refine cases in chatvis_bench, and object identification cases

.gitattributes

@@ -11,4 +11,5 @@
 *.vtu filter=lfs diff=lfs merge=lfs -text
 *.vti filter=lfs diff=lfs merge=lfs -text
 *.cif filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
 *.nc filter=lfs diff=lfs merge=lfs -text

chatvis_bench/chart-opacity/visualization_goals.txt

@@ -1,6 +1,6 @@
-1. Chart Generation: Is the plot over line chart properly created from the wavelet data showing all three specified variables?
+1. Chart Generation: Is the plot over line chart properly created from the wavelet data?
 
-2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted and distinguishable in the chart?
+2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted, showing all three specified variables and distinguishable in the chart?
 
 3. Opacity Settings: Is the arc_length variable displayed with full opacity (1.0) while Points_Z and RTData show reduced opacity (0.3)?
 

chatvis_bench/climate/task_description.txt

@@ -4,4 +4,18 @@ Render the computed values using a tube filter with 0.05 radius, colored by velo
 Add cone glyphs to show the direction of the velocity, using 10 polygons, radius 0.15, height 0.5, and scaling factor 0.5.
 View the result in the -z direction scaled so that the tubes occupy most of the image.
 Save a screenshot of the result, 2294 x 1440 pixels, white background, in the filename "climate/results/{agent_mode}/climate.png".
+Finally, save the ParaView state as "climate/results/{agent_mode}/climate.pvsm"
+
+I would like to use ParaView to visualize a dataset of ocean currents.
+Read in the file named "climate/data/climate.vtp".
+Apply a calculator filter to compute the following function:
+(-velocity_X*sin(coordsX*0.0174533) + velocity_Y*cos(coordsX*0.0174533)) * iHat + (-velocity_X * sin(coordsY*0.0174533) * cos(coordsX*0.0174533) - velocity_Y * sin(coordsY*0.0174533) * sin(coordsX*0.0174533) + velocity_Z * cos(coordsY*0.0174533)) * jHat + 0*kHat
+Render the computed values using a tube filter with 0.05 as the tube radius.
+Color the tubes by the magnitude of the velocity.
+Light the tubes with the maximum shininess and include normals in the lighting.
+Add cone glyphs to show the direction of the velocity.
+The glyphs are composed of 10 polygons, having a radius 0 0.15, a height of 0.5, and a scaling factor of 0.5.
+View the result in the -z direction.
+Adjust the view so that the tubes occupy the 90% of the image.
+Save a screenshot of the result, 2294 x 1440 pixels, white background, in the filename "climate/results/{agent_mode}/climate.png".
 Finally, save the ParaView state as "climate/results/{agent_mode}/climate.pvsm"

chatvis_bench/climate/visualization_goals.txt

@@ -1,7 +1,5 @@
-1. Velocity Conversion: Is the calculator filter properly applied to convert velocity from geospatial to lat-long coordinates?
+1. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
 
-2. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
+2. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
 
-3. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
-
-4. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?
+3. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?

chatvis_bench/color-blocks/task_description.txt

@@ -1,8 +1,16 @@
+I would like to use ParaView to visualize a dataset.
+Set the background to a blue-gray palette.
 Read the file "color-blocks/data/color-blocks.ex2".
+This is a multiblock dataset.
 Color the dataset by the vtkBlockColors field.
-Retrieve the color map, opacity transfer function, and 2D transfer function for vtkBlockColors.
-Set block coloring for the block at /IOSS/element_blocks/block_2 using the x component of the ACCL variable.
+Retrieve the color map for vtkBlockColors.
+Retrieve the opacity transfer function for vtkBlockColors.
+Retrieve the 2D transfer function for vtkBlockColors.
+Set block coloring for the block at /IOSS/element_blocks/block_2 using the variable ACCL on the x component of the points.
 Rescale the block's color and opacity maps to match the current data range of block_2.
-For the ACCL variable of block_2, retrieve the color transfer function, enable the color bar, and apply cool to warm coloring.
-View the entire dataset in the -y direction, and save a screenshot with blue-gray background in "color-blocks/results/{agent_mode}/color-blocks.png".
+Retrieve the color transfer function for the ACCL variable of block_2.
+Enable the color bar for block_2.
+Apply a cool to warm color preset to the color map for block_2.
+Set the camera to look down the -y direction and to see the entire dataset.
+Save a screenshot of the visualization in the file "color-blocks/results/{agent_mode}/color-blocks.png".
 Finally, save the ParaView state as "color-blocks/results/{agent_mode}/color-blocks.pvsm"

