warm (Extended)' colormap. Add a color bar for velocity magnitude.
+ Use a white background. Set an isometric camera view and render at 1024×1024.
+ Save the visualization image as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.png".
+ (Optional, but must save if use paraview) Save the paraview state as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth streamline visualization of the MHD turbulence velocity field?
-
- 2. Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth?
+ 1. Streak Line Patterns: Do the streak lines show similar flow patterns and structures as the ground truth?
- 3. Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?
-
- 4. Color Mapping: Is the color distribution along streamlines visually similar to the ground truth?
+ 2. Streak Line Coverage: Is the spatial extent and distribution of streak lines similar to the ground truth?
+ 3. Color Mapping: Is the color distribution along streak lines visually similar to the ground truth?
-# 21. Rayleigh-Taylor Instability Velocity Field (t=70) (rti-velocity_streamline)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_streamline/data/rti-velocity_streamline.vti" (VTI format, 128x128x128 grid).
- Generate streamlines seeded from a plane at y=64 (using a Point Cloud seed with 200 points distributed on the xz-plane at y=64).
- Color the streamlines by the vz component using a 'Cool to Warm (Extended)' diverging colormap. Render streamlines as tubes with radius 0.4.
- Add a color bar labeled 'Vz Component'.
- Dark background (RGB: 0.02, 0.02, 0.05). Use an isometric camera view. Render at 1024x1024.
- Save the paraview state as "rti-velocity_streamline/results/{agent_mode}/rti-velocity_streamline.pvsm".
- Save the visualization image as "rti-velocity_streamline/results/{agent_mode}/rti-velocity_streamline.png".
- (Optional, if use python script) Save the python script as "rti-velocity_streamline/results/{agent_mode}/rti-velocity_streamline.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1) Streamlines seeded from y=64 plane region, with similar pattern compared to groundtruth
- 2) Streamlines rendered as tubes
- 3) Color by vz with Cool to Warm diverging colormap
- 4) Color bar labeled 'Vz Component'
- 5) Dark background, Isometric camera view, Output resolution 1024x1024
-
-
-# 22. Turbulent Radiative Layer Velocity Field (tcool=0.10, t=50) (trl-velocity_streamline)
-# Turbulent Radiative Layer simulations of astrophysical mixing processes where cold, dense gas interfaces with hot, dilute gas moving at subsonic velocities.
-# The cold dense gas on the bottom and hot dilute gas on the top becomes unstable to the Kelvin-Helmholtz instability.
-# When turbulence causes mixing, intermediate-temperature gas forms and rapidly cools, creating a net mass transfer from the hot phase to the cold phase—a process relevant to interstellar and circumgalactic environments.
-# Generated using Athena++. Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the turbulent radiative layer velocity field dataset from "trl-velocity_streamline/data/trl-velocity_streamline.vti" (VTI format, 256x128x128 grid).
- Generate streamlines seeded from a line along the x-axis at y=64, z=64 (from x=0 to x=255), with 100 seed points.
- Color streamlines by velocity magnitude using the 'Inferno (matplotlib)' colormap. Render as tubes with radius 0.5.
- Add a color bar labeled 'Velocity Magnitude'.
- Dark background (RGB: 0.0, 0.0, 0.0). Set an isometric camera view. Render at 1024x1024."
- Save the paraview state as "trl-velocity_streamline/results/{agent_mode}/trl-velocity_streamline.pvsm".
- Save the visualization image as "trl-velocity_streamline/results/{agent_mode}/trl-velocity_streamline.png".
- (Optional, if use python script) Save the python script as "trl-velocity_streamline/results/{agent_mode}/trl-velocity_streamline.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1) Streamlines seeded along x-axis line, with similar pattern compared to groundtruth
- 2) Streamlines rendered as tubes
- 3) Color by magnitude with Inferno colormap
- 4) Color bar labeled 'Velocity Magnitude'
- 5) Black background, Isometric camera view, Output resolution 1024x1024
-
-
-# 23. MHD Magnetic Field (t=40) (mhd-magfield_volvis)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
+# Case 21: solar-plume
- vars:
question: |
- Load the MHD magnetic field dataset from "mhd-magfield_volvis/data/mhd-magfield_volvis.vti" (VTI format, 128x128x128 grid).
- Compute the magnetic field magnitude from components (bx, by, bz). Perform volume rendering of the magnitude field.
- Use the 'Cool to Warm' colormap with an opacity transfer function that makes low-magnitude regions transparent and high-magnitude regions opaque.
- Add a color bar labeled 'B Magnitude'.
- Use a dark background (RGB: 0.0, 0.0, 0.05). Set an isometric camera view. Render at 1024x1024.
- Save the paraview state as "mhd-magfield_volvis/results/{agent_mode}/mhd-magfield_volvis.pvsm".
- Save the visualization image as "mhd-magfield_volvis/results/{agent_mode}/mhd-magfield_volvis.png".
- (Optional, if use python script) Save the python script as "mhd-magfield_volvis/results/{agent_mode}/mhd-magfield_volvis.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth volume rendering of the MHD magnetic field magnitude?
+ Task:
- 2. Volume Structure: Are the internal structures and features visible in the volume rendering similar to the ground truth?
+ Load the tornado dataset from "solar-plume/data/solar-plume_126x126x512_float32_scalar3.raw", the information about this dataset:
+ solar-plume (Vector)
+ Data Scalar Type: float
+ Data Byte Order: little Endian
+ Data Extent: 126x126x512
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 3. Opacity Distribution: Does the transparency/opacity distribution appear similar to the ground truth, showing the same depth and internal features?
+ Add a "stream tracer" filter under the tornado data to display streamline, set the "Seed type" to "Point Cloud" and set the center of point cloud to 3D position [50, 50, 320] with a radius 30, then hide the point cloud sphere.
- 4. Color Mapping: Is the color distribution throughout the volume visually similar to the ground truth?
+ Add a "tube" filter under the "stream tracer" filter to enhance the streamline visualization. Set the radius to 0.5. In the pipeline browser panel, hide everything except the "tube" filter.
-# 24. Turbulence Gravity Cooling Velocity (temp=1000K, dens=4.45, metal=0.1Z, t=20) (tgc-velocity_volvis)
-# Turbulence-gravity-cooling simulations modeling turbulent fluid with gravity representing the interstellar medium in galaxies.
-# These simulations capture the formation of dense filaments that seed star formation, with filament frequency and timescales varying based on cooling strength.
-# The dataset encompasses three density regimes with systematically varied initial temperatures and metallicity levels representing different cosmic epochs,
-# governed by coupled equations for pressure, density, momentum, and internal energy incorporating gravitational forces, viscosity, and radiative heating/cooling.
-# Data source: The Well (Polymathic AI).
-- vars:
- question: |
- Load the turbulence-gravity-cooling velocity field dataset from "tgc-velocity_volvis/data/tgc-velocity_volvis.vti" (VTI format, 64x64x64 grid).
- Perform volume rendering of velocity magnitude. Use the 'Viridis (matplotlib)' colormap.
- Set opacity transfer function to gradually increase from 0 at minimum to 0.8 at maximum.
- Add a color bar labeled 'Velocity Magnitude'.
- Dark gray background (RGB: 0.1, 0.1, 0.1). Isometric camera view. Render at 1024x1024.
- Save the paraview state as "tgc-velocity_volvis/results/{agent_mode}/tgc-velocity_volvis.pvsm".
- Save the visualization image as "tgc-velocity_volvis/results/{agent_mode}/tgc-velocity_volvis.png".
- (Optional, if use python script) Save the python script as "tgc-velocity_volvis/results/{agent_mode}/tgc-velocity_volvis.py".
+ Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+
+ Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ Save the visualization image as "solar-plume/results/{agent_mode}/solar-plume.png".
+ (Optional, but must save if use paraview) Save the paraview state as "solar-plume/results/{agent_mode}/solar-plume.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "solar-plume/results/{agent_mode}/solar-plume.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "solar-plume/results/{agent_mode}/solar-plume.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth volume rendering of the TGC velocity field magnitude?
-
- 2. Volume Structure: Are the internal structures and features visible in the volume rendering similar to the ground truth?
+ 1. Overall Visualization Goal: Does the result match the ground truth streamline visualization of solar-plume flow structures?
- 3. Opacity Distribution: Does the transparency/opacity distribution appear similar to the ground truth, showing the same depth and internal features?
+ 2. Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth, particularly in the plume region?
- 4. Color Mapping: Is the color distribution throughout the volume visually similar to the ground truth?
+ 3. Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?
+ 4. Visual Appearance: Do the streamline tubes appear similar in thickness and visibility to the ground truth?
-# 25. Rayleigh-Taylor Instability Velocity Field (t=40) (rti-velocity_divergence)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
+# Case 22: supernova_isosurface
- vars:
question: |
- Load the Rayleigh-Taylor instability velocity field from "rti-velocity_divergence/data/rti-velocity_divergence.vti" (VTI format, 128x128x128).
- Compute the divergence of the velocity field using the Gradient filter with 'Compute Divergence' enabled.
- Extract a slice at z=64 and color it by divergence using the 'Cool to Warm' diverging colormap (centered at 0).
- Add a color bar labeled 'Velocity Divergence'.
- White background. Top-down camera view along negative z-axis. Render at 1024x1024.
- Save the paraview state as "rti-velocity_divergence/results/{agent_mode}/rti-velocity_divergence.pvsm".
- Save the visualization image as "rti-velocity_divergence/results/{agent_mode}/rti-velocity_divergence.png".
- (Optional, if use python script) Save the python script as "rti-velocity_divergence/results/{agent_mode}/rti-velocity_divergence.py".
- Do not save any other files, and always save the visualization image
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth visualization of the RTI velocity divergence field?
+ Task:
- 2. Divergence Patterns: Are the divergence patterns and structures visible in the result similar to the ground truth?
+ Load the supernova dataset from "supernova_isosurface/data/supernova_isosurface_256x256x256_float32.raw", the information about this dataset:
+ supernova (Scalar)
+ Data Scalar Type: float
+ Data Byte Order: little Endian
+ Data Spacing: 1x1x1
+ Data Extent: 256x256x256
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 3. Spatial Distribution: Does the spatial distribution of positive and negative divergence regions match the ground truth?
+ Then visualize it and extract two isosurfaces. One of them use color red, showing areas with low density (isovalue 40 and opacity 0.2), while the other use color light blue, showing areas with high density (isovalue 150 and opacity 0.4).
- 4. Color Mapping: Is the color distribution visually similar to the ground truth, showing similar divergence values?
+ Please think step by step and make sure to fulfill all the visualization goals mentioned above. Only make the two isosurfaces visible.
+ Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
-# 26. Turbulent Radiative Layer Velocity Field (tcool=1.00, t=30) (trl-velocity_isosurface)
-# Turbulent Radiative Layer simulations of astrophysical mixing processes where cold, dense gas interfaces with hot, dilute gas moving at subsonic velocities.
-# The cold dense gas on the bottom and hot dilute gas on the top becomes unstable to the Kelvin-Helmholtz instability.
-# When turbulence causes mixing, intermediate-temperature gas forms and rapidly cools, creating a net mass transfer from the hot phase to the cold phase—a process relevant to interstellar and circumgalactic environments.
-# Generated using Athena++. Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the turbulent radiative layer velocity field dataset from "trl-velocity_isosurface/data/trl-velocity_isosurface.vti" (VTI format, 256x128x128).
- Extract an isosurface of velocity magnitude at the value 0.8. Color the isosurface by the vx component using the 'Cool to Warm' colormap.
- Add a color bar labeled 'Vx Component'.
- Dark background (RGB: 0.05, 0.05, 0.1). Isometric camera view. Render at 1024x1024.
- Save the paraview state as "trl-velocity_isosurface/results/{agent_mode}/trl-velocity_isosurface.pvsm".
- Save the visualization image as "trl-velocity_isosurface/results/{agent_mode}/trl-velocity_isosurface.png".
- (Optional, if use python script) Save the python script as "trl-velocity_isosurface/results/{agent_mode}/trl-velocity_isosurface.py".
+ Save the visualization image as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.png".
+ (Optional, but must save if use paraview) Save the paraview state as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth isosurface visualization of the TRL velocity field?
-
- 2. Isosurface Structure: Does the isosurface show similar topology and shape as the ground truth?
-
- 3. Surface Features: Are key features and structures on the isosurface similar to the ground truth?
+ 1. Overall Visualization Goal: How well does the result achieve the overall goal of showing the supernova structure with two distinct isosurfaces representing different density regions?
- 4. Color Mapping: Is the color distribution across the isosurface visually similar to the ground truth?
+ 2. Does the red isosurface show low density areas (outside regions) with lower opacity?
+ 3. Does the blue isosurface show high density areas (inside regions) with higher opacity?
-# 27. Supernova Explosion Velocity Field (t=30) (supernova-velocity_streamline)
-# Supernova explosion simulations capturing the physical dynamics of a stellar explosion propagating through a dense interstellar medium.
-# The simulations inject thermal energy of 10^51 ergs at the center of a computational domain, generating a blastwave that sweeps through ambient gas and creates supernova feedback structures—an explosion inside a compression of a monatomic ideal gas modeling conditions in the Milky Way Galaxy interstellar medium.
-# The simulations employ sophisticated physics including radiative cooling and heating.
-# Data source: The Well (Polymathic AI).
+# Case 23: supernova_streamline
- vars:
question: |
- Load the supernova explosion velocity field dataset from "supernova-velocity_streamline/data/supernova-velocity_streamline.vti" (VTI format, 128x128x128 grid).
- Generate streamlines seeded from a line source along the diagonal from (20,20,20) to (108,108,108) with 80 seed points.
- Color streamlines by velocity magnitude using the 'Magma (matplotlib)' colormap.
- Render as tubes with radius 0.4.
- Add a color bar labeled 'Velocity Magnitude'.
- Dark background (RGB: 0.02, 0.0, 0.05). Isometric camera view. Render at 1024x1024.
- Save the paraview state as "supernova-velocity_streamline/results/{agent_mode}/supernova-velocity_streamline.pvsm".
- Save the visualization image as "supernova-velocity_streamline/results/{agent_mode}/supernova-velocity_streamline.png".
- (Optional, if use python script) Save the python script as "supernova-velocity_streamline/results/{agent_mode}/supernova-velocity_streamline.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth streamline visualization of the supernova velocity field?
+ Load the Supernova velocity vector field from "supernova_streamline/data/supernova_streamline_100x100x100_float32_scalar3.raw", the information about this dataset:
+ Supernova Velocity (Vector)
+ Data Scalar Type: float
+ Data Byte Order: Little Endian
+ Data Extent: 100x100x100
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 2. Streamline Patterns: Do the streamlines show similar flow patterns and structures as the ground truth?
+ Create streamlines using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [50, 50, 50], with 200 seed points and a radius of 45.0. Set maximum streamline length to 100.0.
- 3. Streamline Coverage: Is the spatial distribution and density of streamlines similar to the ground truth?
+ Add a "Tube" filter on the stream tracer. Set tube radius to 0.3 with 12 sides.
- 4. Color Mapping: Is the color distribution along streamlines visually similar to the ground truth?
+ Color the tubes by Vorticity magnitude using a diverging colormap with the following RGB control points:
+ - Value 0.0 -> RGB(0.231, 0.298, 0.753) (blue)
+ - Value 0.05 -> RGB(0.865, 0.865, 0.865) (white)
+ - Value 0.5 -> RGB(0.706, 0.016, 0.149) (red)
+ Show the dataset bounding box as an outline (black).
-# 28. MHD Turbulence Velocity Field (t=50) (mhd-turbulence_vorticity)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the MHD turbulence velocity field dataset "mhd-turbulence_vorticity/data/mhd-turbulence_vorticity.vti" (VTI format, 128x128x128 grid).
- Compute the vorticity field (curl of velocity) using the Gradient filter with 'Compute Vorticity' enabled.
- Then compute vorticity magnitude. Perform volume rendering of vorticity magnitude using the 'Plasma (matplotlib)' colormap.
- Set opacity to highlight high-vorticity regions.
- Add a color bar labeled 'Vorticity Magnitude'.
- Black background. Isometric camera. Render at 1024x1024.
- Save the paraview state as "mhd-turbulence_vorticity/results/{agent_mode}/mhd-turbulence_vorticity.pvsm".
- Save the visualization image as "mhd-turbulence_vorticity/results/{agent_mode}/mhd-turbulence_vorticity.png".
- (Optional, if use python script) Save the python script as "mhd-turbulence_vorticity/results/{agent_mode}/mhd-turbulence_vorticity.py".
+ In the pipeline browser panel, hide the stream tracer and only show the tube filter and the outline.
+
+ Use a white background. Render at 1280x1280.
+
+ Save the visualization image as "supernova_streamline/results/{agent_mode}/supernova_streamline.png".
+ (Optional, but must save if use paraview) Save the paraview state as "supernova_streamline/results/{agent_mode}/supernova_streamline.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "supernova_streamline/results/{agent_mode}/supernova_streamline.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "supernova_streamline/results/{agent_mode}/supernova_streamline.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth volume rendering of the MHD turbulence vorticity field?
-
- 2. Vorticity Patterns: Are the vorticity structures and patterns visible in the result similar to the ground truth?
-
- 3. Volume Features: Are high-vorticity regions highlighted with similar opacity and visibility as the ground truth?
+ 1. Central Structure: Is there a dense, chaotic cluster of streamlines near the center of the volume, matching the ground truth?
+ 2. Radial Extensions: Are there long, straight streamline tubes extending radially outward from the central region, similar to the ground truth?
+ 3. Color Mapping: Are the tubes colored by vorticity magnitude using a blue-white-red diverging colormap, with warm colors concentrated near the center and cool colors on the extended lines?
- 4. Color Mapping: Is the color distribution throughout the volume visually similar to the ground truth?
-
-
-# 29. Supernova Explosion Velocity Field (t=40) (supernova-velocity_isosurface)
-# Supernova explosion simulations capturing the physical dynamics of a stellar explosion propagating through a dense interstellar medium.
-# The simulations inject thermal energy of 10^51 ergs at the center of a computational domain, generating a blastwave that sweeps through ambient gas and creates supernova feedback structures—an explosion inside a compression of a monatomic ideal gas modeling conditions in the Milky Way Galaxy interstellar medium.
-# The simulations employ sophisticated physics including radiative cooling and heating.
-# Data source: The Well (Polymathic AI).
+# Case 24: tangaroa_streamribbon
- vars:
question: |
- Load the supernova explosion velocity field from "supernova-velocity_isosurface/data/supernova-velocity_isosurface.vti" (VTI format, 128x128x128).
- Extract an isosurface of velocity magnitude at threshold 0.7. Color the isosurface by the vz component using 'Blue to Red Rainbow' colormap.
- Add a color bar labeled 'Vz Component'.
- Dark background (RGB: 0.0, 0.0, 0.0). Isometric camera view. Render at 1024x1024.
- Save the paraview state as "supernova-velocity_isosurface/results/{agent_mode}/supernova-velocity_isosurface.pvsm".
- Save the visualization image as "supernova-velocity_isosurface/results/{agent_mode}/supernova-velocity_isosurface.png".
- (Optional, if use python script) Save the python script as "supernova-velocity_isosurface/results/{agent_mode}/supernova-velocity_isosurface.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth isosurface visualization of the supernova velocity field?
+ Task:
- 2. Isosurface Structure: Does the isosurface show similar topology and shape as the ground truth?
+ Load the tangaroa dataset from "tangaroa_streamribbon_300x180x120_float32_scalar3.raw", the information about this dataset:
+ tangaroa (Vector)
+ Data Scalar Type: float
+ Data Byte Order: little Endian
+ Data Extent: 300x180x120
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 3. Surface Features: Are key features and structures on the isosurface similar to the ground truth?
+ Apply "streamline tracer" filter, set the "Seed Type" to point cloud, turn off the "show sphere", set the center to [81.6814, 80.708, 23.5093], and radius to 29.9
- 4. Color Mapping: Is the color distribution across the isosurface visually similar to the ground truth?
+ Add "Ribbon" filter to the streamline tracer results and set width to 0.3, set the Display representation to Surface.
+ In pipeline browser panel, hide everything except the ribbon filter results.
-# 30. MHD Magnetic Field (t=60) (mhd-magfield_isosurface)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the MHD magnetic field dataset from "mhd-magfield_isosurface/data/mhd-magfield_isosurface.vti" (VTI format, 128x128x128).
- Extract an isosurface of magnetic field magnitude at threshold 0.8. Color the isosurface by the bx component using 'Turbo' colormap.
