🎯 SciVisAgentBench Evaluation Report

1. Load the file "brain/data/brain.vti". It is a symmetric tensor field, where the (1,1), (1,2) and (2,2) components of the tensor are respectively given by the arrays A, B, and D. 2. Compute degenerate points of the tensor field. 3. Save the degenerate points as "brain/results/{agent_mode}/brain.vtk" in legacy VTK format. Label the type of degenerate point for each point in an array called DegeneracyType. Use a value of 0 for trisectors and 1 for wedges. 4. Analyze the visualization and answer the following questions: Q1: Are there more trisectors than wedges? (yes/no) Q2: Out of all degenerate points, the sum of one point's coordinates is the highest. What is this highest sum, rounded to the nearest integer? (A) 124 (B) 136 (C) 148 (D) 160 Save the answers to the analysis questions in plain text as "brain/results/{agent_mode}/answers.txt". Do not save any files other than the specified result files.

1. Please load the file "cylinder/data/cylinder.vti" 2. Apply persistence simplification of 0.01 to the Speed field. 3. Compute the Morse-Smale segmentation of the simplified Speed field. 4. Save the Morse-Smale segmentation as "cylinder/results/{agent_mode}/cylinder.vti". It should have a point array called Partition. For each point x, the array "Partition" should store the id number of the region in the segmentation that x belongs to. 5. Analyze the visualization and answer the following questions: Q1: How many unique partition regions are there? (A) 152 (B) 163 (C) 174 (D) 185 Q2: How many points are in the largest partition region? (A) 6879 (B) 7968 (C) 8796 (D) 9687 Save the answers to the analysis questions in plain text as "cylinder/results/{agent_mode}/answers.txt". Do not save any files other than the specified result files.

1. Load the file "isabel/data/isabel.vti". 2. Apply persistent simplification to the field "sf" with a persistence threshold of 0.04 3. Compute the merge tree of the simplified field. 4. Save the nodes of the merge tree as "isabel/results/{agent_mode}/isabel_nodes.vtk" in legacy VTK format. This file should have two point arrays. One should be called "CriticalType" and should store the type of critical point for each node. It should follow the following convention: 0: minima. 1: 1-saddles. 2: 2-saddles. 3: maxima. 4: degenerate critical points. The other point array should be called "Scalar" and should contain the scalar field value at each point in the merge tree. 5. Save the edges of the merge tree as "isabel/results/{agent_mode}/isabel_edges.vtk" in legacy VTK format. The file should store each edge as a separate cell with type vtkLine. 6. Analyze the visualization and answer the following questions: Q1: The parent node of the leaf (377, 265, 0) has coordinates (x,y,z). What is x+y+z? (A) 627 (B) 854 (C) 992 (D) 1039 Q2: How many edges are there in the merge tree? (A) 154 (B) 195 (C) 204 (D) 254 Q3: What is the highest scalar field value of a minimum, rounded to the nearest whole number? (A) 12 (B) 26 (C) 31 (D) 58 Save the answers to the analysis questions in plain text as "isabel/results/{agent_mode}/answers.txt". Do not save any files other than the specified result files.

1. Please load the asymmetric tensor field from "ocean/data/ocean.vti". The (1,1), (1,2), (2,1) and (2,2) entries are respectively given by the arrays A, B, C, and D 2. Compute the eigenvector partition of the dataset. 3. Save the degenerate points as "ocean/results/{agent_mode}/ocean_points.vtk" in legacy VTK format. Include a point array called DegeneracyType which classifies each degenerate point. It should have a value of 0 for trisectors and 1 for wedges. 4. Save the partition information from the eigenvector partition as "ocean/results/{agent_mode}/ocean_eigenvector.vti" as VTK image data. It should have a point array called Partition that stores the region identifiers as follows: 0: W_{c,s}. 1: W_{r,s}. 2: W_{r,n}. 3: W_{c,n} 5. Compute the eigenvalue partition of the dataset. 6. Save the partition information from the eigenvalue partition as "ocean/results/{agent_mode}/ocean_eigenvalue.vti" as VTK image data. It should have a point array called Partition that stores the region identifiers as follows: 0: positive scaling. 1: counterclockwise rotation. 2: negative scaling. 3: clockwise rotation. 4: anisotropic stretching. 7. Analyze the visualization and answer the following questions: Q1: Are there more trisectors than wedges? (yes/no) Q2: How many points have the most common classification in the eigenvector partition? (A) 752342 (B) 802842 (C) 826348 (D) 994682 Q3: Which is the least common classification in the eigenvalue partition? (A) Positive scaling (B) counterclockwise rotation (C) negative scaling (D) clockwise rotation Save the answers to the analysis questions in plain text as "ocean/results/{agent_mode}/answers.txt". Do not save any files other than the specified result files.

1. Please load the dataset from "QMCPACK/data/QMCPACK.vti". 2. Compute the critical points of the scalar field. 3. Save the critical points as "QMCPACK/results/{agent_mode}/QMCPACK.vtk" in legacy VTK format. - The output should contain the critical points as point data - Include an array called "CriticalType" that labels each point according to what type of critical type it is. Use the following convention: * 0 for minima * 1 for 1-saddles * 2 for 2-saddles * 3 for maxima * 4 for degenerate critical points - The point coordinates should be in index space (grid coordinates), not world coordinates 4. Analyze the visualization and answer the following questions: Q1: How many index 1 saddles are there: (A) 248 (B) 274 (C) 299 (D) 344 Q2: What is the type of critical point closest to coordinates (4,58,12): (A) minimum (B) 1-saddle (C) 2-saddle (D) maximum Save the answers to the analysis questions in plain text as "QMCPACK/results/{agent_mode}/answers.txt". Do not save any files other than the specified result files.