chatvis_bench/color-data/visualization_goals.txt

@@ -1,7 +1,5 @@
-1. Calculator Function: Is the calculator properly implemented with the specified vector function combining RTData and coordinate components?
+1. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
 
-2. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
+2. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
 
-3. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
-
-4. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?
+3. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?

chatvis_bench/export-gltf/task_description.txt

@@ -6,7 +6,7 @@ Export the view to "export-gltf/results/{agent_mode}/ExportedGLTF.gltf".
 Next load the file "export-gltf/results/{agent_mode}/ExportedGLTF.gltf" and display it as a surface.
 Color this object by TEXCOORD_0.
 Scale the color map to the data, and don't display the color bar or the orientation axes.
-Use the 'Cool to Warm' colormap.
+Use the 'Cool to Warm' colormap. Set the background color to white.
 
 Save a screenshot to the file "export-gltf/results/{agent_mode}/export-gltf.png".
 Finally, save the ParaView state as "export-gltf/results/{agent_mode}/export-gltf.pvsm"

chatvis_bench/export-gltf/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. GLTF Export Quality: Is the wavelet object properly exported to GLTF format with correct surface representation and RTData coloring?
-
-2. GLTF Import and Display: Is the exported GLTF file successfully loaded and displayed as a surface with proper geometry?
-
-3. Texture Coordinate Coloring: Is the imported GLTF object correctly colored by TEXCOORD_0 with Cool to Warm colormap?
-
+1. GLTF Export Quality: Is the wavelet object properly exported to GLTF format with correct surface representation and RTData coloring?
+
+2. GLTF Import and Display: Is the exported GLTF file successfully loaded and displayed as a surface with proper geometry?
+
+3. Texture Coordinate Coloring: Is the imported GLTF object correctly colored by TEXCOORD_0 with Cool to Warm colormap?
+
 4. Clean Presentation: Are the color bar and orientation axes properly hidden for a clean visualization appearance?

chatvis_bench/import-gltf/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. GLTF Import Success: Are the specified GLTF nodes properly imported and displayed as separate geometric components?
-
-2. Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?
-
-3. Camera Positioning: Is the camera positioned in the positive Y direction with appropriate zoom to fit all imported geometry?
-
+1. GLTF Import Success: Are the specified GLTF nodes properly imported and displayed as separate geometric components?
+
+2. Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?
+
+3. Camera Positioning: Is the camera positioned in the positive Y direction with appropriate zoom to fit all imported geometry? Carefully compare the camera position of GT and result images.
+
 4. Layout Configuration: Is the view properly sized to 300x300 pixels with correct rendering and background palette?

chatvis_bench/materials/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. Side-by-Side Comparison: Are both datasets properly displayed in side-by-side views with correct dimensions and labeling?
-
-2. Data Conversion and Filtering: Are the Intensity and Phase variables correctly converted to point data and isovolume filtering applied?
-
-3. Clipping and Color Mapping: Is the plane clipping correctly applied and Viridis colormap properly used for Phase variable?
-
+1. Side-by-Side Comparison: Are both datasets properly displayed in side-by-side views with correct dimensions and labeling?
+
+2. Data Conversion and Filtering: Are the Intensity and Phase variables correctly converted to point data and isovolume filtering applied?
+
+3. Clipping and Color Mapping: Is the plane clipping correctly applied and Viridis colormap properly used for Phase variable?
+
 4. Camera and Layout: Is the camera positioned correctly in (-1, 0, -1) direction with appropriate fitting and legends visible?