- Add a color bar labeled 'Bx Component'.
- Dark navy background (RGB: 0.0, 0.0, 0.1). Isometric camera view. Render at 1024x1024.
- Save the paraview state as "mhd-magfield_isosurface/results/{agent_mode}/mhd-magfield_isosurface.pvsm".
- Save the visualization image as "mhd-magfield_isosurface/results/{agent_mode}/mhd-magfield_isosurface.png".
- (Optional, if use python script) Save the python script as "mhd-magfield_isosurface/results/{agent_mode}/mhd-magfield_isosurface.py".
+ Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+
+ Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+ Save the visualization image as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.png".
+ (Optional, but must save if use paraview) Save the paraview state as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth isosurface visualization of the MHD magnetic field?
-
- 2. Isosurface Structure: Does the isosurface show similar topology and shape as the ground truth?
+ 1. Overall Visualization Goal: Does the result match the ground truth visualization of tangaroa flow structures using ribbon surfaces?
- 3. Surface Features: Are key features and structures on the isosurface similar to the ground truth?
+ 2. Flow Surface Patterns: Do the ribbon surfaces show similar flow patterns and structures as the ground truth?
- 4. Color Mapping: Is the color distribution across the isosurface visually similar to the ground truth?
+ 3. Surface Coverage: Is the spatial distribution and coverage of the flow surfaces similar to the ground truth?
+ 4. Visual Appearance: Do the ribbon surfaces appear similar in width and structure to the ground truth?
-# 31. Turbulence Gravity Cooling Velocity (temp=100K, dens=0.445, metal=Z, t=10) (tgc-velocity_contour)
-# Turbulence-gravity-cooling simulations modeling turbulent fluid with gravity representing the interstellar medium in galaxies.
-# These simulations capture the formation of dense filaments that seed star formation, with filament frequency and timescales varying based on cooling strength.
-# The dataset encompasses three density regimes with systematically varied initial temperatures and metallicity levels representing different cosmic epochs,
-# governed by coupled equations for pressure, density, momentum, and internal energy incorporating gravitational forces, viscosity, and radiative heating/cooling.
-# Data source: The Well (Polymathic AI).
+# Case 25: tgc-velocity_contour
- vars:
question: |
- Load the turbulence-gravity-cooling velocity field dataset from "tgc-velocity_contour/data/tgc-velocity_contour.vti" (VTI format, 64x64x64).
- Extract a slice at z=32 and color it by velocity magnitude using 'Viridis (matplotlib)' colormap.
- Also add contour lines of velocity magnitude on the same slice at values [0.3, 0.6, 0.9, 1.2] using the Contour filter on the slice output.
- Display contour lines in white. Add a color bar labeled 'Velocity Magnitude'.
+ Load the turbulence-gravity-cooling velocity field dataset from "tgc-velocity_contour/data/tgc-velocity_contour.vti" (VTI format, 64x64x64).
+ Extract a slice at z=32 and color it by velocity magnitude using 'Viridis (matplotlib)' colormap.
+ Also add contour lines of velocity magnitude on the same slice at values [0.3, 0.6, 0.9, 1.2] using the Contour filter on the slice output.
+ Display contour lines in white. Add a color bar labeled 'Velocity Magnitude'.
Light gray background (RGB: 0.9, 0.9, 0.9). Top-down camera. Render at 1024x1024.
- Save the paraview state as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.pvsm".
Save the visualization image as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.png".
- (Optional, if use python script) Save the python script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.py".
+ (Optional, but must save if use paraview) Save the paraview state as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
@@ -997,216 +829,111 @@
4. Color Mapping: Is the color distribution on the slice visually similar to the ground truth?
-
-# 32. Rayleigh-Taylor Instability Velocity Field (t=80) (rti-velocity_slices)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
+# Case 26: tornado
- vars:
question: |
- Load the Rayleigh-Taylor instability velocity field from "rti-velocity_slices/data/rti-velocity_slices.vti" (VTI format, 128x128x128).
- Create three orthogonal slices: at x=64 (YZ-plane), y=64 (XZ-plane), and z=64 (XY-plane).
- Color all three slices by velocity magnitude using the 'Turbo' colormap.
- Add a color bar labeled 'Velocity Magnitude'.
- Dark background (RGB: 0.05, 0.05, 0.05). Set an isometric camera view that shows all three slices. Render at 1024x1024.
- Save the paraview state as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.pvsm".
- Save the visualization image as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.png".
- (Optional, if use python script) Save the python script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.py".
- Do not save any other files, and always save the visualization image
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth three-orthogonal-slice visualization of the RTI velocity field?
+ Load the Tornado vector field from "tornado/data/tornado_64x64x64_float32_scalar3.raw", the information about this dataset:
+ Tornado (Vector)
+ Data Scalar Type: float
+ Data Byte Order: Little Endian
+ Data Extent: 64x64x64
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 2. Slice Patterns: Do all three slices show similar patterns and structures as the ground truth?
+ Create a streamline visualization using a "Stream Tracer" filter with "Point Cloud" seed type. Set the seed center to [31.5, 31.5, 47.25], radius 12.6, and maximum streamline length to 512.0. Add a "Tube" filter (radius 0.25) on the stream tracer. Color the tubes by Velocity magnitude using the 'Cool to Warm (Diverging)' colormap. Also display the stream tracer directly with line width 5.0 and "Render Lines As Tubes" enabled.
- 3. Slice Positioning: Are the three orthogonal slices positioned and oriented similarly to the ground truth?
+ Add a "Glyph" filter on the original data using Arrow glyph type. Orient arrows by the Velocity vector and scale by Velocity magnitude with a scale factor of 25.0. Set maximum number of sample points to 2500. Color glyphs by Velocity magnitude using the same colormap.
- 4. Color Mapping: Is the color distribution across all slices visually similar to the ground truth?
+ Add an "Outline" filter to display the dataset bounding box (black).
-# 33. MHD Magnetic Field Stream Ribbon (t=20) (mhd-magfield_streamribbon)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the MHD magnetic field dataset from "mhd-magfield_streamribbon/data/mhd-magfield_streamribbon.vti" (VTI format, 128x128x128 grid with components bx, by, bz).
- Generate a stream ribbon seeded from a line source along the y-axis at x=64, z=64 (from y=20 to y=108), with 30 seed points.
- The stream ribbon should be traced in both forward and backward directions along the magnetic field lines.
- Color the stream ribbon by magnetic field magnitude using the 'Cool to Warm' colormap. Enable surface lighting with specular reflection for better 3D perception.
- Add a color bar labeled 'Magnetic Field Magnitude'.
- Use a dark navy background (RGB: 0.0, 0.0, 0.12). Set an isometric camera view. Render at 1024x1024 resolution.
- Save the paraview state as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.pvsm".
- Save the visualization image as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.png".
- (Optional, if use python script) Save the python script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.py".
+ Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Show both color bar and coordinate axes.
+ Save the visualization image as "tornado/results/{agent_mode}/tornado.png".
+ (Optional, but must save if use paraview) Save the paraview state as "tornado/results/{agent_mode}/tornado.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "tornado/results/{agent_mode}/tornado.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "tornado/results/{agent_mode}/tornado.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth stream ribbon visualization of the MHD magnetic field?
-
- 2. Surface Patterns: Does the stream ribbon show similar flow patterns and structures as the ground truth?
+ 1. Vortex Structure: Is a funnel-shaped vortex core visible with streamlines spiraling around a central vertical axis, matching the ground truth?
+ 2. Glyph Presence: Are arrow glyphs distributed throughout the volume showing velocity direction, similar to the ground truth?
+ 3. Color Mapping: Are both the streamline tubes and arrow glyphs colored by velocity magnitude using a blue-to-red diverging colormap, matching the ground truth color distribution?
- 3. Surface Coverage: Is the spatial extent and shape of the stream ribbon similar to the ground truth?
-
- 4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
-
-
-# 34. Rayleigh-Taylor Instability Stream Ribbon (t=60) (rti-velocity_streamribbon)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
+# Case 27: twoswirls_streamribbon
- vars:
question: |
- Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_streamribbon/data/rti-velocity_streamribbon.vti" (VTI format, 128x128x128 grid).
- Generate a stream ribbon seeded from a circular ring at y=64 (the mixing interface), centered at x=64, z=64 with radius 30, using 40 seed points distributed around the circle.
- Trace the stream ribbon in both directions along the velocity field. Color the stream ribbon by the vy (vertical velocity) component using the 'Cool to Warm (Extended)' diverging colormap centered at zero.
- Set surface opacity to 0.85 for slight transparency. Add a color bar labeled 'Vertical Velocity (vy)'.
- Use a black background (RGB: 0.0, 0.0, 0.0).
- Set camera to view at 45 degrees elevation to show the mushroom-shaped instability structures. Render at 1024x1024 resolution.
- Save the paraview state as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.pvsm".
- Save the visualization image as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.png".
- (Optional, if use python script) Save the python script as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.py".
- Do not save any other files, and always save the visualization image
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth stream ribbon visualization of the RTI velocity field?
+ Load the Two Swirls vector field from "twoswirls_streamribbon/data/twoswirls_streamribbon_64x64x64_float32_scalar3.raw", the information about this dataset:
+ Two Swirls (Vector)
+ Data Scalar Type: float
+ Data Byte Order: Little Endian
+ Data Extent: 64x64x64
+ Number of Scalar Components: 3
+ Data loading is very important, make sure you correctly load the dataset according to their features.
- 2. Surface Patterns: Does the stream ribbon show similar flow patterns and structures as the ground truth?
+ Create two stream ribbons using "Stream Tracer" filters with "Line" seed type (resolution 25 points each), and apply a "Ribbon" filter (width 2.5) to each:
+ - Stream Ribbon 1: Line seed from [16, 10, 32] to [16, 54, 32]. Ribbon colored solid green (RGB: 0.2, 0.7, 0.3) with opacity 0.35.
+ - Stream Ribbon 2: Line seed from [48, 10, 32] to [48, 54, 32]. Ribbon colored solid blue (RGB: 0.2, 0.4, 0.85) with opacity 0.35.
- 3. Surface Coverage: Is the spatial extent and shape of the stream ribbon similar to the ground truth?
- 4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
+ Show the dataset bounding box as an outline (black, opacity 0.3).
+ In the pipeline browser panel, hide all stream tracers and only show the ribbon filters and the outline.
-# 35. Supernova Explosion Pathlines (Multi-timestep) (supernova-velocity_pathline)
-# Supernova explosion simulations capturing the physical dynamics of a stellar explosion propagating through a dense interstellar medium.
-# The simulations inject thermal energy of 10^51 ergs at the center of a computational domain, generating a blastwave that sweeps through ambient gas and creates supernova feedback structures—an explosion inside a compression of a monatomic ideal gas modeling conditions in the Milky Way Galaxy interstellar medium.
-# The simulations employ sophisticated physics including radiative cooling and heating.
-# Data source: The Well (Polymathic AI).
-- vars:
- question: |
- Load the supernova explosion velocity field time series from "supernova-velocity_pathline/data/supernova-velocity_pathline_{timestep}.vti", where "timestep" in {0000, 0005, 0010, 0015, 0020} (5 timesteps, VTI format, 128x128x128 grid each).
- Visualize the temporal evolution of flow patterns by generating streamlines from each timestep and overlaying them.
- Seed 50 particles from a spherical shell centered at (64, 64, 64) with radius 20 (near the explosion center).
- For each timestep, trace streamlines in the forward direction with maximum length 80.
- Render streamlines as tubes with radius 0.25. Color each timestep differently to show temporal progression (blue for t=0 through red for t=20).
- Also include a magnitude-colored version using the 'Turbo' colormap for the final timestep.
- Add a color bar labeled 'Velocity Magnitude'. Use a dark background (RGB: 0.02, 0.0, 0.04). Set an isometric camera view. Render at 1024x1024 resolution.
- Save the paraview state as "supernova-velocity_pathline/results/{agent_mode}/supernova-velocity_pathline.pvsm".
- Save the visualization image as "supernova-velocity_pathline/results/{agent_mode}/supernova-velocity_pathline.png".
- (Optional, if use python script) Save the python script as "supernova-velocity_pathline/results/{agent_mode}/supernova-velocity_pathline.py".
+ Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+ Save the visualization image as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.png".
+ (Optional, but must save if use paraview) Save the paraview state as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth pathline visualization of the supernova velocity field?
-
- 2. Pathline Patterns: Do the pathlines show similar trajectories and flow structures as the ground truth?
-
- 3. Pathline Coverage: Is the spatial distribution and density of pathlines similar to the ground truth?
+ 1. Swirl Separation: Are there two visually distinct swirl structures (one on the left and one on the right), matching the spatial arrangement in the ground truth?
+ 2. Stream Ribbon Shape: Do the ribbon surfaces show wrapped, swirling sheet-like structures similar to the ground truth?
+ 3. Color and Transparency: Are the stream ribbons rendered with distinct colors (green and blue) and semi-transparency, similar to the ground truth?
- 4. Color Mapping: Is the color distribution along pathlines visually similar to the ground truth?
-
-
-# 36. MHD Turbulence Path Surface (Multi-timestep) (mhd-turbulence_pathsurface)
-# Isothermal magnetohydrodynamic (MHD) simulations capturing compressible turbulence phenomena relevant to astrophysical systems.
-# MHD turbulence is an essential component of the solar wind, galaxy formation, and interstellar medium (ISM) dynamics.
-# The simulations model fluid dynamics governed by conservation equations for mass, momentum, and magnetic fields, exploring MHD flows across multiple regimes—subsonic and supersonic velocities, as well as sub-Alfvénic and super-Alfvénic magnetic conditions.
-# Three field types are captured: density (scalar), velocity (vector), and magnetic field (vector).
-# Data source: The Well (Polymathic AI)
+# Case 28: vortex
- vars:
question: |
- Load the MHD turbulence velocity field time series from "mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
- Visualize the temporal evolution by generating stream ribbons (ribbons) from each timestep with varying opacity.
- Use a line seed along the z-axis at x=64, y=64 (from z=40 to z=88) with 20 seed points.
- Trace streamlines bidirectionally with maximum length 150. Create ribbon surfaces from the streamlines with width 0.8.
- Apply progressive opacity (0.3 for t=0, increasing to 0.9 for t=40) to show temporal layering. Color all surfaces by velocity magnitude using the 'Viridis (matplotlib)' colormap.
- Add a color bar labeled 'Velocity Magnitude'. Use a dark gray background (RGB: 0.08, 0.08, 0.1). Set an isometric camera view. Render at 1024x1024.
- Save the paraview state as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.pvsm".
- Save the visualization image as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.png".
- (Optional, if use python script) Save the python script as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.py".
- Do not save any other files, and always save the visualization image.
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth path surface visualization of the MHD turbulence velocity field?
-
- 2. Surface Patterns: Does the path surface show similar flow patterns and structures as the ground truth?
+ Task:
- 3. Surface Coverage: Is the spatial extent and shape of the path surface similar to the ground truth?
+ Load the vortex dataset from "vortex/data/vortex_128x128x128_float32.raw", the information about this dataset:
+ vortex (Scalar)
+ Data Scalar Type: float
+ Data Byte Order: little Endian
+ Data Extent: 128x128x128
+ Number of Scalar Components: 1
- 4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
+ Instructions:
+ 1. Load the dataset into ParaView.
-# 37. Rayleigh-Taylor Instability Streaklines (Multi-timestep) (rti-velocity_streakline)
-# Rayleigh-Taylor instability simulations examining how varying spectral characteristics and random phase components influence the development of turbulent mixing.
-# The simulations investigate three key physical aspects: the impact of coherence on randomized initial conditions, how initial energy spectrum shapes affect resulting flow structures, and the transition from Boussinesq to non-Boussinesq regimes where mixing becomes asymmetric.
-# The dataset captures the self-similar growth of the turbulent mixing zone, enabling validation of the dimensionless mixing parameter and observation of the characteristic energy cascade.
-# Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the Rayleigh-Taylor instability velocity field time series from "rti-velocity_streakline/data/rti-velocity_streakline_{timestep}.vti", where "timestep" in {0030, 0040, 0050, 0060, 0070} (5 timesteps, VTI format, 128x128x128 grid each).
- Visualize temporal flow evolution by generating streamlines from 4 fixed injection points at each timestep.
- Place injection points at y=64 (the mixing interface): at (32, 64, 64), (64, 64, 32), (96, 64, 64), and (64, 64, 96).
- For each timestep, trace streamlines bidirectionally with maximum length 100.
- Render as tubes with radius 0.4. Color each timestep differently (blue for t=30 through red for t=70) with increasing opacity for later times.
- Add a final layer colored by vy (vertical velocity) using the 'Cool to Warm' diverging colormap.
- Add a color bar labeled 'Vertical Velocity (vy)'.
- Use a black background. Set camera to view from an elevated angle (elevation 35 degrees). Render at 1024x1024 resolution.
- Save the paraview state as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.pvsm".
- Save the visualization image as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.png".
- (Optional, if use python script) Save the python script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.py".
- Do not save any other files, and always save the visualization image
- assert:
- - type: llm-rubric
- subtype: vision
- value: |
- 1. Overall Visualization Goal: Does the result match the ground truth streakline visualization of the RTI velocity field?
+ 2. Leverage "contour" filter to achieve iso-surface rendering. In pipeline browser panel, hide everything except the "contour" fileter.
- 2. Streakline Patterns: Do the streaklines show similar trajectories and flow structures as the ground truth?
+ 3. In properties panel of "contour" filter, set isosurface value to -0.2, use Solid Color and set the color as beige.
- 3. Streakline Coverage: Is the spatial distribution of streaklines from the injection points similar to the ground truth?
+ 4. Enable Ambient occlusion by toggle the "Use Ambient Occlusion" button in the Render Passes.
- 4. Color Mapping: Is the color distribution along streaklines visually similar to the ground truth?
+ 5. Add head light with light inspector, set "Coords" as Camera, "Intentsity" to 0.2, Type to "Directional".
+ 6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
-# 38. Turbulent Radiative Layer Timelines (Multi-timestep) (trl-velocity_timeline)
-# Turbulent Radiative Layer simulations of astrophysical mixing processes where cold, dense gas interfaces with hot, dilute gas moving at subsonic velocities.
-# The cold dense gas on the bottom and hot dilute gas on the top becomes unstable to the Kelvin-Helmholtz instability.
-# When turbulence causes mixing, intermediate-temperature gas forms and rapidly cools, creating a net mass transfer from the hot phase to the cold phase—a process relevant to interstellar and circumgalactic environments.
-# Generated using Athena++. Data source: The Well (Polymathic AI)
-- vars:
- question: |
- Load the turbulent radiative layer velocity field time series from "trl-velocity_timeline/data/trl-velocity_timeline_{timestep}.vti", where "timestep" in {0020, 0030, 0040, 0050, 0060} (5 timesteps, VTI format, 256x128x128 grid each).
- Visualize temporal evolution by generating streamlines from a line seed at each timestep.
- Seed 50 points along the z-axis at x=128, y=64 (from z=20 to z=108). For each timestep, trace streamlines bidirectionally with maximum length 150.
- Render as tubes with radius 0.5. Color each timestep using Spectral-like colors (blue for t=20, cyan for t=30, green for t=40, yellow for t=50, red for t=60) with increasing opacity for later times.
- Add a magnitude-colored layer using the 'Spectral' colormap for reference.
- Add a color bar labeled 'Velocity Magnitude'.
- Use a dark background (RGB: 0.0, 0.0, 0.02). Set camera to an oblique view (azimuth 45, elevation 20) for the elongated domain. Render at 1024x1024 resolution.
- Save the paraview state as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.pvsm".
- Save the visualization image as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.png".
- (Optional, if use python script) Save the python script as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.py".
+ 7. Save your work:
+ Save the visualization image as "vortex/results/{agent_mode}/vortex.png".
+ (Optional, but must save if use paraview) Save the paraview state as "vortex/results/{agent_mode}/vortex.pvsm".
+ (Optional, but must save if use pvpython script) Save the python script as "vortex/results/{agent_mode}/vortex.py".
+ (Optional, but must save if use VTK) Save the cxx code script as "vortex/results/{agent_mode}/vortex.cxx"
Do not save any other files, and always save the visualization image.
assert:
- type: llm-rubric
subtype: vision
value: |
- 1. Overall Visualization Goal: Does the result match the ground truth timeline visualization of the TRL velocity field?
+ 1. Overall Visualization Goal: Does the result match the ground truth isosurface rendering of the vortex scalar field?
- 2. Timeline Patterns: Do the timelines show similar trajectories and temporal evolution as the ground truth?
