update main cases Feb 24

.gitignore

@@ -5,4 +5,6 @@ upload_huggingface.py
 # sci_volume_data/**/*.raw
 statistics/
 .cache/
-.claude/
+.claude/
+.vscode/
+eval_cases/paraview/main_cases_old.yaml

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.

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.

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
+
+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.

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

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"
+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.

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".
+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.

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
+
+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.

main/chameleon_volvis/data/chameleon.txt

@@ -1,5 +0,0 @@
-chameleon (Scalar)
-Data Scalar Type: float
-Data Byte Order: little Endian
-Data Extent: 256x256x270
-Number of Scalar Components: 1

main/chameleon_volvis/data/chameleon_volvis_256x256x270_float32.raw

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bff2380bd60471fedc7e67d016387d08073dbe119733944b9cbb8c6196201cc4
-size 70778880

main/chameleon_volvis/evaluation_results/mcp/test_result_1758566304.json

@@ -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
-  }
-}

main/chameleon_volvis/task_description.txt

@@ -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"

main/chameleon_volvis/test_results/pvpython/test_result_1759261119.json

@@ -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": "<html>\r\n<head><title>404 Not Found</title></head>\r\n<body>\r\n<center><h1>404 Not Found</h1></center>\r\n<hr><center>nginx</center>\r\n</body>\r\n</html>",
-    "token_usage": {
-      "input_tokens": 261,
-      "output_tokens": 0,
-      "total_tokens": 261
-    },
-    "end_time": "2025-09-30T15:38:39.246222",
-    "duration_seconds": 0.144628
-  }
-}

main/chameleon_volvis/test_results/pvpython/test_result_1759261263.json

@@ -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": "<html>\r\n<head><title>404 Not Found</title></head>\r\n<body>\r\n<center><h1>404 Not Found</h1></center>\r\n<hr><center>nginx</center>\r\n</body>\r\n</html>",
-    "token_usage": {
-      "input_tokens": 261,
-      "output_tokens": 0,
-      "total_tokens": 261
-    },
-    "end_time": "2025-09-30T15:41:03.951036",
-    "duration_seconds": 0.144785
-  }
-}

main/chameleon_volvis/visualization_goals.txt

@@ -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?

main/{crayfish → 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.

main/engine/GS/engine.pvsm

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:57d77bac59a328eb14ed6e2eed84a20ad93582462278d4e30301b8a342dc7045
-size 259002

main/{chameleon_volvis/GS/gs_side_view.png → engine/GS/engine_gs.pvsm}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1b51228a266cf780126c91afd5de6f8990e442503a2d78d884f23d8a7c63acd0
-size 131681
+oid sha256:664b38f1f680e03c0e66dd2471ad42227bb7f704b274cce38d1236e7bdedf2c9
+size 257739

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.

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".
+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.

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".
+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.

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.   
+(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.  

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

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}")