chatvis_bench/ml-dvr/task_description.txt

@@ -1,3 +1,7 @@
-Read in the file named "ml-dvr/data/ml-dvr.vtk", and generate a volume rendering using the default transfer function.
-Save a screenshot, size 1920 x 1080 pixels, of an isometric view of the visualization in "ml-dvr/results/{agent_mode}/ml-dvr.png".
+I would like to use ParaView to visualize a dataset.
+Read in the file named "ml-dvr/data/ml-dvr.vtk".
+Generate a volume rendering using the default transfer function.
+Rotate the view to an isometric direction.
+Save a screenshot of the result in the filename "ml-dvr/results/{agent_mode}/ml-dvr.png".
+The rendered view and saved screenshot should be 1920 x 1080 pixels.
 Finally, save the ParaView state as "ml-dvr/results/{agent_mode}/ml-dvr.pvsm"

chatvis_bench/ml-dvr/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. Volume Rendering Quality: Is the volume rendering properly generated with appropriate opacity and color mapping that reveals internal structures?
-
-2. Transfer Function Application: Does the default transfer function effectively highlight meaningful data features and provide good visual contrast?
-
-3. Isometric View Setup: Is the visualization displayed from an isometric viewpoint that provides a clear three-dimensional perspective of the volume?
-
+1. Volume Rendering Quality: Is the volume rendering properly generated with appropriate opacity and color mapping that reveals internal structures?
+
+2. Transfer Function Application: Does the default transfer function effectively highlight meaningful data features and provide good visual contrast?
+
+3. Isometric View Setup: Is the visualization displayed from an isometric viewpoint that provides a clear three-dimensional perspective of the volume?
+
 4. Visual Clarity and Detail: Are the volume details clearly visible with proper lighting and shading that enhances depth perception?

chatvis_bench/ml-iso/task_description.txt

@@ -1,3 +1,3 @@
 Read in the file named "ml-iso/data/ml-iso.vtk", and generate an isosurface of the variable var0 at value 0.5.
-Save a screenshot of the result, size 1920 x 1080 pixels, in "ml-iso/results/{agent_mode}/ml-iso.png".
+Use a white background color. Save a screenshot of the result, size 1920 x 1080 pixels, in "ml-iso/results/{agent_mode}/ml-iso.png".
 Finally, save the ParaView state as "ml-iso/results/{agent_mode}/ml-iso.pvsm"

chatvis_bench/ml-iso/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. Isosurface Generation: Is the isosurface properly generated at the specified value (0.5) with correct topology and continuity?
-
-2. Surface Rendering Quality: Does the isosurface display smooth surfaces with appropriate shading and lighting that reveals the 3D structure?
-
-3. Geometric Accuracy: Are the surface features geometrically correct and free from artifacts or discontinuities?
-
+1. Isosurface Generation: Is the isosurface properly generated at the specified value (0.5) with correct topology and continuity?
+
+2. Surface Rendering Quality: Does the isosurface display smooth surfaces with appropriate shading and lighting that reveals the 3D structure?
+
+3. Geometric Accuracy: Are the surface features geometrically correct and free from artifacts or discontinuities?
+
 4. Visual Presentation: Is the isosurface clearly visible with good contrast and coloring that enhances the understanding of the data structure?

chatvis_bench/ml-slice-iso/task_description.txt

@@ -1,3 +1,9 @@
-Read in the file named "ml-slice-iso/data/ml-slice-iso.vtk", slice the volume with a y-z plane at x=0, and take a contour, colored red, through the slice at the value 0.5.
-Save a screenshot of a +x direction view, size 1920 x 1080 pixels, of the result in "ml-slice-iso/results/{agent_mode}/ml-slice-iso.png".
+Please generate a ParaView Python script for the following operations. 
+Read in the file named "ml-slice-iso/data/ml-slice-iso.vtk". 
+Slice the volume in a plane parallel to the y-z plane at x=0.
+Take a contour through the slice at the value 0.5. 
+Color the contour red. Use a white background.
+Rotate the view to look at the +x direction. 
+Save a screenshot of the result in the filename "ml-slice-iso/results/{agent_mode}/ml-slice-iso.png". 
+The rendered view and saved screenshot should be 1920 x 1080 pixels.
 Finally, save the ParaView state as "ml-slice-iso/results/{agent_mode}/ml-slice-iso.pvsm"