+ 2. Isosurface Structure: Does the isosurface show the same vortex structure and topology as the ground truth?
- 3. Timeline Coverage: Is the spatial distribution and density of timelines similar to the ground truth?
+ 3. Surface Appearance: Does the surface color and shading appear similar to the ground truth?
- 4. Color Mapping: Is the color distribution along timelines visually similar to the ground truth?
\ No newline at end of file
+ 4. Visualization Clarity: Are the vortex features clearly visible and comparable to the ground truth?
diff --git a/main/ABC/GS/abc_glyph.png b/main/ABC/GS/ABC_gs.png
similarity index 100%
rename from main/ABC/GS/abc_glyph.png
rename to main/ABC/GS/ABC_gs.png
diff --git a/main/ABC/GS/abc_glyph.pvsm b/main/ABC/GS/ABC_gs.pvsm
similarity index 100%
rename from main/ABC/GS/abc_glyph.pvsm
rename to main/ABC/GS/ABC_gs.pvsm
diff --git a/main/ABC/GS/abc_glyph.py b/main/ABC/GS/ABC_gs.py
similarity index 100%
rename from main/ABC/GS/abc_glyph.py
rename to main/ABC/GS/ABC_gs.py
diff --git a/main/ABC/data/abc.txt b/main/ABC/data/ABC.txt
similarity index 100%
rename from main/ABC/data/abc.txt
rename to main/ABC/data/ABC.txt
diff --git a/main/ABC/data/abc.raw b/main/ABC/data/ABC_128x128x128_float32_scalar3.raw
similarity index 100%
rename from main/ABC/data/abc.raw
rename to main/ABC/data/ABC_128x128x128_float32_scalar3.raw
diff --git a/main/ABC/task_description.txt b/main/ABC/task_description.txt
index 2eeed2c458bf12bae11efce65f6c47bf6d3d9723..8513ac2fe76f451d8a2d32cd3f76b1e779add2ae 100644
--- a/main/ABC/task_description.txt
+++ b/main/ABC/task_description.txt
@@ -1,4 +1,4 @@
-Load the ABC (Arnold-Beltrami-Childress) flow vector field from "ABC/data/abc.raw", the information about this dataset:
+Load the ABC (Arnold-Beltrami-Childress) flow vector field from "ABC/data/ABC_128x128x128_float32_scalar3.raw", the information about this dataset:
ABC Flow (Vector)
Data Scalar Type: float
Data Byte Order: Little Endian
@@ -14,9 +14,9 @@ Color the tubes by Vorticity magnitude using the 'Cool to Warm (Diverging)' colo
Show the dataset bounding box as an outline.
-Use a dark background (RGB: 0.1, 0.1, 0.15). Render at 1024x1024.
+Use a white background. Render at 1024x1024.
-Save the paraview state as "ABC/results/{agent_mode}/ABC.pvsm".
Save the visualization image as "ABC/results/{agent_mode}/ABC.png".
-(Optional, if use python script) Save the python script as "ABC/results/{agent_mode}/ABC.py".
-Do not save any other files, and always save the visualization image.
+(Optional, but must save if use paraview) Save the paraview state as "ABC/results/{agent_mode}/ABC.pvsm".
+(Optional, but must save if use python script) Save the python script as "ABC/results/{agent_mode}/ABC.py".
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/Bernard/GS/bernard_streamline.png b/main/Bernard/GS/Bernard_gs.png
similarity index 100%
rename from main/Bernard/GS/bernard_streamline.png
rename to main/Bernard/GS/Bernard_gs.png
diff --git a/main/Bernard/GS/bernard_streamline.pvsm b/main/Bernard/GS/Bernard_gs.pvsm
similarity index 100%
rename from main/Bernard/GS/bernard_streamline.pvsm
rename to main/Bernard/GS/Bernard_gs.pvsm
diff --git a/main/Bernard/GS/bernard_streamline.py b/main/Bernard/GS/Bernard_gs.py
similarity index 100%
rename from main/Bernard/GS/bernard_streamline.py
rename to main/Bernard/GS/Bernard_gs.py
diff --git a/main/Bernard/data/bernard.txt b/main/Bernard/data/Bernard.txt
similarity index 100%
rename from main/Bernard/data/bernard.txt
rename to main/Bernard/data/Bernard.txt
diff --git a/main/Bernard/data/bernard.raw b/main/Bernard/data/Bernard_128x32x64_float32_scalar3.raw
similarity index 100%
rename from main/Bernard/data/bernard.raw
rename to main/Bernard/data/Bernard_128x32x64_float32_scalar3.raw
diff --git a/main/Bernard/task_description.txt b/main/Bernard/task_description.txt
index d42369f66edcdbee1a3ca07c0b566b1fc427b18d..c9ad7af4cce24767aa7487067cc25599a5858fc6 100644
--- a/main/Bernard/task_description.txt
+++ b/main/Bernard/task_description.txt
@@ -1,4 +1,4 @@
-Load the Rayleigh-Benard convection vector field from "Bernard/data/bernard.raw", the information about this dataset:
+Load the Rayleigh-Benard convection vector field from "Bernard/data/Bernard_128x32x64_float32_scalar3.raw", the information about this dataset:
Rayleigh-Benard Convection (Vector)
Data Scalar Type: float
Data Byte Order: Little Endian
@@ -16,7 +16,7 @@ In the pipeline browser panel, hide all stream tracers and only show the tube fi
Use a gray-blue background (RGB: 0.329, 0.349, 0.427). Render at 1280x1280. Do not show a color bar.
-Save the paraview state as "Bernard/results/{agent_mode}/Bernard.pvsm".
Save the visualization image as "Bernard/results/{agent_mode}/Bernard.png".
-(Optional, if use python script) Save the python script as "Bernard/results/{agent_mode}/Bernard.py".
+(Optional, but must save if use paraview) Save the paraview state as "Bernard/results/{agent_mode}/Bernard.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "Bernard/results/{agent_mode}/Bernard.py".
Do not save any other files, and always save the visualization image.
diff --git a/main/argon-bubble/task_description.txt b/main/argon-bubble/task_description.txt
index 322b7f066abd50d9f2a483b8e3908a242b0c28c0..0ce69807f9086c949632eba5a5e1e0f7c2679727 100644
--- a/main/argon-bubble/task_description.txt
+++ b/main/argon-bubble/task_description.txt
@@ -7,6 +7,9 @@ Generate a visualization image of the Argon Bubble scalar field dataset with the
4) Set the viewpoint parameters as: [0, 450, 0] to position; [0, 0, -15] to focal point; [0, 0, -1] to camera up direction
5) Visualization image resolution is 1024x1024. White background. Shade turned off. Volume rendering ray casting sample distance is 0.1
6) Don't show color/scalar bar or coordinate axes.
-7) Save the visualization image as a png file "argon-bubble/results/{agent_mode}/argon-bubble.png"
-8) (Option 1) Save the paraview state as "argon-bubble/results/{agent_mode}/argon-bubble.pvsm" if you are using ParaView as the visualization tool
-9) (Option 2) Save the cxx code script as "argon-bubble/results/{agent_mode}/argon-bubble.cxx" if you are using VTK as the visualization tool
\ No newline at end of file
+
+Save the visualization image as "argon-bubble/results/{agent_mode}/argon-bubble.png".
+(Optional, but must save if use paraview) Save the paraview state as "argon-bubble/results/{agent_mode}/argon-bubble.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "argon-bubble/results/{agent_mode}/argon-bubble.py".
+(Optional, but must save if use VTK) Save the cxx code script as "argon-bubble/results/{agent_mode}/argon-bubble.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/bonsai/GS/bonsai_gs.png b/main/bonsai/GS/bonsai_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..bdbc8bb9de4d66e03bc2a2a74efa0355700d3147
--- /dev/null
+++ b/main/bonsai/GS/bonsai_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2255b5eff8acadf5a01d96262db06ddcab209e8ad9945a8057b5a1900529110a
+size 548206
diff --git a/main/bonsai/GS/bonsai_gs.pvsm b/main/bonsai/GS/bonsai_gs.pvsm
index ac26f52d295649eb9853ed29fb0e6e5155b249f0..fd757b93f42c00c0f6500f8d05d585b66a8a7efd 100644
--- a/main/bonsai/GS/bonsai_gs.pvsm
+++ b/main/bonsai/GS/bonsai_gs.pvsm
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:aa03b91b2e71ffbfef15c26dce85b93887ee4ffd1461759c11a46f640f604ac0
-size 235372
+oid sha256:4f5d6f7b0d57498866973037a38f680ae791e0e90f82101d7deebfc2b15990e1
+size 240130
diff --git a/main/bonsai/task_description.txt b/main/bonsai/task_description.txt
index 877165cccfcaa43edab8f0c41e9cff7fa4af3822..60a7067099bdd74713206ff2f86c2812707dbe20 100644
--- a/main/bonsai/task_description.txt
+++ b/main/bonsai/task_description.txt
@@ -11,4 +11,9 @@ Then visualize it with volume rendering, modify the transfer function and reach
Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-Finally, save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm"
\ No newline at end of file
+Use a white background. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+Save the visualization image as "bonsai/results/{agent_mode}/bonsai.png".
+(Optional, but must save if use paraview) Save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "bonsai/results/{agent_mode}/bonsai.py".
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/carp/GS/carp_gs.png b/main/carp/GS/carp_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..39a4264c9517118dcaab85c0b7fd2b953c983969
--- /dev/null
+++ b/main/carp/GS/carp_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bb7776c8746e7269df5ba0844a7040f54c20d6a7149d503421be6629ccbc79a
+size 314694
diff --git a/main/carp/task_description.txt b/main/carp/task_description.txt
index 88c05da7692dddb0bd32957d1a1e862c4995347d..0f86926f1edcc3043ad803bc5c7f1b4ff374bc68 100644
--- a/main/carp/task_description.txt
+++ b/main/carp/task_description.txt
@@ -31,6 +31,12 @@ B. ~22%
C. ~30%
D. ~40%
-6. Save your work:
-Save the ParaView state as "carp/results/{agent_mode}/carp.pvsm".
-Save the answers to the analysis questions in plain text as "carp/results/{agent_mode}/answers.txt".
\ No newline at end of file
+6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+7. Save your work:
+Save the visualization image as "carp/results/{agent_mode}/carp.png".
+Save the answers to the analysis questions in plain text as "carp/results/{agent_mode}/answers.txt".
+(Optional, but must save if use paraview) Save the paraview state as "carp/results/{agent_mode}/carp.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "carp/results/{agent_mode}/carp.py".
+(Optional, but must save if use VTK) Save the cxx code script as "carp/results/{agent_mode}/carp.cxx"
+Do not save any other files, and always save the visualization image and the text file.
\ No newline at end of file
diff --git a/main/chameleon_isosurface/task_description.txt b/main/chameleon_isosurface/task_description.txt
index e71b42a2afc33e90cdbcdd296b6104a321e47e04..a8ffcc492128b2a5945d9dd514f62071563f2b3c 100644
--- a/main/chameleon_isosurface/task_description.txt
+++ b/main/chameleon_isosurface/task_description.txt
@@ -9,6 +9,9 @@ Generate a visualization image of 2 isosurfaces of the Chameleon scalar field da
6) White background
7) Visualization image resolution is 1024x1024
8) Don't show color/scalar bar or coordinate axes.
-9) Save the visualization image as a png file "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.png"
-10) (Option 1) Save the paraview state as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.pvsm" if you are using ParaView as the visualization tool
-11) (Option 2) Save the cxx code script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.cxx" if you are using VTK as the visualization tool
\ No newline at end of file
+
+Save the visualization image as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.png".
+(Optional, but must save if use paraview) Save the paraview state as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.py".
+(Optional, but must save if use VTK) Save the cxx code script as "chameleon_isosurface/results/{agent_mode}/chameleon_isosurface.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/chameleon_volvis/GS/chameleon_gs.pvsm b/main/chameleon_volvis/GS/chameleon_gs.pvsm
deleted file mode 100644
index 7d6714ec5ab8a064d18ef98cd9d13a6cd0813d83..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/GS/chameleon_gs.pvsm
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9f19cabc7c0a6fd449d07b11fd99e45838aeacdc11ce082d010fd522b3151a4d
-size 248359
diff --git a/main/chameleon_volvis/GS/gs_diagonal_view.png b/main/chameleon_volvis/GS/gs_diagonal_view.png
deleted file mode 100644
index cabe27c5ee62fe756b978cfae2a42b551c376dc5..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/GS/gs_diagonal_view.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5a501d057b7ebc55758d2c9cd235fd9612c616ac349ddf4353c5acddcf794dad
-size 238962
diff --git a/main/chameleon_volvis/GS/gs_front_view.png b/main/chameleon_volvis/GS/gs_front_view.png
deleted file mode 100644
index d726170b985832b13db3b1b360a13b19eead85d8..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/GS/gs_front_view.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ce6b385fe4af6f0377168bbd4569753f7cff98edbf936f41f35efe40115ecd5d
-size 301162
diff --git a/main/chameleon_volvis/GS/gs_side_view.png b/main/chameleon_volvis/GS/gs_side_view.png
deleted file mode 100644
index 73f8e479a25f84dd798e1b7e4a7a4c8022f9268c..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/GS/gs_side_view.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1b51228a266cf780126c91afd5de6f8990e442503a2d78d884f23d8a7c63acd0
-size 131681
diff --git a/main/chameleon_volvis/data/chameleon.txt b/main/chameleon_volvis/data/chameleon.txt
deleted file mode 100644
index 012f111f7ccd4288866d1b972b669396ba101765..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/data/chameleon.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-chameleon (Scalar)
-Data Scalar Type: float
-Data Byte Order: little Endian
-Data Extent: 256x256x270
-Number of Scalar Components: 1
diff --git a/main/chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw b/main/chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw
deleted file mode 100644
index db90cbd9f6294c937eeb9b6c5d78beafb039d1ec..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bff2380bd60471fedc7e67d016387d08073dbe119733944b9cbb8c6196201cc4
-size 70778880
diff --git a/main/chameleon_volvis/evaluation_results/mcp/test_result_1758566304.json b/main/chameleon_volvis/evaluation_results/mcp/test_result_1758566304.json
deleted file mode 100644
index abe5549fd8b55139be13b24c36db3e6c38785f6b..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/evaluation_results/mcp/test_result_1758566304.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
- "timestamp": "2025-09-22T14:38:24.128177",
- "case_name": "chameleon",
- "result": {
- "case_name": "chameleon",
- "status": "failed",
- "start_time": "2025-09-22T14:38:24.127714",
- "task_description": "Your agent_mode is \"mcp\", use it when saving results. Your working directory is \"/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench/benchmark/cases\", and you should have access to it. In the following prompts, we will use relative path with respect to your working path. But remember, when you load or save any file, always stick to absolute path.\n\nTask:\n\nLoad the chameleon dataset from \"chameleon/data/chameleon_256x256x270_float32.raw\", the information about this dataset:\nchameleon (Scalar)\nData Scalar Type: float\nData Byte Order: little Endian\nData Extent: 256x256x270\nNumber of Scalar Components: 1\nData loading is very important, make sure you correctly load the dataset according to their features.\n\nApply the volume rendering to visualize the chameleon dataset\n\nAdjust the transfer function to highlight the bony structures and skin in an X-ray style.\n\nAdjust the camera position and focus on the head part of the chameleon\n\nPlease think step by step and make sure to fulfill all the visualization goals mentioned above.\n\nFinally, save the paraview state as \"chameleon/results/{agent_mode}/chameleon.pvsm\"",
- "llm_rubric": "1. Overall Visualization Goal: Does the result present a clean, X-ray–style volume rendering where the chameleon's bony structures are clearly emphasized and soft tissue is faint but discernible?\n\n2. Data Loading Correctness: Is the RAW volume loaded with the specified metadata (float32, little-endian, 256×256×270, 1 component) so that the histogram looks reasonable and the anatomy is not flipped or distorted?\n\n3. Transfer Function Quality: Does the grayscale transfer function make low-intensity tissue mostly transparent while assigning higher opacity to bones/skin ridges, yielding good depth cues without over-saturation or banding?\n\n4. Camera & Framing: Is the camera positioned and zoomed to focus on the chameleon's head, keeping it sharply framed (no clipping), with a stable viewpoint that highlights key anatomical details?",
- "response": "",
- "error": "'TinyAgent' object has no attribute 'submit'",
- "token_usage": {
- "input_tokens": 254,
- "output_tokens": 0,
- "total_tokens": 254
- },
- "end_time": "2025-09-22T14:38:24.128145",
- "duration_seconds": 0.000431
- }
-}
\ No newline at end of file
diff --git a/main/chameleon_volvis/task_description.txt b/main/chameleon_volvis/task_description.txt
deleted file mode 100644
index aeb00ffee1699d800df2e41a5a50ea11c6318d5a..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/task_description.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Task:
-
-Load the chameleon dataset from "chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw", the information about this dataset:
-chameleon (Scalar)
-Data Scalar Type: float
-Data Byte Order: little Endian
-Data Extent: 256x256x270
-Number of Scalar Components: 1
-Data loading is very important, make sure you correctly load the dataset according to their features.
-
-Apply the volume rendering to visualize the chameleon dataset
-
-Adjust the transfer function to highlight the bony structures and skin in an X-ray style.