chatvis_bench/ml-slice-iso/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. Slice Generation: Is the y-z plane slice properly generated at x=0 position showing the correct cross-section of the volume?
-
-2. Contour on Slice: Are the contour lines at value 0.5 correctly extracted from the slice and properly displayed?
-
-3. Red Color Application: Is the contour visualization properly colored red as specified in the requirements?
-
+1. Slice Generation: Is the y-z plane slice properly generated at x=0 position showing the correct cross-section of the volume?
+
+2. Contour on Slice: Are the contour lines at value 0.5 correctly extracted from the slice and properly displayed?
+
+3. Red Color Application: Is the contour visualization properly colored red as specified in the requirements?
+
 4. View Direction: Is the visualization displayed from the correct +x direction view that provides clear visibility of the slice and contours?

chatvis_bench/points-surf-clip/task_description.txt

@@ -1,3 +1,8 @@
-Read in the file named "points-surf-clip/data/points-surf-clip.ex2", generate a 3D Delaunay triangulation of the dataset, and clip with a y-z plane at x=0, keeping the -x half of the data.
-Save a screenshot of the result as a wireframe, image size 1920 x 1080 pixels, in "points-surf-clip/results/{agent_mode}/points-surf-clip.png".
+I would like to use ParaView to visualize a dataset.
+Read in the file named "points-surf-clip/data/points-surf-clip.ex2".
+Generate an 3d Delaunay triangulation of the dataset.
+Clip the data with a y-z plane at x=0, keeping the -x half of the data and removing the +x half.
+Render the image as a wireframe.
+Save a screenshot of the result in the filename "points-surf-clip/results/{agent_mode}/points-surf-clip.png".
+The rendered view and saved screenshot should be 1920 x 1080 pixels. Use a white background color.
 Finally, save the ParaView state as "points-surf-clip/results/{agent_mode}/points-surf-clip.pvsm"

chatvis_bench/render-histogram/GS/render-histogram_gs.py

@@ -24,14 +24,14 @@ wavelet1Display.SetRepresentationType('Surface')
 ColorBy(wavelet1Display, ('POINTS', 'RTData'))
 
 # rescale color and/or opacity maps used to include current data range
-#wavelet1Display.RescaleTransferFunctionToDataRange(True)
+wavelet1Display.RescaleTransferFunctionToDataRange(True)
 
-# show color bar/color legend
+# hide color bar/color legend
 wavelet1Display.SetScalarBarVisibility(renderView1, True)
 
 # get color transfer function/color map for 'RTData'
 rTDataLUT = GetColorTransferFunction('RTData')
-#rTDataLUT.ApplyPreset('Cool to Warm', True)
+rTDataLUT.ApplyPreset('Cool to Warm', True)
 
 # get layout
 viewLayout1 = GetLayout()
@@ -61,5 +61,10 @@ histogram.LookupTable = rTDataLUT
 
 Render(histogramView1)
 
-# save screenshot
-SaveScreenshot('render-histogram/results/{agent_mode}/render-histogram.png', lineChartView1)
+agent_mode = 'pvpython'
+
+# save screenshot of the entire layout (both views)
+SaveScreenshot(f'render-histogram/results/{agent_mode}/render-histogram.png', viewLayout1)
+
+# save ParaView state
+SaveState(f'render-histogram/results/{agent_mode}/render-histogram.pvsm')

chatvis_bench/render-histogram/task_description.txt

@@ -1,9 +1,8 @@
-Create a wavelet object.
-Render the RTDATA data in the wavelet and show the color bar.
-[optional: Make sure the colors are rescaled to the data range]
-[optional: Use the color map called 'Cool to Warm']
+Create a wavelet object and render it as a surface colored by RTDATA with a visible color bar.
+Rescale the colors to the data range and use the 'Cool to Warm' color map.
 
-Next, split the view to the right and create a histogram from RTDATA.
-Use the same color map as before.
-Save a screenshot of the line chart in the file "render-histogram/results/{agent_mode}/render-histogram.png".
+Next, split the view horizontally to the right and create a histogram view from the wavelet RTDATA.
+Apply the same 'Cool to Warm' color map to the histogram.
+
+Save a screenshot of both views (wavelet rendering on the left and histogram on the right) in the file "render-histogram/results/{agent_mode}/render-histogram.png".
 Finally, save the ParaView state as "render-histogram/results/{agent_mode}/render-histogram.pvsm"

chatvis_bench/save-transparent/task_description.txt

@@ -1,4 +1,7 @@
-Create a cone object.
-Set the transparency of the cone to be 50%.
-Save a screenshot with a transparent background in "save-transparent/results/{agent_mode}/save-transparent.png".
+I would like to use ParaView to visualize a dataset.
+Create a wavelet object and show it.  Color the rendering by the variable ‘RTData’.
+Render the wavelet as a surface.  Hide the color bar.
+Next, set the layout size to be 300 pixels by 300 pixels.
+Next, move the camera with the following settings.  The camera position should be [30.273897726939246, 40.8733980301544, 43.48927935675712].  The camera view up should be [-0.3634544237682163, 0.7916848767068606, -0.49105594165731975]. The camera parallel scale should be 17.320508075688775.
+Save a screenshot to the file “save-transparent/results/{agent_mode}/save-transparent.png”, set the image resolution to 300x300, and set the background to transparent.
 Finally, save the ParaView state as "save-transparent/results/{agent_mode}/save-transparent.pvsm"

chatvis_bench/save-transparent/visualization_goals.txt

@@ -1,7 +1,3 @@
-1. Cone Object Creation: Is the cone object properly created and displayed in the scene?
+1. Object Creation: Is the wavelet object properly created and displayed in the scene? Looking similar to the GT image?
 
-2. Transparency Setting: Is the cone transparency correctly set to 50% showing partial see-through effect?
-
-3. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?
-
-4. Visual Quality: Does the transparent cone maintain good visual quality and edge definition?
+2. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?

chatvis_bench/shrink-sphere/task_description.txt

@@ -1,4 +1,9 @@
-Create a default sphere, hide it, and create a shrink filter from the sphere.
-Double the sphere's theta resolution while halving the shrink filter's shrink factor.
-Group the shrink filter and a wireframe of the sphere together, and save a screenshot of the result in "shrink-sphere/results/{agent_mode}/shrink-sphere.png", size 1920 x 1080 pixels with a white background.
-Finally, save the ParaView state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm"
+Create a default sphere and then hide it.
+Create a shrink filter from the sphere.
+Double the sphere's theta resolution.
+Divide the shrink filter's shrink factor in half.
+Extract a wireframe from the sphere.
+Group the shrink filter and wireframe together and show them.
+Save a screenshot of the result in the filename "shrink-sphere/results/{agent_mode}/shrink-sphere.png".
+The rendered view and saved screenshot should be 1920 x 1080 pixels and have a white background.
+Finally, save the ParaView state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm".

chatvis_bench/shrink-sphere/visualization_goals.txt

@@ -1,7 +1,7 @@
-1. Sphere Creation and Resolution: Is the sphere created with doubled theta resolution providing higher geometric detail and smoother curvature?
-
-2. Shrink Filter Application: Is the shrink filter properly applied with halved shrink factor creating visible separation between mesh elements?
-
-3. Dual Representation: Are both the wireframe sphere and shrink filter results simultaneously visible and properly grouped together?
-
+1. Sphere Creation and Resolution: Is the sphere created with doubled theta resolution providing higher geometric detail and smoother curvature?
+
+2. Shrink Filter Application: Is the shrink filter properly applied with halved shrink factor creating visible separation between mesh elements?
+
+3. Dual Representation: Are both the wireframe sphere and shrink filter results simultaneously visible and properly grouped together?
+
 4. Visual Quality: Does the visualization clearly show the contrast between the wireframe structure and the shrunken elements with appropriate white background?