-
-Adjust the camera position and focus on the head part of the chameleon
-
-Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-
-Finally, save the paraview state as "chameleon/results/{agent_mode}/chameleon.pvsm"
diff --git a/main/chameleon_volvis/test_results/pvpython/test_result_1759261119.json b/main/chameleon_volvis/test_results/pvpython/test_result_1759261119.json
deleted file mode 100644
index 5b569d3ce05fdf22c0626a02b724c1db48edda81..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/test_results/pvpython/test_result_1759261119.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
- "timestamp": "2025-09-30T15:38:39.248241",
- "case_name": "chameleon",
- "status": "failed",
- "duration": 0.144628,
- "token_usage": {
- "input_tokens": 261,
- "output_tokens": 0,
- "total_tokens": 261
- },
- "response": "",
- "task_description": "Your agent_mode is \"pvpython\", use it when saving results. Your working directory is \"/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench/benchmark/../SciVisAgentBench-tasks/main\", and you should have access to it. In the following prompts, we will use relative path with respect to your working path. But remember, when you load or save any file, always stick to absolute path.\n\nTask:\n\nLoad the chameleon dataset from \"chameleon/data/chameleon_256x256x270_float32.raw\", the information about this dataset:\nchameleon (Scalar)\nData Scalar Type: float\nData Byte Order: little Endian\nData Extent: 256x256x270\nNumber of Scalar Components: 1\nData loading is very important, make sure you correctly load the dataset according to their features.\n\nApply the volume rendering to visualize the chameleon dataset\n\nAdjust the transfer function to highlight the bony structures and skin in an X-ray style.\n\nAdjust the camera position and focus on the head part of the chameleon\n\nPlease think step by step and make sure to fulfill all the visualization goals mentioned above.\n\nFinally, save the paraview state as \"chameleon/results/{agent_mode}/chameleon.pvsm\"",
- "full_result": {
- "case_name": "chameleon",
- "status": "failed",
- "start_time": "2025-09-30T15:38:39.101594",
- "task_description": "Your agent_mode is \"pvpython\", use it when saving results. Your working directory is \"/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench/benchmark/../SciVisAgentBench-tasks/main\", and you should have access to it. In the following prompts, we will use relative path with respect to your working path. But remember, when you load or save any file, always stick to absolute path.\n\nTask:\n\nLoad the chameleon dataset from \"chameleon/data/chameleon_256x256x270_float32.raw\", the information about this dataset:\nchameleon (Scalar)\nData Scalar Type: float\nData Byte Order: little Endian\nData Extent: 256x256x270\nNumber of Scalar Components: 1\nData loading is very important, make sure you correctly load the dataset according to their features.\n\nApply the volume rendering to visualize the chameleon dataset\n\nAdjust the transfer function to highlight the bony structures and skin in an X-ray style.\n\nAdjust the camera position and focus on the head part of the chameleon\n\nPlease think step by step and make sure to fulfill all the visualization goals mentioned above.\n\nFinally, save the paraview state as \"chameleon/results/{agent_mode}/chameleon.pvsm\"",
- "llm_rubric": "1. Overall Visualization Goal: Does the result present a clean, X-ray–style volume rendering where the chameleon's bony structures are clearly emphasized and soft tissue is faint but discernible?\n\n2. Data Loading Correctness: Is the RAW volume loaded with the specified metadata (float32, little-endian, 256×256×270, 1 component) so that the histogram looks reasonable and the anatomy is not flipped or distorted?\n\n3. Transfer Function Quality: Does the grayscale transfer function make low-intensity tissue mostly transparent while assigning higher opacity to bones/skin ridges, yielding good depth cues without over-saturation or banding?\n\n4. Camera & Framing: Is the camera positioned and zoomed to focus on the chameleon's head, keeping it sharply framed (no clipping), with a stable viewpoint that highlights key anatomical details?",
- "evaluation_subtype": "vision",
- "is_vision_evaluation": true,
- "response": "",
- "error": "\r\n404 Not Found\r\n\r\n404 Not Found
\r\n
nginx\r\n\r\n",
- "token_usage": {
- "input_tokens": 261,
- "output_tokens": 0,
- "total_tokens": 261
- },
- "end_time": "2025-09-30T15:38:39.246222",
- "duration_seconds": 0.144628
- }
-}
\ No newline at end of file
diff --git a/main/chameleon_volvis/test_results/pvpython/test_result_1759261263.json b/main/chameleon_volvis/test_results/pvpython/test_result_1759261263.json
deleted file mode 100644
index a1540c0f79fdfad32d2d369fa9a66986333bb0cb..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/test_results/pvpython/test_result_1759261263.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
- "timestamp": "2025-09-30T15:41:03.951159",
- "case_name": "chameleon",
- "status": "failed",
- "duration": 0.144785,
- "token_usage": {
- "input_tokens": 261,
- "output_tokens": 0,
- "total_tokens": 261
- },
- "response": "",
- "task_description": "Your agent_mode is \"pvpython\", use it when saving results. Your working directory is \"/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench/benchmark/../SciVisAgentBench-tasks/main\", and you should have access to it. In the following prompts, we will use relative path with respect to your working path. But remember, when you load or save any file, always stick to absolute path.\n\nTask:\n\nLoad the chameleon dataset from \"chameleon/data/chameleon_256x256x270_float32.raw\", the information about this dataset:\nchameleon (Scalar)\nData Scalar Type: float\nData Byte Order: little Endian\nData Extent: 256x256x270\nNumber of Scalar Components: 1\nData loading is very important, make sure you correctly load the dataset according to their features.\n\nApply the volume rendering to visualize the chameleon dataset\n\nAdjust the transfer function to highlight the bony structures and skin in an X-ray style.\n\nAdjust the camera position and focus on the head part of the chameleon\n\nPlease think step by step and make sure to fulfill all the visualization goals mentioned above.\n\nFinally, save the paraview state as \"chameleon/results/{agent_mode}/chameleon.pvsm\"",
- "full_result": {
- "case_name": "chameleon",
- "status": "failed",
- "start_time": "2025-09-30T15:41:03.806251",
- "task_description": "Your agent_mode is \"pvpython\", use it when saving results. Your working directory is \"/Users/kuangshiai/Documents/ND-VIS/Code/SciVisAgentBench/benchmark/../SciVisAgentBench-tasks/main\", and you should have access to it. In the following prompts, we will use relative path with respect to your working path. But remember, when you load or save any file, always stick to absolute path.\n\nTask:\n\nLoad the chameleon dataset from \"chameleon/data/chameleon_256x256x270_float32.raw\", the information about this dataset:\nchameleon (Scalar)\nData Scalar Type: float\nData Byte Order: little Endian\nData Extent: 256x256x270\nNumber of Scalar Components: 1\nData loading is very important, make sure you correctly load the dataset according to their features.\n\nApply the volume rendering to visualize the chameleon dataset\n\nAdjust the transfer function to highlight the bony structures and skin in an X-ray style.\n\nAdjust the camera position and focus on the head part of the chameleon\n\nPlease think step by step and make sure to fulfill all the visualization goals mentioned above.\n\nFinally, save the paraview state as \"chameleon/results/{agent_mode}/chameleon.pvsm\"",
- "llm_rubric": "1. Overall Visualization Goal: Does the result present a clean, X-ray–style volume rendering where the chameleon's bony structures are clearly emphasized and soft tissue is faint but discernible?\n\n2. Data Loading Correctness: Is the RAW volume loaded with the specified metadata (float32, little-endian, 256×256×270, 1 component) so that the histogram looks reasonable and the anatomy is not flipped or distorted?\n\n3. Transfer Function Quality: Does the grayscale transfer function make low-intensity tissue mostly transparent while assigning higher opacity to bones/skin ridges, yielding good depth cues without over-saturation or banding?\n\n4. Camera & Framing: Is the camera positioned and zoomed to focus on the chameleon's head, keeping it sharply framed (no clipping), with a stable viewpoint that highlights key anatomical details?",
- "evaluation_subtype": "vision",
- "is_vision_evaluation": true,
- "response": "",
- "error": "\r\n404 Not Found\r\n\r\n404 Not Found
\r\n
nginx\r\n\r\n",
- "token_usage": {
- "input_tokens": 261,
- "output_tokens": 0,
- "total_tokens": 261
- },
- "end_time": "2025-09-30T15:41:03.951036",
- "duration_seconds": 0.144785
- }
-}
\ No newline at end of file
diff --git a/main/chameleon_volvis/visualization_goals.txt b/main/chameleon_volvis/visualization_goals.txt
deleted file mode 100644
index ec1cd3d75c3509b47c5042251e18d6a304e9c789..0000000000000000000000000000000000000000
--- a/main/chameleon_volvis/visualization_goals.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-1. Overall Visualization Goal: Does the result match the ground truth X-ray-style volume rendering of the chameleon?
-
-2. Bone Structure Visibility: Are the chameleon's bony structures clearly visible and similar to the ground truth, with the skull, jaw, and spine clearly distinguishable?
-
-3. Soft Tissue Rendering: Is the soft tissue faintly visible and transparent, similar to the ground truth appearance?
-
-4. Depth and Contrast: Does the visualization show similar depth cues and contrast between bones and soft tissue as the ground truth?
diff --git a/main/crayfish/GS/crayfish_streamline.png b/main/crayfish_streamline/GS/crayfish_streamline_gs.png
similarity index 100%
rename from main/crayfish/GS/crayfish_streamline.png
rename to main/crayfish_streamline/GS/crayfish_streamline_gs.png
diff --git a/main/crayfish/GS/crayfish_streamline.pvsm b/main/crayfish_streamline/GS/crayfish_streamline_gs.pvsm
similarity index 100%
rename from main/crayfish/GS/crayfish_streamline.pvsm
rename to main/crayfish_streamline/GS/crayfish_streamline_gs.pvsm
diff --git a/main/crayfish/GS/crayfish_streamline.py b/main/crayfish_streamline/GS/crayfish_streamline_gs.py
similarity index 100%
rename from main/crayfish/GS/crayfish_streamline.py
rename to main/crayfish_streamline/GS/crayfish_streamline_gs.py
diff --git a/main/crayfish/data/crayfish.txt b/main/crayfish_streamline/data/crayfish_streamline.txt
similarity index 100%
rename from main/crayfish/data/crayfish.txt
rename to main/crayfish_streamline/data/crayfish_streamline.txt
diff --git a/main/crayfish/data/crayfish.raw b/main/crayfish_streamline/data/crayfish_streamline_322x162x119_float32_scalar3.raw
similarity index 100%
rename from main/crayfish/data/crayfish.raw
rename to main/crayfish_streamline/data/crayfish_streamline_322x162x119_float32_scalar3.raw
diff --git a/main/crayfish/task_description.txt b/main/crayfish_streamline/task_description.txt
similarity index 71%
rename from main/crayfish/task_description.txt
rename to main/crayfish_streamline/task_description.txt
index 4287eb387be9449fd7e58d196a9e02aab3b7e844..1107594e6e614fb2230f0123bc4f1bbd63590a3d 100644
--- a/main/crayfish/task_description.txt
+++ b/main/crayfish_streamline/task_description.txt
@@ -1,4 +1,4 @@
-Load the Crayfish flow vector field from "crayfish/data/crayfish.raw", the information about this dataset:
+Load the Crayfish flow vector field from "crayfish_streamline/data/crayfish_streamline_322x162x119_float32_scalar3.raw", the information about this dataset:
Crayfish Flow (Vector)
Data Scalar Type: float
Data Byte Order: Little Endian
@@ -17,9 +17,9 @@ Show the dataset bounding box as an outline (black).
In the pipeline browser panel, hide all stream tracers and only show the tube filters and the outline.
-Use a gray-blue background (RGB: 0.329, 0.349, 0.427). Render at 1280x1280.
+Use a white background. Render at 1280x1280.
-Save the paraview state as "crayfish/results/{agent_mode}/crayfish.pvsm".
-Save the visualization image as "crayfish/results/{agent_mode}/crayfish.png".
-(Optional, if use python script) Save the python script as "crayfish/results/{agent_mode}/crayfish.py".
+Save the paraview state as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.pvsm".
+Save the visualization image as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.png".
+(Optional, if use python script) Save the python script as "crayfish_streamline/results/{agent_mode}/crayfish_streamline.py".
Do not save any other files, and always save the visualization image.
diff --git a/main/crayfish/visualization_goals.txt b/main/crayfish_streamline/visualization_goals.txt
similarity index 100%
rename from main/crayfish/visualization_goals.txt
rename to main/crayfish_streamline/visualization_goals.txt
diff --git a/main/engine/GS/engine.pvsm b/main/engine/GS/engine.pvsm
deleted file mode 100644
index 4deb835f39fdddfb68339183e87314ac41219114..0000000000000000000000000000000000000000
--- a/main/engine/GS/engine.pvsm
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:57d77bac59a328eb14ed6e2eed84a20ad93582462278d4e30301b8a342dc7045
-size 259002
diff --git a/main/engine/GS/engine_gs.png b/main/engine/GS/engine_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..975f8603200b939cad385450647057d4a3821c5a
--- /dev/null
+++ b/main/engine/GS/engine_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f3e773941a421a7f0bc4b35d77c4a8cecf2b36688b3dd49f31aeeee8a0052d0
+size 693999
diff --git a/main/engine/GS/engine_gs.pvsm b/main/engine/GS/engine_gs.pvsm
new file mode 100644
index 0000000000000000000000000000000000000000..74760822f50fcdb5827d750a4256e416e6437cf8
--- /dev/null
+++ b/main/engine/GS/engine_gs.pvsm
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:664b38f1f680e03c0e66dd2471ad42227bb7f704b274cce38d1236e7bdedf2c9
+size 257739
diff --git a/main/engine/task_description.txt b/main/engine/task_description.txt
index fc51b744b432c0b5b4011bcb3b2b9414e35178ad..0a7a0305a7540736f17fa60daecf752cb3867105 100644
--- a/main/engine/task_description.txt
+++ b/main/engine/task_description.txt
@@ -15,5 +15,11 @@ Instructions:
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. Save your work:
-Save the ParaView state as "engine/results/{agent_mode}/engine.pvsm".
+4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+5. 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.
diff --git a/main/foot/GS/foot_gs.png b/main/foot/GS/foot_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..bdc71555ab0d9707489a5a8c073199a4840ec47c
--- /dev/null
+++ b/main/foot/GS/foot_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05459aceee01938f0566321c1af3a85278742ce6263417555c5d3b491b0abb19
+size 349308
diff --git a/main/foot/task_description.txt b/main/foot/task_description.txt
index 087ea0cca2cccdee1b7957df8934d920695c525c..6e4ef8acb54d699494d17c5fd10887491c069710 100644
--- a/main/foot/task_description.txt
+++ b/main/foot/task_description.txt
@@ -20,6 +20,12 @@ B. The phalanges are fully visible, but the metatarsals are only partially visib
C. The metatarsals are fully visible, but the phalanges are only partially visible
D. Neither the phalanges nor the metatarsals are clearly visible
-3. Save your work:
-Save the ParaView state as "foot/results/{agent_mode}/foot.pvsm".
-Save the answers to the analysis questions in plain text as "foot/results/{agent_mode}/answers.txt".
\ No newline at end of file
+3. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+4. Save your work:
+Save the visualization image as "foot/results/{agent_mode}/foot.png".
+Save the answers to the analysis questions in plain text as "foot/results/{agent_mode}/answers.txt".
+(Optional, but must save if use paraview) Save the paraview state as "foot/results/{agent_mode}/foot.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "foot/results/{agent_mode}/foot.py".
+(Optional, but must save if use VTK) Save the cxx code script as "foot/results/{agent_mode}/foot.cxx"
+Do not save any other files, and always save the visualization image and the text file.
\ No newline at end of file
diff --git a/main/lobster/GS/lobster_gs.png b/main/lobster/GS/lobster_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..106cb6d1ec4b655ee52112f197290b9ecdf66fc2
--- /dev/null
+++ b/main/lobster/GS/lobster_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06c39ff7b6abdc7294152ad417e8029a4eddf3a7c6a44407533d086d510055b5
+size 380485
diff --git a/main/lobster/task_description.txt b/main/lobster/task_description.txt
index 8433934c0fa72e69160124f1de599fb908c2f4eb..1f126b7e397a809aebbf7bb06b58fe4b326bde2a 100644
--- a/main/lobster/task_description.txt
+++ b/main/lobster/task_description.txt
@@ -22,6 +22,12 @@ B. 7 walking legs
C. 8 walking legs
D. 10 walking legs
-4. Save your work:
-Save the ParaView state as "lobster/results/{agent_mode}/lobster.pvsm".
-Save the answers to the analysis questions in plain text as "lobster/results/{agent_mode}/answers.txt".
\ No newline at end of file
+4. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+5. Save your work:
+Save the visualization image as "lobster/results/{agent_mode}/lobster.png".
+Save the answers to the analysis questions in plain text as "lobster/results/{agent_mode}/answers.txt".
+(Optional, but must save if use paraview) Save the paraview state as "lobster/results/{agent_mode}/lobster.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "lobster/results/{agent_mode}/lobster.py".
+(Optional, but must save if use VTK) Save the cxx code script as "lobster/results/{agent_mode}/lobster.cxx"
+Do not save any other files, and always save the visualization image and the text file.
\ No newline at end of file
diff --git a/main/mhd-magfield_streamribbon/task_description.txt b/main/mhd-magfield_streamribbon/task_description.txt
index 2242438b5a1aaf684137b3d383762a9b284a689a..7d34a584c9dd8ec522096a1a7464ceaa6d64d904 100644
--- a/main/mhd-magfield_streamribbon/task_description.txt
+++ b/main/mhd-magfield_streamribbon/task_description.txt
@@ -1,10 +1,11 @@
Load the MHD magnetic field dataset from "mhd-magfield_streamribbon/data/mhd-magfield_streamribbon.vti" (VTI format, 128x128x128 grid with components bx, by, bz).
Generate a stream ribbon seeded from a line source along the y-axis at x=64, z=64 (from y=20 to y=108), with 30 seed points.
-The stream ribbon should be traced in both forward and backward directions along the magnetic field lines.
+The stream ribbon should be traced along the magnetic field lines.
Color the stream ribbon by magnetic field magnitude using the 'Cool to Warm' colormap. Enable surface lighting with specular reflection for better 3D perception.
Add a color bar labeled 'Magnetic Field Magnitude'.
-Use a dark navy background (RGB: 0.0, 0.0, 0.12). Set an isometric camera view. Render at 1024x1024 resolution.
-Save the paraview state as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.pvsm".
+Use a white background. Set an isometric camera view. Render at 1024x1024 resolution.
Save the visualization image as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.png".
-(Optional, if use python script) Save the python script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.py".
-Do not save any other files, and always save the visualization image.
\ No newline at end of file
+(Optional, but must save if use paraview) Save the paraview state as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.py".
+(Optional, but must save if use VTK) Save the cxx code script as "mhd-magfield_streamribbon/results/{agent_mode}/mhd-magfield_streamribbon.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.png b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..ce2d3f61ff1c2e8afa34299178b0a056dc400953
--- /dev/null
+++ b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6418a264912dbf6d67f1066a785cebd05fb0bbd1d337418cf6c3b5da896e9ac
+size 102204
diff --git a/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.pvsm b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.pvsm
new file mode 100644
index 0000000000000000000000000000000000000000..cf6281cb910d477798eb3f2134d4f3db2825d596
--- /dev/null
+++ b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.pvsm
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e3498a503dc92f814697be8899a30f5b533b7ddf588b704f3541395c8f3900b
+size 285084
diff --git a/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.py b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..03b7f976b7cd8f8b8552bcc64bfc06df550cf95d
--- /dev/null
+++ b/main/mhd-turbulence_pathline/GS/mhd-turbulence_pathline_gs.py
@@ -0,0 +1,79 @@
+import os
+import glob
+from paraview.simple import *
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+VTI_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, '..', 'data', 'mhd-turbulence_pathline_*.vti')))
+OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathline_gs.png')
+OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathline_gs.pvsm')
+
+# Load all timesteps as temporal data
+reader = XMLImageDataReader(FileName=VTI_FILES)
+reader.UpdatePipeline()
+
+# Scale time for longer particle travel
+temporalScale = TemporalShiftScale(Input=reader)
+temporalScale.Scale = 20.0
+temporalScale.UpdatePipeline()
+
+# Interpolate for smoother pathlines
+temporalInterp = TemporalInterpolator(Input=temporalScale)
+temporalInterp.DiscreteTimeStepInterval = 0.5
+temporalInterp.UpdatePipeline()
+
+# Seed line along z-axis
+seedLine = Line()
+seedLine.Point1 = [64.0, 64.0, 20.0]
+seedLine.Point2 = [64.0, 64.0, 108.0]
+seedLine.Resolution = 25
+seedLine.UpdatePipeline()
+
+# ParticlePath filter - true pathlines
+particlePath = ParticlePath(Input=temporalInterp, SeedSource=seedLine)
+particlePath.SelectInputVectors = ['POINTS', 'vector']
+particlePath.StaticSeeds = True
+particlePath.TerminationTime = 80.0
+particlePath.ForceReinjectionEveryNSteps = 0
+particlePath.ComputeVorticity = False
+
+# Advance animation through all timesteps
+animationScene = GetAnimationScene()
+animationScene.UpdateAnimationUsingDataTimeSteps()
+animationScene.PlayMode = 'Snap To TimeSteps'
+for t in list(temporalInterp.TimestepValues):
+ animationScene.AnimationTime = t
+ particlePath.UpdatePipeline(t)
+
+# Add tubes for visibility
+tube = Tube(Input=particlePath)
+tube.Radius = 0.3
+tube.UpdatePipeline()
+
+# Render setup
+renderView = GetActiveViewOrCreate('RenderView')
+renderView.ViewSize = [1024, 1024]
+renderView.Background = [0.05, 0.05, 0.1]
+
+tubeDisplay = Show(tube, renderView)
+tubeDisplay.Representation = 'Surface'
+ColorBy(tubeDisplay, ('POINTS', 'magnitude'))
+
+magLUT = GetColorTransferFunction('magnitude')
+magLUT.ApplyPreset('Viridis (matplotlib)', True)
+
+tubeDisplay.SetScalarBarVisibility(renderView, True)
+colorBar = GetScalarBar(magLUT, renderView)
+colorBar.Title = ''
+colorBar.ComponentTitle = ''
+colorBar.TitleColor = [0.0, 0.0, 0.0]
+colorBar.LabelColor = [0.0, 0.0, 0.0]
+
+renderView.CameraPosition = [200.0, 200.0, 200.0]
+renderView.CameraFocalPoint = [63.5, 63.5, 63.5]
+renderView.CameraViewUp = [0.0, 0.0, 1.0]
+renderView.ResetCamera()
+Render()
+
+SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
+SaveState(OUTPUT_STATE)
+print(f"Done: {OUTPUT_IMG}")
diff --git a/main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0000.vti b/main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0000.vti
similarity index 100%
rename from main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0000.vti
rename to main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0000.vti
diff --git a/main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0010.vti b/main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0010.vti
similarity index 100%
rename from main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0010.vti
rename to main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0010.vti
diff --git a/main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0020.vti b/main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0020.vti
similarity index 100%
rename from main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0020.vti
rename to main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0020.vti
diff --git a/main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0030.vti b/main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0030.vti
similarity index 100%
rename from main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0030.vti
rename to main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0030.vti
diff --git a/main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0040.vti b/main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0040.vti
similarity index 100%
rename from main/mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_0040.vti
rename to main/mhd-turbulence_pathline/data/mhd-turbulence_pathline_0040.vti
diff --git a/main/mhd-turbulence_pathline/task_description.txt b/main/mhd-turbulence_pathline/task_description.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7b8aec23c248bc5fdf81c90d319df10eb364db5f
--- /dev/null
+++ b/main/mhd-turbulence_pathline/task_description.txt
@@ -0,0 +1,10 @@
+Load the MHD turbulence velocity field time series "mhd-turbulence_pathline/data/mhd-turbulence_pathline_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
+Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.5) to extend particle travel and smooth trajectories.
+Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80.
+Render pathlines as tubes with radius 0.3. Color by velocity magnitude using the 'Viridis (matplotlib)' colormap.
+Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view. Render at 1024x1024.
+Save the visualization image as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.png".
+(Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.py".
+(Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathline/results/{agent_mode}/mhd-turbulence_pathline.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/mhd-turbulence_pathline/visualization_goals.txt b/main/mhd-turbulence_pathline/visualization_goals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa9ddf2c96e2fba261ff9c6eb91315c28714f411
--- /dev/null
+++ b/main/mhd-turbulence_pathline/visualization_goals.txt
@@ -0,0 +1,7 @@
+1. Overall Visualization Goal: Does the result match the ground truth pathline visualization of the MHD turbulence velocity field?
+
+2. Pathline Patterns: Do the pathlines show similar particle trajectories and flow structures as the ground truth?
+
+3. Pathline Coverage: Is the spatial extent and distribution of pathlines similar to the ground truth?
+
+4. Color Mapping: Is the color distribution along pathlines visually similar to the ground truth?
\ No newline at end of file
diff --git a/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.png b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..36b528274349ce31091d35d549b7f139787d030e
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd0f05c0cc6ca4d7f8cd319759a6fd56975fba3622f4feecaae7a9957dc5812e
+size 122139
diff --git a/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.pvsm b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.pvsm
new file mode 100644
index 0000000000000000000000000000000000000000..5d449ee5901fff6727809246de81e4f3f3b5d18d
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.pvsm
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f700aabc0626066cae0d4ef1874b67cdb59f679cfc307eb5544809ad1f6c6072
+size 268601
diff --git a/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.py b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef2e908ea91c1bdf72da22e81de74094ae0fb856
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/GS/mhd-turbulence_pathribbon_gs.py
@@ -0,0 +1,100 @@
+import os
+import glob
+from paraview.simple import *
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+VTI_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, '..', 'data', 'mhd-turbulence_pathribbon_*.vti')))
+OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathribbon_gs.png')
+OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathribbon_gs.pvsm')
+
+# Load all timesteps as temporal data
+reader = XMLImageDataReader(FileName=VTI_FILES)
+reader.UpdatePipeline()
+
+# Scale time for longer particle travel
+temporalScale = TemporalShiftScale(Input=reader)
+temporalScale.Scale = 20.0
+temporalScale.UpdatePipeline()
+
+# Dense interpolation for smooth pathlines
+temporalInterp = TemporalInterpolator(Input=temporalScale)
+temporalInterp.DiscreteTimeStepInterval = 0.1
+temporalInterp.UpdatePipeline()
+
+# Seed line along z-axis
+seedLine = Line()
+seedLine.Point1 = [64.0, 64.0, 20.0]
+seedLine.Point2 = [64.0, 64.0, 108.0]
+seedLine.Resolution = 25
+seedLine.UpdatePipeline()
+
+# ParticlePath filter - true pathlines
+particlePath = ParticlePath(Input=temporalInterp, SeedSource=seedLine)
+particlePath.SelectInputVectors = ['POINTS', 'vector']
+particlePath.StaticSeeds = True
+particlePath.TerminationTime = 80.0
+particlePath.ForceReinjectionEveryNSteps = 0
+particlePath.ComputeVorticity = False
+
+# Advance animation through all timesteps
+animationScene = GetAnimationScene()
+animationScene.UpdateAnimationUsingDataTimeSteps()
+animationScene.PlayMode = 'Snap To TimeSteps'
+for t in list(temporalInterp.TimestepValues):
+ animationScene.AnimationTime = t
+ particlePath.UpdatePipeline(t)
+
+di = particlePath.GetDataInformation()
+print(f"PathLines: {di.GetNumberOfPoints()} pts, {di.GetNumberOfCells()} cells")
+
+# Create ribbon with UseDefaultNormal to prevent twisting
+ribbon = Ribbon(Input=particlePath)
+ribbon.Scalars = ['POINTS', 'magnitude']
+ribbon.Width = 1.5
+ribbon.UseDefaultNormal = True
+ribbon.DefaultNormal = [0.0, 1.0, 0.0]
+ribbon.UpdatePipeline()
+
+# Smooth the ribbon mesh
+smooth = Smooth(Input=ribbon)
+smooth.NumberofIterations = 500
+smooth.UpdatePipeline()
+
+# Generate normals for smooth shading
+normals = GenerateSurfaceNormals(Input=smooth)
+normals.UpdatePipeline()
+
+# Render setup
+renderView = GetActiveViewOrCreate('RenderView')
+renderView.ViewSize = [1024, 1024]
+renderView.Background = [0.05, 0.05, 0.1]
+
+display = Show(normals, renderView)
+display.Representation = 'Surface'
+display.Opacity = 0.85
+ColorBy(display, ('POINTS', 'magnitude'))
+
+magLUT = GetColorTransferFunction('magnitude')
+magLUT.ApplyPreset('Cool to Warm', True)
+magLUT.RescaleTransferFunction(0.1, 0.8)
+magPWF = GetOpacityTransferFunction('magnitude')
+magPWF.RescaleTransferFunction(0.1, 0.8)
+
+display.Specular = 0.5
+
+display.SetScalarBarVisibility(renderView, True)
+colorBar = GetScalarBar(magLUT, renderView)
+colorBar.Title = ''
+colorBar.ComponentTitle = ''
+colorBar.TitleColor = [0.0, 0.0, 0.0]
+colorBar.LabelColor = [0.0, 0.0, 0.0]
+
+renderView.CameraPosition = [200.0, 200.0, 200.0]
+renderView.CameraFocalPoint = [63.5, 63.5, 63.5]
+renderView.CameraViewUp = [0.0, 0.0, 1.0]
+renderView.ResetCamera()
+Render()
+
+SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
+SaveState(OUTPUT_STATE)
+print(f"Done: {OUTPUT_IMG}")
diff --git a/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0000.vti b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0000.vti
new file mode 100644
index 0000000000000000000000000000000000000000..c13bdadb6bfe7445224dfce280ca014b53ee4bf3
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0000.vti
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfbef0b2919cbfaaf3d462c4e1802b2d06e9d6cc617a6d2c8edcc586f7e55a67
+size 78294594
diff --git a/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0010.vti b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0010.vti
new file mode 100644
index 0000000000000000000000000000000000000000..b6c44642c2bfaff7f62045f25c9d6d916b44818a
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0010.vti
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:823282fcbb627582c8d6bac7edc7cb8f87801ba65ff92d65a1d06df7444fd54e
+size 78294594
diff --git a/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0020.vti b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0020.vti
new file mode 100644
index 0000000000000000000000000000000000000000..64ccd243d418fd4499281bb0eeb4d203bf0293e9
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0020.vti
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:080e439bab3575dd4e42e2eb6dbcc118e2f86f618448e10df78a6c067d766a00
+size 78294594
diff --git a/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0030.vti b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0030.vti
new file mode 100644
index 0000000000000000000000000000000000000000..dce3ae3e4d7fc9354bdf112e66019e1d062e0717
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0030.vti
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cea4a3655827fe96fbe3bc1677fb0424602ff179742d84326363712b050f0b8d
+size 78294594
diff --git a/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0040.vti b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0040.vti
new file mode 100644
index 0000000000000000000000000000000000000000..a18441d10e6a617953375c9e9375dc2b846d8ddc
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_0040.vti
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a190989db4160876c20a42e79813d9e601559974959fc85a702636ff0cbd0791
+size 78294594
diff --git a/main/mhd-turbulence_pathribbon/task_description.txt b/main/mhd-turbulence_pathribbon/task_description.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d4f73cf64d93ef4f6515d91d55d7bb9e79ce9c90
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/task_description.txt
@@ -0,0 +1,11 @@
+Load the MHD turbulence velocity field time series "mhd-turbulence_pathribbon/data/mhd-turbulence_pathribbon_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
+Compute true pathlines by tracking particles through the time-varying velocity field using the ParticlePath filter. Apply TemporalShiftScale (scale=20) and TemporalInterpolator (interval=0.1) for dense, smooth trajectories.
+Seed 26 points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use static seeds with termination time 80.
+Create ribbon surfaces from the pathlines using the Ribbon filter with width 1.5 and a fixed default normal to prevent twisting. Apply Smooth filter (500 iterations) and generate surface normals for smooth shading.
+Set surface opacity to 0.85. Color by velocity magnitude using the 'Cool to Warm' colormap (range 0.1-0.8). Add specular highlights (0.5).
+Add a color bar for velocity magnitude. Use a white background. Set an isometric camera view. Render at 1024x1024.
+Save the visualization image as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.png".
+(Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.py".
+(Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_pathribbon/results/{agent_mode}/mhd-turbulence_pathribbon.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/mhd-turbulence_pathribbon/visualization_goals.txt b/main/mhd-turbulence_pathribbon/visualization_goals.txt
new file mode 100644
index 0000000000000000000000000000000000000000..af67062cbe413ad0b4f3c431d5af4ede3cc1848b
--- /dev/null
+++ b/main/mhd-turbulence_pathribbon/visualization_goals.txt
@@ -0,0 +1,5 @@
+1. Surface Patterns: Does the path ribbon show similar flow patterns and structures as the ground truth?
+
+2. Surface Coverage: Is the spatial extent and shape of the path ribbon similar to the ground truth?
+
+3. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
diff --git a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.png b/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.png
deleted file mode 100644
index eb6c79d57022b5e2d220f604da76f477be3f5b86..0000000000000000000000000000000000000000
--- a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c938c93ef7e3c5b54475919ab9758d40b13c9161a3d7449a80b71e98b6d98f23
-size 483426
diff --git a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.pvsm b/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.pvsm
deleted file mode 100644
index ceb988b62d1c2cfdce7905a463eca901434611e8..0000000000000000000000000000000000000000
--- a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.pvsm
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b31675cb2a6305d680195feedeb6fdb6c398e615ca42c50ec918b5103f980629
-size 777252
diff --git a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.py b/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.py
deleted file mode 100644
index 76fe966cf8dc5d082150088532c8745771d47015..0000000000000000000000000000000000000000
--- a/main/mhd-turbulence_pathsurface/GS/mhd-turbulence_pathsurface_gs.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-import glob
-from paraview.simple import *
-
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-VTI_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, '..', 'data', 'mhd-turbulence_pathsurface_*.vti')))
-OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathsurface_gs.png')
-OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'mhd-turbulence_pathsurface_gs.pvsm')
-
-# Path surface: show stream surfaces from multiple timesteps with transparency
-
-renderView = GetActiveViewOrCreate('RenderView')
-renderView.ViewSize = [1024, 1024]
-renderView.Background = [0.08, 0.08, 0.1]
-
-# Opacity progression for time (earlier = more transparent)
-opacities = [0.3, 0.4, 0.5, 0.7, 0.9]
-
-for idx, vti_file in enumerate(VTI_FILES):
- reader = XMLImageDataReader(FileName=[vti_file])
- reader.UpdatePipeline()
-
- stream = StreamTracer(Input=reader, SeedType='Line')
- stream.SeedType.Point1 = [64.0, 64.0, 40.0]
- stream.SeedType.Point2 = [64.0, 64.0, 88.0]
- stream.SeedType.Resolution = 20
- stream.Vectors = ['POINTS', 'vector']
- stream.IntegrationDirection = 'BOTH'
- stream.MaximumStreamlineLength = 150.0
- stream.UpdatePipeline()
-
- ribbon = Ribbon(Input=stream)
- ribbon.Scalars = ['POINTS', 'magnitude']
- ribbon.Width = 0.8
- ribbon.UpdatePipeline()
-
- ribbonDisplay = Show(ribbon, renderView)
- ribbonDisplay.Representation = 'Surface'
- ribbonDisplay.Opacity = opacities[idx]
- ColorBy(ribbonDisplay, ('POINTS', 'magnitude'))
-
-magLUT = GetColorTransferFunction('magnitude')
-magLUT.ApplyPreset('Viridis (matplotlib)', True)
-
-# Show color bar on the last one
-ribbonDisplay.SetScalarBarVisibility(renderView, True)
-colorBar = GetScalarBar(magLUT, renderView)
-colorBar.Title = ''
-colorBar.ComponentTitle = ''
-colorBar.TitleColor = [0.0, 0.0, 0.0]
-colorBar.LabelColor = [0.0, 0.0, 0.0]
-
-renderView.CameraPosition = [180.0, 180.0, 180.0]
-renderView.CameraFocalPoint = [63.5, 63.5, 63.5]
-renderView.CameraViewUp = [0.0, 0.0, 1.0]
-renderView.ResetCamera()
-Render()
-
-SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
-SaveState(OUTPUT_STATE)
-print(f"Task 22 done: {OUTPUT_IMG}")
diff --git a/main/mhd-turbulence_pathsurface/task_description.txt b/main/mhd-turbulence_pathsurface/task_description.txt
deleted file mode 100644
index 4e53a09c109935ef99a062e2146c2c2c5ab2c326..0000000000000000000000000000000000000000
--- a/main/mhd-turbulence_pathsurface/task_description.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-Load the MHD turbulence velocity field time series "mhd-turbulence_pathsurface/data/mhd-turbulence_pathsurface_{timestep}.vti", where "timestep" in {0000, 0010, 0020, 0030, 0040} (5 timesteps, VTI format, 128x128x128 grid each).
-Visualize the temporal evolution by generating stream surfaces (ribbons) from each timestep with varying opacity.
-Use a line seed along the z-axis at x=64, y=64 (from z=40 to z=88) with 20 seed points.
-Trace streamlines bidirectionally with maximum length 150. Create ribbon surfaces from the streamlines with width 0.8.
-Apply progressive opacity (0.3 for t=0, increasing to 0.9 for t=40) to show temporal layering. Color all surfaces by velocity magnitude using the 'Viridis (matplotlib)' colormap.
-Add a color bar labeled 'Velocity Magnitude'. Use a dark gray background (RGB: 0.08, 0.08, 0.1). Set an isometric camera view. Render at 1024x1024.
-Save the paraview state as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.pvsm".
-Save the visualization image as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.png".
-(Optional, if use python script) Save the python script as "mhd-turbulence_pathsurface/results/{agent_mode}/mhd-turbulence_pathsurface.py".
-Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/mhd-turbulence_pathsurface/visualization_goals.txt b/main/mhd-turbulence_pathsurface/visualization_goals.txt
deleted file mode 100644
index b13cb32a28dae5429459e5035b6541e40e6bf9f0..0000000000000000000000000000000000000000
--- a/main/mhd-turbulence_pathsurface/visualization_goals.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-1. Overall Visualization Goal: Does the result match the ground truth path surface visualization of the MHD turbulence velocity field?
-
-2. Surface Patterns: Does the path surface show similar flow patterns and structures as the ground truth?
-
-3. Surface Coverage: Is the spatial extent and shape of the path surface similar to the ground truth?
-
-4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
\ No newline at end of file
diff --git a/main/mhd-turbulence_streamline/task_description.txt b/main/mhd-turbulence_streamline/task_description.txt
index 9a30e34ab22444e61e6e1dba8cd04b582df572c3..10e9ace3f2ac2379d6e635188559ee2248db8f71 100644
--- a/main/mhd-turbulence_streamline/task_description.txt
+++ b/main/mhd-turbulence_streamline/task_description.txt
@@ -1,8 +1,9 @@
Load the MHD turbulence velocity field dataset "mhd-turbulence_streamline/data/mhd-turbulence_streamline.vti" (VTI format, 128x128x128 grid).
Generate 3D streamlines seeded from a line source along the z-axis at x=64, y=64 (from z=0 to z=127), with 50 seed points.
Color the streamlines by velocity magnitude using the 'Turbo' colormap. Set streamline tube radius to 0.3 using the Tube filter.
-Add a color bar labeled 'Velocity Magnitude'. Use a dark background (RGB: 0.05, 0.05, 0.1). Set an isometric camera view. Render at 1024x1024.
-Save the paraview state as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.pvsm".
+Add a color bar labeled 'Velocity Magnitude'. Use a white background. Set an isometric camera view. Render at 1024x1024.
Save the visualization image as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.png".
-(Optional, if use python script) Save the python script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.py".
-Do not save any other files, and always save the visualization image.
\ No newline at end of file
+(Optional, but must save if use paraview) Save the paraview state as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.py".
+(Optional, but must save if use VTK) Save the cxx code script as "mhd-turbulence_streamline/results/{agent_mode}/mhd-turbulence_streamline.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/miranda/task_description.txt b/main/miranda/task_description.txt
index 66bf01f7d7d052bbb26c0a829aa54482b780a380..3c60b87ba92a00e58427f2b07d76d67c47840cf0 100644
--- a/main/miranda/task_description.txt
+++ b/main/miranda/task_description.txt
@@ -9,6 +9,8 @@ Generate a visualization image of the Rayleigh-Taylor Instability dataset, a tim
6) White background
7) Visualization image resolution is 1024x1024
8) Don't show color/scalar bar or coordinate axes.
-9) Save the visualization image as a png file "miranda/results/{agent_mode}/miranda.png"
-10) (Option 1) Save the paraview state as "miranda/results/{agent_mode}/miranda.pvsm" if you are using ParaView as the visualization tool
-11) (Option 2) Save the cxx code script as "miranda/results/{agent_mode}/miranda.cxx" if you are using VTK as the visualization tool
\ No newline at end of file
+Save the visualization image as "miranda/results/{agent_mode}/miranda.png".
+(Optional, but must save if use paraview) Save the paraview state as "miranda/results/{agent_mode}/miranda.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "miranda/results/{agent_mode}/miranda.py".
+(Optional, but must save if use VTK) Save the cxx code script as "miranda/results/{agent_mode}/miranda.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/richtmyer/task_description.txt b/main/richtmyer/task_description.txt
index 4be17324bc6c9c309a25d7c81a3f94c4e82f9a7c..2d3b2a39626fb4e09155687bf3421d9ae99923f0 100644
--- a/main/richtmyer/task_description.txt
+++ b/main/richtmyer/task_description.txt
@@ -9,6 +9,8 @@ Generate a visualization image of the Richtmyer dataset, Entropy field (timestep
6) Turn on the shade and set the ambient, diffuse and specular as 1.0
7) White background. Volume rendering ray casting sample distance is 0.1
8) Don't show color/scalar bar or coordinate axes.
-9) Save the visualization image as a png file "richtmyer/results/{agent_mode}/richtmyer.png"
-10) (Option 1) Save the paraview state as "richtmyer/results/{agent_mode}/richtmyer.pvsm" if you are using ParaView as the visualization tool
-11) (Option 2) Save the cxx code script as "richtmyer/results/{agent_mode}/richtmyer.cxx" if you are using VTK as the visualization tool
\ No newline at end of file
+Save the visualization image as "richtmyer/results/{agent_mode}/richtmyer.png".
+(Optional, but must save if use paraview) Save the paraview state as "richtmyer/results/{agent_mode}/richtmyer.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "richtmyer/results/{agent_mode}/richtmyer.py".
+(Optional, but must save if use VTK) Save the cxx code script as "richtmyer/results/{agent_mode}/richtmyer.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/rotstrat/task_description.txt b/main/rotstrat/task_description.txt
index 1fea57af104dcbf8dc2af1969c892e802af44b1c..e4268c5deadafd896012cb38b13f0225ed40be5b 100644
--- a/main/rotstrat/task_description.txt
+++ b/main/rotstrat/task_description.txt
@@ -9,6 +9,8 @@ Generate a visualization image of the Rotstrat dataset, temperature field of a d
6) White background
7) Visualization image resolution is 1024x1024
8) Don't show color/scalar bar or coordinate axes.
-9) Save the visualization image as a png file "rotstrat/results/{agent_mode}/rotstrat.png"
-10) (Option 1) Save the paraview state as "rotstrat/results/{agent_mode}/rotstrat.pvsm" if you are using ParaView as the visualization tool
-11) (Option 2) Save the cxx code script as "rotstrat/results/{agent_mode}/rotstrat.cxx" if you are using VTK as the visualization tool
\ No newline at end of file
+Save the visualization image as "rotstrat/results/{agent_mode}/rotstrat.png".