chatvis_bench/stream-glyph/task_description.txt

@@ -1,4 +1,10 @@
-Read in the file named "stream-glyph/data/stream-glyph.ex2", and trace streamlines of the V variable seeded from a default point cloud.
-Render the streamlines with tubes, adding cone glyphs to the streamlines, and coloring the streamlines and glyphs by the Temp variable.
-Save a screenshot of a +x view of the result, size 1920 x 1080 pixels, in "stream-glyph/results/{agent_mode}/stream-glyph.png".
-Finally, save the ParaView state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm"
+I would like to use ParaView to visualize a dataset.
+Read in the file named "stream-glyph/data/stream-glyph.ex2".
+Trace streamlines of the V data array seeded from a default point cloud.
+Render the streamlines with tubes.
+Add cone glyphs to the streamlines.
+Color the streamlines and glyphs by the Temp data array.
+View the result in the +X direction.
+Save a screenshot of the result in the filename "stream-glyph/results/{agent_mode}/stream-glyph.png".
+The rendered view and saved screenshot should be 1920 x 1080 pixels.
+Finally, save the ParaView state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm".

chatvis_bench/subseries-of-time-series/visualization_goals.txt

@@ -1,7 +1,5 @@
 1. Data Loading and Block Selection: Are the specified element blocks properly loaded and the slice plane correctly applied?
 
-2. Time Series Export: Is the time series correctly saved as VTM files with the specified time step range and interval?
+2. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
 
-3. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
-
-4. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?
+3. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?

chatvis_bench/time-varying/GS/time-varying_gs.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64d949e424472e3435f99c214aa9e5e0f76577c7d8012bfe1ce23e6432715311
+size 77406

chatvis_bench/write-ply/task_description.txt

@@ -1,5 +1,9 @@
-Create a cube object.
-Export the cube to a PLY file named "write-ply/results/{agent_mode}/cube.ply".
-Load the PLY file back into ParaView.
-Save a screenshot to "write-ply/results/{agent_mode}/write-ply.png".
+I would like to use ParaView to visualize a dataset.
+Create a wavelet object. Change the view size to 400x400.
+Show the wavelet object and reset the camera to fit the data.
+Next, create a contour of wavelet object from the dataset "RTData".
+The contour should have isosurfaces at the following values: 97.222075, 157.09105, 216.96002500000003, and 276.829.
+Show the contour and color it with the same colormap that is used for coloring "RTData".
+Finally, save the contour in PLY format to the file "write-ply/results/{agent_mode}/PLYWriterData.ply".
+Save a screenshot to the file "write-ply/results/{agent_mode}/write-ply.png".
 Finally, save the ParaView state as "write-ply/results/{agent_mode}/write-ply.pvsm"

chatvis_bench/write-ply/visualization_goals.txt

@@ -1,7 +1,5 @@
 1. Cube Creation: Is the cube object properly created and displayed with correct geometry?
 
-2. PLY Export: Is the cube successfully exported to PLY format with proper mesh data preservation?
+2. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
 
-3. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
-
-4. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?
+3. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?

eval_cases/paraview/chatvis_bench_cases.yaml

@@ -19,18 +19,15 @@
 
           4. Legend and Readability: Is there a clear legend identifying each variable line with readable labels and proper visual organization?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - line-plot/GS/line-plot_gs.py
-      rs_file:
-        - line-plot/results/{agent_mode}/line-plot.py
-
 # 2. ml-dvr
 - vars:
     question: |
-          Read in the file named "ml-dvr/data/ml-dvr.vtk", and generate a volume rendering using the default transfer function.
-          Save a screenshot, size 1920 x 1080 pixels, of an isometric view of the visualization in "ml-dvr/results/{agent_mode}/ml-dvr.png".
+          I would like to use ParaView to visualize a dataset.
+          Read in the file named "ml-dvr/data/ml-dvr.vtk".
+          Generate a volume rendering using the default transfer function.
+          Rotate the view to an isometric direction.
+          Save a screenshot of the result in the filename "ml-dvr/results/{agent_mode}/ml-dvr.png".
+          The rendered view and saved screenshot should be 1920 x 1080 pixels.
           Finally, save the ParaView state as "ml-dvr/results/{agent_mode}/ml-dvr.pvsm"
 