+(Optional, but must save if use paraview) Save the paraview state as "rotstrat/results/{agent_mode}/rotstrat.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "rotstrat/results/{agent_mode}/rotstrat.py".
+(Optional, but must save if use VTK) Save the cxx code script as "rotstrat/results/{agent_mode}/rotstrat.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/rti-velocity_glyph/task_description.txt b/main/rti-velocity_glyph/task_description.txt
index 6ba73cb85cee048ebb64d7f1dda3bb9d561400d5..7822fa4d67c2aea42166a6f45ce8ea8834598028 100644
--- a/main/rti-velocity_glyph/task_description.txt
+++ b/main/rti-velocity_glyph/task_description.txt
@@ -3,9 +3,9 @@ Create a slice at y=64 through the volume.
Place arrow glyphs on the slice, oriented by the velocity vector. Use uniform arrow size (no magnitude scaling, scale factor 3.0).
Color the arrows by velocity magnitude using the 'Viridis (matplotlib)' colormap. Use a sampling stride of 3.
Add a color bar labeled 'Velocity Magnitude'.
-Use a black background.
-Set the camera to view along the negative y-axis. Render at 1024x1024.
-Save the paraview state as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.pvsm".
+Use a white background. Set the camera to view along the negative y-axis. Render at 1024x1024.
Save the visualization image as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.png".
-(Optional, if use python script) Save the python script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.py".
+(Optional, but must save if use paraview) Save the paraview state as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.py".
+(Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_glyph/results/{agent_mode}/rti-velocity_glyph.cxx"
Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/rti-velocity_slices/task_description.txt b/main/rti-velocity_slices/task_description.txt
index 85e14b06e3b3132a2c10dd0521d85cd93593352d..2ade8d5214694c081566626d8a1da484e37b5e81 100644
--- a/main/rti-velocity_slices/task_description.txt
+++ b/main/rti-velocity_slices/task_description.txt
@@ -2,8 +2,9 @@ Load the Rayleigh-Taylor instability velocity field from "rti-velocity_slices/da
Create three orthogonal slices: at x=64 (YZ-plane), y=64 (XZ-plane), and z=64 (XY-plane).
Color all three slices by velocity magnitude using the 'Turbo' colormap.
Add a color bar labeled 'Velocity Magnitude'.
-Dark background (RGB: 0.05, 0.05, 0.05). Set an isometric camera view that shows all three slices. Render at 1024x1024.
-Save the paraview state as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.pvsm".
+Use a white background. Set an isometric camera view that shows all three slices. Render at 1024x1024.
Save the visualization image as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.png".
-(Optional, if use python script) Save the python script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.py".
-Do not save any other files, and always save the visualization image
\ No newline at end of file
+(Optional, but must save if use paraview) Save the paraview state as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.py".
+(Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_slices/results/{agent_mode}/rti-velocity_slices.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.png b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.png
index 98186228b1ab30fadc95687fcc3e5977a2534885..2ba969cfb4a364f9a96a1c4dc69bd871869eb571 100644
--- a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.png
+++ b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.png
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:ad4a1089c3e2916e8bb2d7af59ed560f2a6042bef64e13dfad2b286fbd5bd506
-size 133537
+oid sha256:546ae51afa2c2cc9617aac3547dcad9b005a1c34b4939ede1017b72494e45af6
+size 128470
diff --git a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.pvsm b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.pvsm
index 9c1f547d7d07faa3907fac3a577f576cb6f5d9aa..235bec6dc8d363901aa31bf33ce3525fff765a0f 100644
--- a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.pvsm
+++ b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.pvsm
@@ -1,3 +1,3 @@
version https://git-lfs.github.com/spec/v1
-oid sha256:a91b48041e5913c3e608996dcc5fca9822a03c519c2ad511ff2045f70a6daab1
-size 849385
+oid sha256:c6fdfa4640a8441208c64166f208c89d8a20c2e63b31a7be469eccc7c7eb7472
+size 265464
diff --git a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.py b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.py
index 809f5b1a032a4d9ca939c88bd0c635b3d239e828..527af9d3ede9ea4828ed2f230c1ced3708331072 100644
--- a/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.py
+++ b/main/rti-velocity_streakline/GS/rti-velocity_streakline_gs.py
@@ -3,98 +3,99 @@ import glob
from paraview.simple import *
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-VTI_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, '..', 'data', 'rti-velocity_streakline_*.vti')))
+NC_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, '..', 'data', 'RTI_velocity_*.nc')))
OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'rti-velocity_streakline_gs.png')
OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'rti-velocity_streakline_gs.pvsm')
-# Streaklines: show streamlines from injection points at multiple timesteps
-
+# Load all 11 NC timesteps as temporal data
+reader = NetCDFReader(FileName=NC_FILES)
+reader.UpdatePipeline()
+
+# Merge vx, vy, vz into a vector field
+mergeVec = MergeVectorComponents(Input=reader)
+mergeVec.XArray = 'vx'
+mergeVec.YArray = 'vy'
+mergeVec.ZArray = 'vz'
+mergeVec.OutputVectorName = 'velocity'
+mergeVec.UpdatePipeline()
+
+# Calculate magnitude
+calc = Calculator(Input=mergeVec)
+calc.ResultArrayName = 'magnitude'
+calc.Function = 'mag(velocity)'
+calc.UpdatePipeline()
+
+# Scale time so particles travel farther (11 timesteps: 0-10 -> 0-200)
+temporalScale = TemporalShiftScale(Input=calc)
+temporalScale.PreShift = 0
+temporalScale.Scale = 20.0
+temporalScale.PostShift = 0
+temporalScale.UpdatePipeline()
+
+# Interpolate for finer sub-timesteps -> smoother streaks
+temporalInterp = TemporalInterpolator(Input=temporalScale)
+temporalInterp.DiscreteTimeStepInterval = 1.0
+temporalInterp.UpdatePipeline()
+
+# Create seed line along z-axis
+seedLine = Line()
+seedLine.Point1 = [64.0, 64.0, 20.0]
+seedLine.Point2 = [64.0, 64.0, 108.0]
+seedLine.Resolution = 25
+seedLine.UpdatePipeline()
+
+# StreakLine filter
+streakLine = StreakLine(Input=temporalInterp, SeedSource=seedLine)
+streakLine.SelectInputVectors = ['POINTS', 'velocity']
+streakLine.ForceReinjectionEveryNSteps = 10
+streakLine.StaticSeeds = True
+streakLine.TerminationTime = 200.0
+streakLine.ComputeVorticity = False
+
+# Advance animation through all timesteps
+animationScene = GetAnimationScene()
+animationScene.UpdateAnimationUsingDataTimeSteps()
+animationScene.PlayMode = 'Snap To TimeSteps'
+timesteps = list(temporalInterp.TimestepValues)
+print(f"Number of timesteps: {len(timesteps)}")
+for t in timesteps:
+ animationScene.AnimationTime = t
+ streakLine.UpdatePipeline(t)
+
+di = streakLine.GetDataInformation()
+print(f"StreakLine: {di.GetNumberOfPoints()} pts, {di.GetNumberOfCells()} cells")
+
+# Add tubes for visibility
+tube = Tube(Input=streakLine)
+tube.Radius = 0.3
+tube.UpdatePipeline()
+
+# Render setup
renderView = GetActiveViewOrCreate('RenderView')
renderView.ViewSize = [1024, 1024]
-renderView.Background = [0.0, 0.0, 0.0]
-
-# Create 4 injection points at y=64
-injectionPoints = ProgrammableSource()
-injectionPoints.OutputDataSetType = 'vtkPolyData'
-injectionPoints.Script = """
-import vtk
-points = vtk.vtkPoints()
-points.InsertNextPoint(32.0, 64.0, 64.0)
-points.InsertNextPoint(64.0, 64.0, 32.0)
-points.InsertNextPoint(96.0, 64.0, 64.0)
-points.InsertNextPoint(64.0, 64.0, 96.0)
-output.SetPoints(points)
-"""
-injectionPoints.UpdatePipeline()
-
-# Colors for timesteps (t=30 to t=70)
-colors = [
- [0.1, 0.1, 0.9], # t=30 dark blue
- [0.2, 0.5, 0.8], # t=40
- [0.4, 0.7, 0.4], # t=50 green
- [0.8, 0.6, 0.2], # t=60
- [0.9, 0.2, 0.1], # t=70 red
-]
-
-for idx, vti_file in enumerate(VTI_FILES):
- reader = XMLImageDataReader(FileName=[vti_file])
- reader.UpdatePipeline()
-
- stream = StreamTracerWithCustomSource(Input=reader, SeedSource=injectionPoints)
- stream.Vectors = ['POINTS', 'vector']
- stream.IntegrationDirection = 'BOTH'
- stream.MaximumStreamlineLength = 100.0
- stream.UpdatePipeline()
-
- tube = Tube(Input=stream)
- tube.Radius = 0.4
- tube.UpdatePipeline()
-
- tubeDisplay = Show(tube, renderView)
- tubeDisplay.Representation = 'Surface'
- tubeDisplay.DiffuseColor = colors[idx]
- tubeDisplay.Opacity = 0.6 + 0.08 * idx # Later times more opaque
-
-# Add one with vy coloring for the color bar
-lastReader = XMLImageDataReader(FileName=[VTI_FILES[-1]])
-lastReader.UpdatePipeline()
-lastStream = StreamTracerWithCustomSource(Input=lastReader, SeedSource=injectionPoints)
-lastStream.Vectors = ['POINTS', 'vector']
-lastStream.IntegrationDirection = 'BOTH'
-lastStream.MaximumStreamlineLength = 100.0
-lastStream.UpdatePipeline()
-lastTube = Tube(Input=lastStream)
-lastTube.Radius = 0.4
-lastTube.UpdatePipeline()
-
-vyDisplay = Show(lastTube, renderView)
-vyDisplay.Representation = 'Surface'
-ColorBy(vyDisplay, ('POINTS', 'vy'))
-
-vyLUT = GetColorTransferFunction('vy')
-vyLUT.ApplyPreset('Cool to Warm', True)
-
-vyDisplay.SetScalarBarVisibility(renderView, True)
-colorBar = GetScalarBar(vyLUT, renderView)
+renderView.Background = [0.05, 0.05, 0.1]
+
+tubeDisplay = Show(tube, renderView)
+tubeDisplay.Representation = 'Surface'
+ColorBy(tubeDisplay, ('POINTS', 'magnitude'))
+
+magLUT = GetColorTransferFunction('magnitude')
+magLUT.ApplyPreset('Cool to Warm (Extended)', True)
+
+tubeDisplay.SetScalarBarVisibility(renderView, True)
+colorBar = GetScalarBar(magLUT, renderView)
colorBar.Title = ''
colorBar.ComponentTitle = ''
colorBar.TitleColor = [0.0, 0.0, 0.0]
colorBar.LabelColor = [0.0, 0.0, 0.0]
-# Elevated camera
-import math
-elevation = 35 * math.pi / 180
-distance = 250
-cx, cy, cz = 63.5, 63.5, 63.5
-camY = cy - distance * math.cos(elevation)
-camZ = cz + distance * math.sin(elevation)
-
-renderView.CameraPosition = [cx, camY, camZ]
-renderView.CameraFocalPoint = [cx, cy, cz]
+# Isometric camera
+renderView.CameraPosition = [200.0, 200.0, 200.0]
+renderView.CameraFocalPoint = [63.5, 63.5, 63.5]
renderView.CameraViewUp = [0.0, 0.0, 1.0]
renderView.ResetCamera()
Render()
SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
SaveState(OUTPUT_STATE)
-print(f"Task 23 done: {OUTPUT_IMG}")
+print(f"Done: {OUTPUT_IMG}")
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0030.nc b/main/rti-velocity_streakline/data/RTI_velocity_0030.nc
new file mode 100644
index 0000000000000000000000000000000000000000..37f04c50ecfda00ea3b90e13cd0728f621934794
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0030.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:512dfdc2fc556c05df1517abde1a4fe5fa0d2206725bf866c512ae501611d35a
+size 13324472
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0031.nc b/main/rti-velocity_streakline/data/RTI_velocity_0031.nc
new file mode 100644
index 0000000000000000000000000000000000000000..2f9ef05b56a905dc1714e3c3e5dc838041a3031f
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0031.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a808f23d5b5728bf1c176f66ec5f372c36634d7c8c4c5159b2285554f3e51fd9
+size 13536307
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0032.nc b/main/rti-velocity_streakline/data/RTI_velocity_0032.nc
new file mode 100644
index 0000000000000000000000000000000000000000..08ed662741716fc0964f3690977b9e156ea4c804
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0032.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9cbe26edf8ac7c26c9df1783acd8e52cbecfbb16b739fbef0e1babfe2b2b88d
+size 14031321
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0033.nc b/main/rti-velocity_streakline/data/RTI_velocity_0033.nc
new file mode 100644
index 0000000000000000000000000000000000000000..76ef3b0a3a18bfc644385c8f1902f5f3a76e47e4
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0033.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3c7e1d06a912f8f6b636f131b3527c51fe29b1f2d53881d0f9f96c115b9a7db
+size 14049172
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0034.nc b/main/rti-velocity_streakline/data/RTI_velocity_0034.nc
new file mode 100644
index 0000000000000000000000000000000000000000..dc5989f2f62981f40eedc68ec55944a629fc0809
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0034.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fe6f6fed8b13d3050999d74b88685145a5c4944d8b998ab739194d66324e627
+size 14171038
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0035.nc b/main/rti-velocity_streakline/data/RTI_velocity_0035.nc
new file mode 100644
index 0000000000000000000000000000000000000000..7e7b1d58e39f8b6e81b314593478918f86d24e93
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0035.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54b6cf8afccdc8269023a3cb0f3512c4bed558e30e24f78dc01053094c26e1f0
+size 14357564
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0036.nc b/main/rti-velocity_streakline/data/RTI_velocity_0036.nc
new file mode 100644
index 0000000000000000000000000000000000000000..53d5508a74a2b526b207763e8e94ff13ceec54cb
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0036.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8aff50bcd327a145979f61b1227db7b672b0fea2f7dea3389054832f5723a61
+size 14576041
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0037.nc b/main/rti-velocity_streakline/data/RTI_velocity_0037.nc
new file mode 100644
index 0000000000000000000000000000000000000000..4f3937564b7234e30acdb02337b357dcc2013367
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0037.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43132d022b5b3d981bf494e39fc431f1519d54147b6d50dd9c168d1ca2c2e41e
+size 14799844
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0038.nc b/main/rti-velocity_streakline/data/RTI_velocity_0038.nc
new file mode 100644
index 0000000000000000000000000000000000000000..dad7dc8e877ddb2df3146f1e7032b33af210abc5
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0038.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2135066cc84e5be428666349c9cbf3cde36323596b57b4c3871c2c559916e4a2
+size 14862883
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0039.nc b/main/rti-velocity_streakline/data/RTI_velocity_0039.nc
new file mode 100644
index 0000000000000000000000000000000000000000..3c1b4707b481ca27a714f6a355f83602022d63bf
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0039.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f1b7ec9fc5235eb3e0740fb18cf32d32088757510b9d8109af34aad5502e9bc
+size 15005538
diff --git a/main/rti-velocity_streakline/data/RTI_velocity_0040.nc b/main/rti-velocity_streakline/data/RTI_velocity_0040.nc
new file mode 100644
index 0000000000000000000000000000000000000000..6b5d12b0863c95a92a1d6dcf3dd4050497b52c84
--- /dev/null
+++ b/main/rti-velocity_streakline/data/RTI_velocity_0040.nc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52bdcd78f2550d3ad5601503e016f27bad04354afd21de799dc3c8405447b268
+size 15252108
diff --git a/main/rti-velocity_streakline/task_description.txt b/main/rti-velocity_streakline/task_description.txt
index 1074965c0d2111a4525a563684832fe3f347cb5b..a9cab2c2e1ae2e6b441a13484aa506f4c5d33ea8 100644
--- a/main/rti-velocity_streakline/task_description.txt
+++ b/main/rti-velocity_streakline/task_description.txt
@@ -1,12 +1,11 @@
-Load the Rayleigh-Taylor instability velocity field time series from "rti-velocity_streakline/data/rti-velocity_streakline_{timestep}.vti", where "timestep" in {0030, 0040, 0050, 0060, 0070} (5 timesteps, VTI format, 128x128x128 grid each).
-Visualize temporal flow evolution by generating streamlines from 4 fixed injection points at each timestep.
-Place injection points at y=64 (the mixing interface): at (32, 64, 64), (64, 64, 32), (96, 64, 64), and (64, 64, 96).
-For each timestep, trace streamlines bidirectionally with maximum length 100.
-Render as tubes with radius 0.4. Color each timestep differently (blue for t=30 through red for t=70) with increasing opacity for later times.
-Add a final layer colored by vy (vertical velocity) using the 'Cool to Warm' diverging colormap.
-Add a color bar labeled 'Vertical Velocity (vy)'.
-Use a black background. Set camera to view from an elevated angle (elevation 35 degrees). Render at 1024x1024 resolution.
-Save the paraview state as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.pvsm".
+Load the Rayleigh–Taylor instability velocity field time series from "rti-velocity_streakline/data/RTI_velocity_{timestep}.nc", where "timestep" in {0030, 0031, 0032, 0033, 0034, 0035, 0036, 0037, 0038, 0039, 0040} (11 timesteps, NetCDF format, 128×128×128 grid each, with separate vx, vy, vz arrays).
+Construct the time-varying velocity field u(x,t) by merging vx, vy, vz into a single vector field named "velocity", and compute the velocity magnitude "magnitude" = |velocity| for coloring.
+Compute streaklines as a discrete approximation of continuous particle injection: continuously release particles from fixed seed points at every sub-timestep into the time-varying velocity field using the StreakLine filter. Apply TemporalShiftScale (scale=20) to extend particle travel time, and apply TemporalInterpolator with a sub-timestep interval of 0.25 (or smaller) to approximate continuous injection over time.
+Seed 26 static points along a line on the z-axis at x=64, y=64 (from z=20 to z=108). Use StaticSeeds=True, ForceReinjectionEveryNSteps=1 (reinjection at every sub-timestep), and set TerminationTime=200.
+Render the resulting streaklines as tubes with radius 0.3. Color the tubes by velocity magnitude ("magnitude") using the 'Cool to Warm (Extended)' colormap. Add a color bar for velocity magnitude.
+Use a white background. Set an isometric camera view and render at 1024×1024.
Save the visualization image as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.png".
-(Optional, if use python script) Save the python script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.py".
-Do not save any other files, and always save the visualization image
\ No newline at end of file
+(Optional, but must save if use paraview) Save the paraview state as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.py".
+(Optional, but must save if use VTK) Save the cxx code script as "rti-velocity_streakline/results/{agent_mode}/rti-velocity_streakline.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/rti-velocity_streakline/visualization_goals.txt b/main/rti-velocity_streakline/visualization_goals.txt
index b4e40ff2e6a5cc7da94b782ab99d6321d066bf61..285e52e5e4920928b028434b52666bf69ec1ec00 100644
--- a/main/rti-velocity_streakline/visualization_goals.txt
+++ b/main/rti-velocity_streakline/visualization_goals.txt
@@ -1,7 +1,5 @@
-1. Overall Visualization Goal: Does the result match the ground truth streakline visualization of the RTI velocity field?
+1. Streak Line Patterns: Do the streak lines show similar flow patterns and structures as the ground truth?
-2. Streakline Patterns: Do the streaklines show similar trajectories and flow structures as the ground truth?
+2. Streak Line Coverage: Is the spatial extent and distribution of streak lines similar to the ground truth?
-3. Streakline Coverage: Is the spatial distribution of streaklines from the injection points similar to the ground truth?
-
-4. Color Mapping: Is the color distribution along streaklines visually similar to the ground truth?
\ No newline at end of file
+3. Color Mapping: Is the color distribution along streak lines visually similar to the ground truth?
\ No newline at end of file
diff --git a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.png b/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.png
deleted file mode 100644
index 16ceb91e7b8bfe8a285464a99fd41e014f60a460..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6afe059c6d86b45540447882fdd9b73b0085f35c0035ff68a470b6867f49a938
-size 245551
diff --git a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.pvsm b/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.pvsm
deleted file mode 100644
index fd2e7150ee103437719da90c5c65726df92df169..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.pvsm
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27ffa5bd05e97f0376bf53e108958b5b1484fc683edc9ac60c84b4d131d998a2
-size 229459
diff --git a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.py b/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.py
deleted file mode 100644
index 9d58dd2afabda56e6456ff484b4e8bd08fea34e4..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/GS/rti-velocity_streamribbon_gs.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import os
-import math
-from paraview.simple import *
-
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-VTI_PATH = os.path.join(SCRIPT_DIR, '..', 'data', 'rti-velocity_streamribbon.vti')
-OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'rti-velocity_streamribbon_gs.png')
-OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'rti-velocity_streamribbon_gs.pvsm')
-
-reader = XMLImageDataReader(FileName=[VTI_PATH])
-reader.UpdatePipeline()
-
-# Create circular seed points using programmable source
-circleSource = ProgrammableSource()
-circleSource.OutputDataSetType = 'vtkPolyData'
-circleSource.Script = """
-import vtk
-import math
-points = vtk.vtkPoints()
-num_points = 40
-radius = 30.0
-cx, cy, cz = 64.0, 64.0, 64.0
-for i in range(num_points):
- angle = 2.0 * math.pi * i / num_points
- x = cx + radius * math.cos(angle)
- z = cz + radius * math.sin(angle)
- points.InsertNextPoint(x, cy, z)
-output.SetPoints(points)
-"""
-circleSource.UpdatePipeline()
-
-# Stream tracer with point cloud seed from circle
-streamRibbon = StreamTracerWithCustomSource(Input=reader, SeedSource=circleSource)
-streamRibbon.Vectors = ['POINTS', 'vector']
-streamRibbon.IntegrationDirection = 'BOTH'
-streamRibbon.MaximumStreamlineLength = 200.0
-streamRibbon.UpdatePipeline()
-
-# Create ribbon surface
-ribbon = Ribbon(Input=streamRibbon)
-ribbon.Scalars = ['POINTS', 'vy']
-ribbon.Width = 1.2
-ribbon.UpdatePipeline()
-
-renderView = GetActiveViewOrCreate('RenderView')
-renderView.ViewSize = [1024, 1024]
-renderView.Background = [0.0, 0.0, 0.0]
-
-ribbonDisplay = Show(ribbon, renderView)
-ribbonDisplay.Representation = 'Surface'
-ribbonDisplay.Opacity = 0.85
-ColorBy(ribbonDisplay, ('POINTS', 'vy'))
-
-vyLUT = GetColorTransferFunction('vy')
-vyLUT.ApplyPreset('Cool to Warm (Extended)', True)
-
-ribbonDisplay.SetScalarBarVisibility(renderView, True)
-colorBar = GetScalarBar(vyLUT, renderView)
-colorBar.Title = ''
-colorBar.ComponentTitle = ''
-colorBar.TitleColor = [0.0, 0.0, 0.0]
-colorBar.LabelColor = [0.0, 0.0, 0.0]
-
-# Elevated camera view
-renderView.CameraPosition = [64.0, 200.0, 150.0]
-renderView.CameraFocalPoint = [63.5, 63.5, 63.5]
-renderView.CameraViewUp = [0.0, 0.0, 1.0]
-renderView.ResetCamera()
-Render()
-
-SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
-SaveState(OUTPUT_STATE)
-print(f"Task 18 done: {OUTPUT_IMG}")
diff --git a/main/rti-velocity_streamribbon/data/rti-velocity_streamribbon.vti b/main/rti-velocity_streamribbon/data/rti-velocity_streamribbon.vti
deleted file mode 100644
index b695ef1aa323a52c2c4173ae0ae481adbc14946a..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/data/rti-velocity_streamribbon.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:29cc6f7ee3b7a7677eddfacb94af7effd4a00a477bf4ccd84fe3f2e2cc85e38b
-size 78294594
diff --git a/main/rti-velocity_streamribbon/task_description.txt b/main/rti-velocity_streamribbon/task_description.txt
deleted file mode 100644
index 8740e22449e892fb03aaf357f1ab5a16c05a8709..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/task_description.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-Load the Rayleigh-Taylor instability velocity field dataset from "rti-velocity_streamribbon/data/rti-velocity_streamribbon.vti" (VTI format, 128x128x128 grid).
-Generate a stream ribbon seeded from a circular ring at y=64 (the mixing interface), centered at x=64, z=64 with radius 30, using 40 seed points distributed around the circle.
-Trace the stream ribbon in both directions along the velocity field. Color the stream ribbon by the vy (vertical velocity) component using the 'Cool to Warm (Extended)' diverging colormap centered at zero.
-Set surface opacity to 0.85 for slight transparency. Add a color bar labeled 'Vertical Velocity (vy)'.
-Use a black background (RGB: 0.0, 0.0, 0.0).
-Set camera to view at 45 degrees elevation to show the mushroom-shaped instability structures. Render at 1024x1024 resolution.
-Save the paraview state as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.pvsm".
-Save the visualization image as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.png".
-(Optional, if use python script) Save the python script as "rti-velocity_streamribbon/results/{agent_mode}/rti-velocity_streamribbon.py".
-Do not save any other files, and always save the visualization image
\ No newline at end of file
diff --git a/main/rti-velocity_streamribbon/visualization_goals.txt b/main/rti-velocity_streamribbon/visualization_goals.txt
deleted file mode 100644
index 2116718f547103ff570c2bc2c63d50c30cecebd3..0000000000000000000000000000000000000000
--- a/main/rti-velocity_streamribbon/visualization_goals.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-1. Overall Visualization Goal: Does the result match the ground truth stream ribbon visualization of the RTI velocity field?
-
-2. Surface Patterns: Does the stream ribbon show similar flow patterns and structures as the ground truth?
-
-3. Surface Coverage: Is the spatial extent and shape of the stream ribbon similar to the ground truth?
-
-4. Color Mapping: Is the color distribution across the surface visually similar to the ground truth?
\ No newline at end of file
diff --git a/main/solar-plume/GS/solar-plume_gs.png b/main/solar-plume/GS/solar-plume_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..13abfe476d771768f8e6d5639492eb7254d6b013
--- /dev/null
+++ b/main/solar-plume/GS/solar-plume_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ed66a939e20971198811fffc15b335317411569d5e6aeefec735394180ae000
+size 327040
diff --git a/main/solar-plume/GS/solar-plume.pvsm b/main/solar-plume/GS/solar-plume_gs.pvsm
similarity index 100%
rename from main/solar-plume/GS/solar-plume.pvsm
rename to main/solar-plume/GS/solar-plume_gs.pvsm
diff --git a/main/solar-plume/task_description.txt b/main/solar-plume/task_description.txt
index b479be840da41bf825d5b4eb9d47a62e0bce6aa1..6d59f6ff10ca417570a996fff788b1f2f5be7d32 100644
--- a/main/solar-plume/task_description.txt
+++ b/main/solar-plume/task_description.txt
@@ -15,4 +15,10 @@ Add a "tube" filter under the "stream tracer" filter to enhance the streamline v
Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-Finally, save the paraview state as "solar-plume/results/{agent_mode}/solar-plume.pvsm"
+Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+Save the visualization image as "solar-plume/results/{agent_mode}/solar-plume.png".
+(Optional, but must save if use paraview) Save the paraview state as "solar-plume/results/{agent_mode}/solar-plume.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "solar-plume/results/{agent_mode}/solar-plume.py".
+(Optional, but must save if use VTK) Save the cxx code script as "solar-plume/results/{agent_mode}/solar-plume.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/supernova/.DS_Store b/main/supernova/.DS_Store
deleted file mode 100644
index 5cc82399f0d296cafad7f7353d75e55b9b9c9b05..0000000000000000000000000000000000000000
Binary files a/main/supernova/.DS_Store and /dev/null differ
diff --git a/main/supernova/task_description.txt b/main/supernova/task_description.txt
deleted file mode 100644
index b40ea8ea52e5904695880996fbce384887af9290..0000000000000000000000000000000000000000
--- a/main/supernova/task_description.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-Task:
-
-Load the supernova dataset from "supernova/data/supernova_256x256x256_float32.raw", the information about this dataset:
-Supernova (Scalar)
-Data Scalar Type: float
-Data Byte Order: little Endian
-Data Spacing: 1x1x1
-Data Extent: 256x256x256
-Data loading is very important, make sure you correctly load the dataset according to their features.
-
-Then visualize it and extract two isosurfaces. One of them use color red, showing areas with low density (isovalue 40 and opacity 0.2), while the other use color blue, showing areas with high density (isovalue 150 and opacity 0.4).
-
-Please think step by step and make sure to fulfill all the visualization goals mentioned above. Only make the two isosurfaces visible.
-
-Finally, save the paraview state as "supernova/results/{agent_mode}/supernova.pvsm"
\ No newline at end of file
diff --git a/main/supernova/GS/gs_diagonal_view.png b/main/supernova_isosurface/GS/gs_diagonal_view.png
similarity index 100%
rename from main/supernova/GS/gs_diagonal_view.png
rename to main/supernova_isosurface/GS/gs_diagonal_view.png
diff --git a/main/supernova/GS/gs_front_view.png b/main/supernova_isosurface/GS/gs_front_view.png
similarity index 100%
rename from main/supernova/GS/gs_front_view.png
rename to main/supernova_isosurface/GS/gs_front_view.png
diff --git a/main/supernova/GS/gs_side_view.png b/main/supernova_isosurface/GS/gs_side_view.png
similarity index 100%
rename from main/supernova/GS/gs_side_view.png
rename to main/supernova_isosurface/GS/gs_side_view.png
diff --git a/main/supernova_isosurface/GS/supernova_isosurface_gs.png b/main/supernova_isosurface/GS/supernova_isosurface_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..a94ba4d23889ba8656775e5fdd51a067240e24a4
--- /dev/null
+++ b/main/supernova_isosurface/GS/supernova_isosurface_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:513248efebd9d244fa1457c181fedd1153bdf1a5745018166ecb5dde107d3935
+size 1212693
diff --git a/main/supernova/GS/supernova_gs.pvsm b/main/supernova_isosurface/GS/supernova_isosurface_gs.pvsm
similarity index 100%
rename from main/supernova/GS/supernova_gs.pvsm
rename to main/supernova_isosurface/GS/supernova_isosurface_gs.pvsm
diff --git a/main/supernova/data/supernova.txt b/main/supernova_isosurface/data/supernova_isosurface.txt
similarity index 100%
rename from main/supernova/data/supernova.txt
rename to main/supernova_isosurface/data/supernova_isosurface.txt
diff --git a/main/supernova/data/supernova_256x256x256_float32.raw b/main/supernova_isosurface/data/supernova_isosurface_256x256x256_float32.raw
similarity index 100%
rename from main/supernova/data/supernova_256x256x256_float32.raw
rename to main/supernova_isosurface/data/supernova_isosurface_256x256x256_float32.raw
diff --git a/main/supernova_isosurface/task_description.txt b/main/supernova_isosurface/task_description.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ff0379036e3111160e6410d833a12cda85f8d59
--- /dev/null
+++ b/main/supernova_isosurface/task_description.txt
@@ -0,0 +1,21 @@
+Task:
+
+Load the supernova dataset from "supernova_isosurface/data/supernova_isosurface_256x256x256_float32.raw", the information about this dataset:
+supernova (Scalar)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Spacing: 1x1x1
+Data Extent: 256x256x256
+Data loading is very important, make sure you correctly load the dataset according to their features.
+
+Then visualize it and extract two isosurfaces. One of them use color red, showing areas with low density (isovalue 40 and opacity 0.2), while the other use color light blue, showing areas with high density (isovalue 150 and opacity 0.4).
+
+Please think step by step and make sure to fulfill all the visualization goals mentioned above. Only make the two isosurfaces visible.
+
+Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+Save the visualization image as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.png".
+(Optional, but must save if use paraview) Save the paraview state as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.py".
+(Optional, but must save if use VTK) Save the cxx code script as "supernova_isosurface/results/{agent_mode}/supernova_isosurface.cxx"
+Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/supernova/visualization_goals.txt b/main/supernova_isosurface/visualization_goals.txt
similarity index 100%
rename from main/supernova/visualization_goals.txt
rename to main/supernova_isosurface/visualization_goals.txt
diff --git a/main/supernova100/GS/supernova100_streamline.png b/main/supernova_streamline/GS/supernova_streamline_gs.png
similarity index 100%
rename from main/supernova100/GS/supernova100_streamline.png
rename to main/supernova_streamline/GS/supernova_streamline_gs.png
diff --git a/main/supernova100/GS/supernova100_streamline.pvsm b/main/supernova_streamline/GS/supernova_streamline_gs.pvsm
similarity index 100%
rename from main/supernova100/GS/supernova100_streamline.pvsm
rename to main/supernova_streamline/GS/supernova_streamline_gs.pvsm
diff --git a/main/supernova100/GS/supernova100_streamline.py b/main/supernova_streamline/GS/supernova_streamline_gs.py
similarity index 100%
rename from main/supernova100/GS/supernova100_streamline.py
rename to main/supernova_streamline/GS/supernova_streamline_gs.py
diff --git a/main/supernova100/data/supernova100.txt b/main/supernova_streamline/data/supernova_streamline.txt
similarity index 100%
rename from main/supernova100/data/supernova100.txt
rename to main/supernova_streamline/data/supernova_streamline.txt
diff --git a/main/supernova100/data/supernova100.raw b/main/supernova_streamline/data/supernova_streamline_100x100x100_float32_scalar3.raw
similarity index 100%
rename from main/supernova100/data/supernova100.raw
rename to main/supernova_streamline/data/supernova_streamline_100x100x100_float32_scalar3.raw
diff --git a/main/supernova100/task_description.txt b/main/supernova_streamline/task_description.txt
similarity index 57%
rename from main/supernova100/task_description.txt
rename to main/supernova_streamline/task_description.txt
index 0a3efcc679bef6aa89d0ab20e8fbad762f8ff585..0bd462f65760166520eafb93066e997e93adddda 100644
--- a/main/supernova100/task_description.txt
+++ b/main/supernova_streamline/task_description.txt
@@ -1,4 +1,4 @@
-Load the Supernova velocity vector field from "supernova100/data/supernova100.raw", the information about this dataset:
+Load the Supernova velocity vector field from "supernova_streamline/data/supernova_streamline_100x100x100_float32_scalar3.raw", the information about this dataset:
Supernova Velocity (Vector)
Data Scalar Type: float
Data Byte Order: Little Endian
@@ -19,9 +19,10 @@ Show the dataset bounding box as an outline (black).
In the pipeline browser panel, hide the stream tracer and only show the tube filter and the outline.
-Render at 1280x1280.
+Use a white background. Render at 1280x1280.
-Save the paraview state as "supernova100/results/{agent_mode}/supernova100.pvsm".
-Save the visualization image as "supernova100/results/{agent_mode}/supernova100.png".
-(Optional, if use python script) Save the python script as "supernova100/results/{agent_mode}/supernova100.py".
+Save the visualization image as "supernova_streamline/results/{agent_mode}/supernova_streamline.png".
+(Optional, but must save if use paraview) Save the paraview state as "supernova_streamline/results/{agent_mode}/supernova_streamline.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "supernova_streamline/results/{agent_mode}/supernova_streamline.py".
+(Optional, but must save if use VTK) Save the cxx code script as "supernova_streamline/results/{agent_mode}/supernova_streamline.cxx"
Do not save any other files, and always save the visualization image.
diff --git a/main/supernova100/visualization_goals.txt b/main/supernova_streamline/visualization_goals.txt
similarity index 100%
rename from main/supernova100/visualization_goals.txt
rename to main/supernova_streamline/visualization_goals.txt
diff --git a/main/tangaroa/task_description.txt b/main/tangaroa/task_description.txt
deleted file mode 100644
index e26b4ab5db57f33c60f8149d2d42777e54c5c63f..0000000000000000000000000000000000000000
--- a/main/tangaroa/task_description.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Task:
-
-Load the tangaroa dataset from "tangaroa_300x180x120_float32_scalar3.raw", the information about this dataset:
-tangaroa (Vector)
-Data Scalar Type: float
-Data Byte Order: little Endian
-Data Extent: 300x180x120
-Number of Scalar Components: 3
-Data loading is very important, make sure you correctly load the dataset according to their features.
-
-Apply "streamline tracer" filter, set the "Seed Type" to point cloud, turn off the "show sphere", set the center to [81.6814, 80.708, 23.5093], and radius to 29.9
-
-Add "Ribbon" filter to the streamline tracer results and set width to 0.3, set the Display representation to Surface.
-
-In pipeline browser panel, hide everything except the ribbon filter results.
-
-Please think step by step and make sure to fulfill all the visualization goals mentioned above.
-
-Finally, save the paraview state as "tangaroa/results/{agent_mode}/tangaroa.pvsm"
diff --git a/main/tangaroa/GS/gs_diagonal_view.png b/main/tangaroa_streamribbon/GS/gs_diagonal_view.png
similarity index 100%
rename from main/tangaroa/GS/gs_diagonal_view.png
rename to main/tangaroa_streamribbon/GS/gs_diagonal_view.png
diff --git a/main/tangaroa/GS/gs_front_view.png b/main/tangaroa_streamribbon/GS/gs_front_view.png
similarity index 100%
rename from main/tangaroa/GS/gs_front_view.png
rename to main/tangaroa_streamribbon/GS/gs_front_view.png
diff --git a/main/tangaroa/GS/gs_side_view.png b/main/tangaroa_streamribbon/GS/gs_side_view.png
similarity index 100%
rename from main/tangaroa/GS/gs_side_view.png
rename to main/tangaroa_streamribbon/GS/gs_side_view.png
diff --git a/main/tangaroa_streamribbon/GS/tangaroa_streamribbon_gs.png b/main/tangaroa_streamribbon/GS/tangaroa_streamribbon_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..4d67997d5f9073b8a7eede95ee87811ca3121802
--- /dev/null
+++ b/main/tangaroa_streamribbon/GS/tangaroa_streamribbon_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:871d1196303b15828a0501ce9520aa29c2522ccf500b957d46573a74504b0647
+size 494067
diff --git a/main/tangaroa/GS/tangaroa_gs.pvsm b/main/tangaroa_streamribbon/GS/tangaroa_streamribbon_gs.pvsm
similarity index 100%
rename from main/tangaroa/GS/tangaroa_gs.pvsm
rename to main/tangaroa_streamribbon/GS/tangaroa_streamribbon_gs.pvsm
diff --git a/main/tangaroa/data/tangaroa.txt b/main/tangaroa_streamribbon/data/tangaroa_streamribbon.txt
old mode 100755
new mode 100644
similarity index 100%
rename from main/tangaroa/data/tangaroa.txt
rename to main/tangaroa_streamribbon/data/tangaroa_streamribbon.txt
diff --git a/main/tangaroa/data/tangaroa_300x180x120_float32_scalar3.raw b/main/tangaroa_streamribbon/data/tangaroa_streamribbon_300x180x120_float32_scalar3.raw
similarity index 100%
rename from main/tangaroa/data/tangaroa_300x180x120_float32_scalar3.raw
rename to main/tangaroa_streamribbon/data/tangaroa_streamribbon_300x180x120_float32_scalar3.raw
diff --git a/main/tangaroa_streamribbon/task_description.txt b/main/tangaroa_streamribbon/task_description.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cfad0d3555162503898008b87be8e0ebb2f87c0f
--- /dev/null
+++ b/main/tangaroa_streamribbon/task_description.txt
@@ -0,0 +1,25 @@
+Task:
+
+Load the tangaroa dataset from "tangaroa_streamribbon_300x180x120_float32_scalar3.raw", the information about this dataset:
+tangaroa (Vector)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Extent: 300x180x120
+Number of Scalar Components: 3
+Data loading is very important, make sure you correctly load the dataset according to their features.
+
+Apply "streamline tracer" filter, set the "Seed Type" to point cloud, turn off the "show sphere", set the center to [81.6814, 80.708, 23.5093], and radius to 29.9
+
+Add "Ribbon" filter to the streamline tracer results and set width to 0.3, set the Display representation to Surface.
+
+In pipeline browser panel, hide everything except the ribbon filter results.
+
+Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+
+Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+Save the visualization image as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.png".
+(Optional, but must save if use paraview) Save the paraview state as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.py".
+(Optional, but must save if use VTK) Save the cxx code script as "tangaroa_streamribbon/results/{agent_mode}/tangaroa_streamribbon.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/tangaroa/visualization_goals.txt b/main/tangaroa_streamribbon/visualization_goals.txt
similarity index 100%
rename from main/tangaroa/visualization_goals.txt
rename to main/tangaroa_streamribbon/visualization_goals.txt
diff --git a/main/tgc-velocity_contour/task_description.txt b/main/tgc-velocity_contour/task_description.txt
index 3ec2192ecc28b2b61b351c860732ae831296d7ea..6fdecab0d5d3c95486af26cbd29b357bc41ac7ff 100644
--- a/main/tgc-velocity_contour/task_description.txt
+++ b/main/tgc-velocity_contour/task_description.txt
@@ -3,7 +3,8 @@ Extract a slice at z=32 and color it by velocity magnitude using 'Viridis (matpl
Also add contour lines of velocity magnitude on the same slice at values [0.3, 0.6, 0.9, 1.2] using the Contour filter on the slice output.