   assert:
@@ -45,18 +42,11 @@
 
           4. Visual Clarity and Detail: Are the volume details clearly visible with proper lighting and shading that enhances depth perception?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - ml-dvr/GS/ml-dvr_gs.py
-      rs_file:
-        - ml-dvr/results/{agent_mode}/ml-dvr.py
-
 # 3. ml-iso
 - vars:
     question: |
           Read in the file named "ml-iso/data/ml-iso.vtk", and generate an isosurface of the variable var0 at value 0.5.
-          Save a screenshot of the result, size 1920 x 1080 pixels, in "ml-iso/results/{agent_mode}/ml-iso.png".
+          Use a white background color. Save a screenshot of the result, size 1920 x 1080 pixels, in "ml-iso/results/{agent_mode}/ml-iso.png".
           Finally, save the ParaView state as "ml-iso/results/{agent_mode}/ml-iso.pvsm"
 
   assert:
@@ -71,18 +61,17 @@
 
           4. Visual Presentation: Is the isosurface clearly visible with good contrast and coloring that enhances the understanding of the data structure?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - ml-iso/GS/ml-iso_gs.py
-      rs_file:
-        - ml-iso/results/{agent_mode}/ml-iso.py
-
 # 4. ml-slice-iso
 - vars:
     question: |
-          Read in the file named "ml-slice-iso/data/ml-slice-iso.vtk", slice the volume with a y-z plane at x=0, and take a contour, colored red, through the slice at the value 0.5.
-          Save a screenshot of a +x direction view, size 1920 x 1080 pixels, of the result in "ml-slice-iso/results/{agent_mode}/ml-slice-iso.png".
+          Please generate a ParaView Python script for the following operations.
+          Read in the file named "ml-slice-iso/data/ml-slice-iso.vtk".
+          Slice the volume in a plane parallel to the y-z plane at x=0.
+          Take a contour through the slice at the value 0.5.
+          Color the contour red. Use a white background.
+          Rotate the view to look at the +x direction.
+          Save a screenshot of the result in the filename "ml-slice-iso/results/{agent_mode}/ml-slice-iso.png".
+          The rendered view and saved screenshot should be 1920 x 1080 pixels.
           Finally, save the ParaView state as "ml-slice-iso/results/{agent_mode}/ml-slice-iso.pvsm"
 
   assert:
@@ -97,18 +86,16 @@
 
           4. View Direction: Is the visualization displayed from the correct +x direction view that provides clear visibility of the slice and contours?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - ml-slice-iso/GS/ml-slice-iso_gs.py
-      rs_file:
-        - ml-slice-iso/results/{agent_mode}/ml-slice-iso.py
-
 # 5. points-surf-clip
 - vars:
     question: |
-          Read in the file named "points-surf-clip/data/points-surf-clip.ex2", generate a 3D Delaunay triangulation of the dataset, and clip with a y-z plane at x=0, keeping the -x half of the data.
-          Save a screenshot of the result as a wireframe, image size 1920 x 1080 pixels, in "points-surf-clip/results/{agent_mode}/points-surf-clip.png".
+          I would like to use ParaView to visualize a dataset.
+          Read in the file named "points-surf-clip/data/points-surf-clip.ex2".
+          Generate an 3d Delaunay triangulation of the dataset.
+          Clip the data with a y-z plane at x=0, keeping the -x half of the data and removing the +x half.
+          Render the image as a wireframe.
+          Save a screenshot of the result in the filename "points-surf-clip/results/{agent_mode}/points-surf-clip.png".
+          The rendered view and saved screenshot should be 1920 x 1080 pixels. Use a white background color.
           Finally, save the ParaView state as "points-surf-clip/results/{agent_mode}/points-surf-clip.pvsm"
 