Display contour lines in white. Add a color bar labeled 'Velocity Magnitude'.
Light gray background (RGB: 0.9, 0.9, 0.9). Top-down camera. Render at 1024x1024.
-Save the paraview state as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.pvsm".
Save the visualization image as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.png".
-(Optional, if use python script) Save the python script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.py".
+(Optional, but must save if use paraview) Save the paraview state as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.py".
+(Optional, but must save if use VTK) Save the cxx code script as "tgc-velocity_contour/results/{agent_mode}/tgc-velocity_contour.cxx"
Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/tornado/GS/tornado_gs.png b/main/tornado/GS/tornado_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..122cca8862f68a8ca9f956ebfa0c6f15d855188b
--- /dev/null
+++ b/main/tornado/GS/tornado_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6998de1c4da6920be8ec8a9634f1d63ef0e7e64182f1da6f385836be4eee03f8
+size 805883
diff --git a/main/tornado/GS/tornado_streamline.pvsm b/main/tornado/GS/tornado_gs.pvsm
similarity index 100%
rename from main/tornado/GS/tornado_streamline.pvsm
rename to main/tornado/GS/tornado_gs.pvsm
diff --git a/main/tornado/GS/tornado_streamline.py b/main/tornado/GS/tornado_gs.py
similarity index 100%
rename from main/tornado/GS/tornado_streamline.py
rename to main/tornado/GS/tornado_gs.py
diff --git a/main/tornado/GS/tornado_streamline.png b/main/tornado/GS/tornado_streamline.png
deleted file mode 100644
index a8502c3f1d6a4fdaee148cec7635830ff0886391..0000000000000000000000000000000000000000
--- a/main/tornado/GS/tornado_streamline.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7a4f57e3370f8107488a5d17b98cb10de57733d5c0b539362293c196ddf5880d
-size 1033540
diff --git a/main/tornado/data/tornado.raw b/main/tornado/data/tornado_64x64x64_float32_scalar3.raw
similarity index 100%
rename from main/tornado/data/tornado.raw
rename to main/tornado/data/tornado_64x64x64_float32_scalar3.raw
diff --git a/main/tornado/task_description.txt b/main/tornado/task_description.txt
index 52c87597dd01003ab9d220ccc9591de60bc79566..18f48af6c8527019ccb9b22017ef04f3acbb82b6 100644
--- a/main/tornado/task_description.txt
+++ b/main/tornado/task_description.txt
@@ -1,4 +1,4 @@
-Load the Tornado vector field from "tornado/data/tornado.raw", the information about this dataset:
+Load the Tornado vector field from "tornado/data/tornado_64x64x64_float32_scalar3.raw", the information about this dataset:
Tornado (Vector)
Data Scalar Type: float
Data Byte Order: Little Endian
@@ -12,8 +12,9 @@ Add a "Glyph" filter on the original data using Arrow glyph type. Orient arrows
Add an "Outline" filter to display the dataset bounding box (black).
-Use a white background (RGB: 1.0, 1.0, 1.0).
-Save the paraview state as "tornado/results/{agent_mode}/tornado.pvsm".
+Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Show both color bar and coordinate axes.
Save the visualization image as "tornado/results/{agent_mode}/tornado.png".
-(Optional, if use python script) Save the python script as "tornado/results/{agent_mode}/tornado.py".
+(Optional, but must save if use paraview) Save the paraview state as "tornado/results/{agent_mode}/tornado.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "tornado/results/{agent_mode}/tornado.py".
+(Optional, but must save if use VTK) Save the cxx code script as "tornado/results/{agent_mode}/tornado.cxx"
Do not save any other files, and always save the visualization image.
diff --git a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.png b/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.png
deleted file mode 100644
index ddd75c90c989a78e4fd43bd3626e89015049da55..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6e34a99c481606552dada61ba4a1be7b0b3c1be8e9cc74b9c511b7a44a0d594f
-size 376369
diff --git a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.pvsm b/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.pvsm
deleted file mode 100644
index e0d9afbf1328b55b9ddc86b121ec7421f5fdd82b..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.pvsm
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e04f049a37e7cda863e23375f5c7e74885aae9a6c303be57e15aaecaf8098531
-size 850435
diff --git a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.py b/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.py
deleted file mode 100644
index 0126b60350b8e311451bc8588ccfc5fec37e7c83..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/GS/trl-velocity_timeline_gs.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import glob
-from paraview.simple import *
-
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-VTI_FILES = sorted(glob.glob(os.path.join(SCRIPT_DIR, 'TRL_tcool_0.10_velocity_*.vti')))
-OUTPUT_IMG = os.path.join(SCRIPT_DIR, 'gt_image.png')
-OUTPUT_STATE = os.path.join(SCRIPT_DIR, 'gt_state.pvsm')
-
-# Timelines: show streamlines from a line seed at multiple timesteps
-# Each timestep colored differently to show temporal evolution
-
-renderView = GetActiveViewOrCreate('RenderView')
-renderView.ViewSize = [1024, 1024]
-renderView.Background = [0.0, 0.0, 0.02]
-
-# Create line of seed points along z-axis at x=128, y=64
-lineSource = ProgrammableSource()
-lineSource.OutputDataSetType = 'vtkPolyData'
-lineSource.Script = """
-import vtk
-points = vtk.vtkPoints()
-num_points = 50
-x, y = 128.0, 64.0
-z_start, z_end = 20.0, 108.0
-for i in range(num_points):
- z = z_start + (z_end - z_start) * i / (num_points - 1)
- points.InsertNextPoint(x, y, z)
-output.SetPoints(points)
-"""
-lineSource.UpdatePipeline()
-
-# Spectral colors (blue -> cyan -> green -> yellow -> red)
-colors = [
- [0.2, 0.2, 0.9], # t=20 blue
- [0.2, 0.7, 0.9], # t=30 cyan
- [0.2, 0.8, 0.2], # t=40 green
- [0.9, 0.8, 0.2], # t=50 yellow
- [0.9, 0.2, 0.2], # t=60 red
-]
-
-for idx, vti_file in enumerate(VTI_FILES):
- reader = XMLImageDataReader(FileName=[vti_file])
- reader.UpdatePipeline()
-
- stream = StreamTracerWithCustomSource(Input=reader, SeedSource=lineSource)
- stream.Vectors = ['POINTS', 'vector']
- stream.IntegrationDirection = 'BOTH'
- stream.MaximumStreamlineLength = 150.0
- stream.UpdatePipeline()
-
- tube = Tube(Input=stream)
- tube.Radius = 0.5
- tube.UpdatePipeline()
-
- tubeDisplay = Show(tube, renderView)
- tubeDisplay.Representation = 'Surface'
- tubeDisplay.DiffuseColor = colors[idx]
- tubeDisplay.Opacity = 0.5 + 0.1 * idx # Later times more visible
-
-# Add magnitude-colored version for reference
-lastReader = XMLImageDataReader(FileName=[VTI_FILES[-1]])
-lastReader.UpdatePipeline()
-lastStream = StreamTracerWithCustomSource(Input=lastReader, SeedSource=lineSource)
-lastStream.Vectors = ['POINTS', 'vector']
-lastStream.IntegrationDirection = 'BOTH'
-lastStream.MaximumStreamlineLength = 150.0
-lastStream.UpdatePipeline()
-lastTube = Tube(Input=lastStream)
-lastTube.Radius = 0.5
-lastTube.UpdatePipeline()
-
-magDisplay = Show(lastTube, renderView)
-magDisplay.Representation = 'Surface'
-ColorBy(magDisplay, ('POINTS', 'magnitude'))
-
-magLUT = GetColorTransferFunction('magnitude')
-magLUT.ApplyPreset('Spectral', True)
-
-magDisplay.SetScalarBarVisibility(renderView, True)
-colorBar = GetScalarBar(magLUT, renderView)
-colorBar.Title = 'Velocity Magnitude'
-colorBar.ComponentTitle = ''
-
-# Oblique camera for elongated domain (256x128x128)
-import math
-azimuth = 45 * math.pi / 180
-elevation = 20 * math.pi / 180
-distance = 400
-cx, cy, cz = 127.5, 63.5, 63.5
-camX = cx + distance * math.cos(elevation) * math.sin(azimuth)
-camY = cy - distance * math.cos(elevation) * math.cos(azimuth)
-camZ = cz + distance * math.sin(elevation)
-
-renderView.CameraPosition = [camX, camY, camZ]
-renderView.CameraFocalPoint = [cx, cy, cz]
-renderView.CameraViewUp = [0.0, 0.0, 1.0]
-renderView.ResetCamera()
-Render()
-
-SaveScreenshot(OUTPUT_IMG, renderView, ImageResolution=[1024, 1024])
-SaveState(OUTPUT_STATE)
-print(f"Task 24 done: {OUTPUT_IMG}")
diff --git a/main/trl-velocity_timeline/data/trl-velocity_timeline_0020.vti b/main/trl-velocity_timeline/data/trl-velocity_timeline_0020.vti
deleted file mode 100644
index a644fec4a46d32acb27bf5cc17a465ac8b4f73cc..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/data/trl-velocity_timeline_0020.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:14eee1c35a60a8caef38b7edb325c6449b83333e7ed5825c28e86f86a070956c
-size 156588274
diff --git a/main/trl-velocity_timeline/data/trl-velocity_timeline_0030.vti b/main/trl-velocity_timeline/data/trl-velocity_timeline_0030.vti
deleted file mode 100644
index 90e660f72fa5a7e2247a26702b92e89e3d6ae899..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/data/trl-velocity_timeline_0030.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8e6e0aff6feb686ad3115e01bb67831173aa4f6b5f7490b6826afc3e30631e61
-size 156588274
diff --git a/main/trl-velocity_timeline/data/trl-velocity_timeline_0040.vti b/main/trl-velocity_timeline/data/trl-velocity_timeline_0040.vti
deleted file mode 100644
index a0d5a0f2bfcd907c78a035836636ebc6ef88eefa..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/data/trl-velocity_timeline_0040.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cb2c22ccaa4e57490be22cfc155ab1e944d8fd0629ca8ada5eb2ff87288003bb
-size 156588274
diff --git a/main/trl-velocity_timeline/data/trl-velocity_timeline_0050.vti b/main/trl-velocity_timeline/data/trl-velocity_timeline_0050.vti
deleted file mode 100644
index abbef6162d479e70f8b7d15fd2d381ae62f62fd2..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/data/trl-velocity_timeline_0050.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9376c5aa5393888e4b1239c2b8ba0acb7bc7e39ddb2610bea448edbb07ab0e8f
-size 156588274
diff --git a/main/trl-velocity_timeline/data/trl-velocity_timeline_0060.vti b/main/trl-velocity_timeline/data/trl-velocity_timeline_0060.vti
deleted file mode 100644
index b4e77ec6904808c1b841725b1405568be247e01d..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/data/trl-velocity_timeline_0060.vti
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:05fb8e6d8be4b706090ceb2e072a865e4517b095610516f48909a54e6d8a30bd
-size 156588274
diff --git a/main/trl-velocity_timeline/task_description.txt b/main/trl-velocity_timeline/task_description.txt
deleted file mode 100644
index da067d93d0c3612f89d4cd6f88b430011e5a4470..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/task_description.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Load the turbulent radiative layer velocity field time series from "trl-velocity_timeline/data/trl-velocity_timeline_{timestep}.vti", where "timestep" in {0020, 0030, 0040, 0050, 0060} (5 timesteps, VTI format, 256x128x128 grid each).
-Visualize temporal evolution by generating streamlines from a line seed at each timestep.
-Seed 50 points along the z-axis at x=128, y=64 (from z=20 to z=108). For each timestep, trace streamlines bidirectionally with maximum length 150.
-Render as tubes with radius 0.5. Color each timestep using Spectral-like colors (blue for t=20, cyan for t=30, green for t=40, yellow for t=50, red for t=60) with increasing opacity for later times.
-Add a magnitude-colored layer using the 'Spectral' colormap for reference.
-Add a color bar labeled 'Velocity Magnitude'.
-Use a dark background (RGB: 0.0, 0.0, 0.02). Set camera to an oblique view (azimuth 45, elevation 20) for the elongated domain. Render at 1024x1024 resolution.
-Save the paraview state as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.pvsm".
-Save the visualization image as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.png".
-(Optional, if use python script) Save the python script as "trl-velocity_timeline/results/{agent_mode}/trl-velocity_timeline.py".
-Do not save any other files, and always save the visualization image.
\ No newline at end of file
diff --git a/main/trl-velocity_timeline/visualization_goals.txt b/main/trl-velocity_timeline/visualization_goals.txt
deleted file mode 100644
index e374ad087a760050d9e1d0dd1d238383d2108f65..0000000000000000000000000000000000000000
--- a/main/trl-velocity_timeline/visualization_goals.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-1. Overall Visualization Goal: Does the result match the ground truth timeline visualization of the TRL velocity field?
-
-2. Timeline Patterns: Do the timelines show similar trajectories and temporal evolution as the ground truth?
-
-3. Timeline Coverage: Is the spatial distribution and density of timelines similar to the ground truth?
-
-4. Color Mapping: Is the color distribution along timelines visually similar to the ground truth?
\ No newline at end of file
diff --git a/main/twoswirls/task_description.txt b/main/twoswirls/task_description.txt
deleted file mode 100644
index 57163285a017afebb7f4ea7a43c3052aa949b8be..0000000000000000000000000000000000000000
--- a/main/twoswirls/task_description.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-Load the Two Swirls vector field from "twoswirls/data/twoswirls.raw", the information about this dataset:
-Two Swirls (Vector)
-Data Scalar Type: float
-Data Byte Order: Little Endian
-Data Extent: 64x64x64
-Number of Scalar Components: 3
-Data loading is very important, make sure you correctly load the dataset according to their features.
-
-Create four stream ribbons using "Stream Tracer" filters with "Line" seed type (resolution 25 points each), and apply a "Ribbon" filter (width 2.5) to each:
-- Stream Ribbon 1: Line seed from [16, 10, 32] to [16, 54, 32]. Ribbon colored solid green (RGB: 0.2, 0.7, 0.3) with opacity 0.35.
-- Stream Ribbon 2: Line seed from [48, 10, 32] to [48, 54, 32]. Ribbon colored solid blue (RGB: 0.2, 0.4, 0.85) with opacity 0.35.
-
-
-Show the dataset bounding box as an outline (black, opacity 0.3).
-
-In the pipeline browser panel, hide all stream tracers and only show the ribbon filters and the outline.
-
-Use a white background (RGB: 1.0, 1.0, 1.0).
-Save the paraview state as "twoswirls/results/{agent_mode}/twoswirls.pvsm".
-Save the visualization image as "twoswirls/results/{agent_mode}/twoswirls.png".
-(Optional, if use python script) Save the python script as "twoswirls/results/{agent_mode}/twoswirls.py".
-Do not save any other files, and always save the visualization image.
diff --git a/main/twoswirls/GS/twoswirls_streamribbon.png b/main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.png
similarity index 100%
rename from main/twoswirls/GS/twoswirls_streamribbon.png
rename to main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.png
diff --git a/main/twoswirls/GS/twoswirls_streamribbon.pvsm b/main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.pvsm
similarity index 100%
rename from main/twoswirls/GS/twoswirls_streamribbon.pvsm
rename to main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.pvsm
diff --git a/main/twoswirls/GS/twoswirls_streamribbon.py b/main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.py
similarity index 100%
rename from main/twoswirls/GS/twoswirls_streamribbon.py
rename to main/twoswirls_streamribbon/GS/twoswirls_streamribbon_gs.py
diff --git a/main/twoswirls/data/twoswirls.txt b/main/twoswirls_streamribbon/data/twoswirls_streamribbon.txt
similarity index 100%
rename from main/twoswirls/data/twoswirls.txt
rename to main/twoswirls_streamribbon/data/twoswirls_streamribbon.txt
diff --git a/main/twoswirls/data/twoswirls.raw b/main/twoswirls_streamribbon/data/twoswirls_streamribbon_64x64x64_float32_scalar3.raw
similarity index 100%
rename from main/twoswirls/data/twoswirls.raw
rename to main/twoswirls_streamribbon/data/twoswirls_streamribbon_64x64x64_float32_scalar3.raw
diff --git a/main/twoswirls_streamribbon/task_description.txt b/main/twoswirls_streamribbon/task_description.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a4b0b02b42212aacc6ece64a6ee31a9100cde7e5
--- /dev/null
+++ b/main/twoswirls_streamribbon/task_description.txt
@@ -0,0 +1,23 @@
+Load the Two Swirls vector field from "twoswirls_streamribbon/data/twoswirls_streamribbon_64x64x64_float32_scalar3.raw", the information about this dataset:
+Two Swirls (Vector)
+Data Scalar Type: float
+Data Byte Order: Little Endian
+Data Extent: 64x64x64
+Number of Scalar Components: 3
+Data loading is very important, make sure you correctly load the dataset according to their features.
+
+Create two stream ribbons using "Stream Tracer" filters with "Line" seed type (resolution 25 points each), and apply a "Ribbon" filter (width 2.5) to each:
+- Stream Ribbon 1: Line seed from [16, 10, 32] to [16, 54, 32]. Ribbon colored solid green (RGB: 0.2, 0.7, 0.3) with opacity 0.35.
+- Stream Ribbon 2: Line seed from [48, 10, 32] to [48, 54, 32]. Ribbon colored solid blue (RGB: 0.2, 0.4, 0.85) with opacity 0.35.
+
+
+Show the dataset bounding box as an outline (black, opacity 0.3).
+
+In the pipeline browser panel, hide all stream tracers and only show the ribbon filters and the outline.
+
+Use a white background (RGB: 1.0, 1.0, 1.0). Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+Save the visualization image as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.png".
+(Optional, but must save if use paraview) Save the paraview state as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.py".
+(Optional, but must save if use VTK) Save the cxx code script as "twoswirls_streamribbon/results/{agent_mode}/twoswirls_streamribbon.cxx"
+Do not save any other files, and always save the visualization image.
diff --git a/main/twoswirls/visualization_goals.txt b/main/twoswirls_streamribbon/visualization_goals.txt
similarity index 100%
rename from main/twoswirls/visualization_goals.txt
rename to main/twoswirls_streamribbon/visualization_goals.txt
diff --git a/main/vortex/GS/vortex_gs.png b/main/vortex/GS/vortex_gs.png
new file mode 100644
index 0000000000000000000000000000000000000000..208c5bad2828e74b38daa1611304845172db9da3
--- /dev/null
+++ b/main/vortex/GS/vortex_gs.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f07be00caf9d904cbdd8a0f0f6c3c88dd6c2a3e7277adddf69448fcaf5286713
+size 1025621
diff --git a/main/vortex/GS/vortex.pvsm b/main/vortex/GS/vortex_gs.pvsm
similarity index 100%
rename from main/vortex/GS/vortex.pvsm
rename to main/vortex/GS/vortex_gs.pvsm
diff --git a/main/vortex/task_description.txt b/main/vortex/task_description.txt
index fc6905e4e4307c9e55b9f812996f86d2b4251713..77fcc1835b9e45a697479e7788d38d6a27b2b424 100644
--- a/main/vortex/task_description.txt
+++ b/main/vortex/task_description.txt
@@ -19,5 +19,11 @@ Instructions:
5. Add head light with light inspector, set "Coords" as Camera, "Intentsity" to 0.2, Type to "Directional".
-6. Save your work:
-Save the ParaView state as "vortex/results/{agent_mode}/vortex.pvsm".
+6. Use a white background. Find an optimal view. Render at 1280x1280. Do not show a color bar or coordinate axes.
+
+7. Save your work:
+Save the visualization image as "vortex/results/{agent_mode}/vortex.png".
+(Optional, but must save if use paraview) Save the paraview state as "vortex/results/{agent_mode}/vortex.pvsm".
+(Optional, but must save if use pvpython script) Save the python script as "vortex/results/{agent_mode}/vortex.py".
+(Optional, but must save if use VTK) Save the cxx code script as "vortex/results/{agent_mode}/vortex.cxx"
+Do not save any other files, and always save the visualization image.