   assert:
@@ -123,20 +110,18 @@
 
           4. Geometric Integrity: Does the clipped wireframe maintain proper connectivity and show the expected geometric features without artifacts?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - points-surf-clip/GS/points-surf-clip_gs.py
-      rs_file:
-        - points-surf-clip/results/{agent_mode}/points-surf-clip.py
-
 # 6. shrink-sphere
 - vars:
     question: |
-          Create a default sphere, hide it, and create a shrink filter from the sphere.
-          Double the sphere's theta resolution while halving the shrink filter's shrink factor.
-          Group the shrink filter and a wireframe of the sphere together, and save a screenshot of the result in "shrink-sphere/results/{agent_mode}/shrink-sphere.png", size 1920 x 1080 pixels with a white background.
-          Finally, save the ParaView state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm"
+          Create a default sphere and then hide it.
+          Create a shrink filter from the sphere.
+          Double the sphere's theta resolution.
+          Divide the shrink filter's shrink factor in half.
+          Extract a wireframe from the sphere.
+          Group the shrink filter and wireframe together and show them.
+          Save a screenshot of the result in the filename "shrink-sphere/results/{agent_mode}/shrink-sphere.png".
+          The rendered view and saved screenshot should be 1920 x 1080 pixels and have a white background.
+          Finally, save the ParaView state as "shrink-sphere/results/{agent_mode}/shrink-sphere.pvsm".
 
   assert:
     - type: llm-rubric
@@ -150,20 +135,19 @@
 
           4. Visual Quality: Does the visualization clearly show the contrast between the wireframe structure and the shrunken elements with appropriate white background?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - shrink-sphere/GS/shrink-sphere_gs.py
-      rs_file:
-        - shrink-sphere/results/{agent_mode}/shrink-sphere.py
-
 # 7. stream-glyph
 - vars:
     question: |
-          Read in the file named "stream-glyph/data/stream-glyph.ex2", and trace streamlines of the V variable seeded from a default point cloud.
-          Render the streamlines with tubes, adding cone glyphs to the streamlines, and coloring the streamlines and glyphs by the Temp variable.
-          Save a screenshot of a +x view of the result, size 1920 x 1080 pixels, in "stream-glyph/results/{agent_mode}/stream-glyph.png".
-          Finally, save the ParaView state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm"
+          I would like to use ParaView to visualize a dataset.
+          Read in the file named "stream-glyph/data/stream-glyph.ex2".
+          Trace streamlines of the V data array seeded from a default point cloud.
+          Render the streamlines with tubes.
+          Add cone glyphs to the streamlines.
+          Color the streamlines and glyphs by the Temp data array.
+          View the result in the +X direction.
+          Save a screenshot of the result in the filename "stream-glyph/results/{agent_mode}/stream-glyph.png".
+          The rendered view and saved screenshot should be 1920 x 1080 pixels.
+          Finally, save the ParaView state as "stream-glyph/results/{agent_mode}/stream-glyph.pvsm".
 
   assert:
     - type: llm-rubric
@@ -177,13 +161,6 @@
 
           4. View Configuration: Is the visualization displayed from the correct +x view direction providing clear visibility of the flow patterns and structures?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - stream-glyph/GS/stream-glyph_gs.py
-      rs_file:
-        - stream-glyph/results/{agent_mode}/stream-glyph.py
-
 # 8. time-varying
 - vars:
     question: |
@@ -207,13 +184,6 @@
 
           4. View Direction and Layout: Is the +y direction view properly set and are both views arranged side-by-side in the correct layout configuration?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - time-varying/GS/time-varying_gs.py
-      rs_file:
-        - time-varying/results/{agent_mode}/time-varying.py
-
 # 9. chart-opacity
 - vars:
     question: |
@@ -226,31 +196,32 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Chart Generation: Is the plot over line chart properly created from the wavelet data showing all three specified variables?
+          1. Chart Generation: Is the plot over line chart properly created from the wavelet data?
 
-          2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted and distinguishable in the chart?
+          2. Variable Display: Are arc_length, Points_Z, and RTData variables all correctly plotted, showing all three specified variables and distinguishable in the chart?
 
           3. Opacity Settings: Is the arc_length variable displayed with full opacity (1.0) while Points_Z and RTData show reduced opacity (0.3)?
 
           4. Chart Clarity: Does the chart provide clear visualization of the data trends with appropriate axis scaling and readable formatting?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - chart-opacity/GS/chart-opacity_gs.py
-      rs_file:
-        - chart-opacity/results/{agent_mode}/chart-opacity.py
-
 # 10. color-blocks
 - vars:
     question: |
+          I would like to use ParaView to visualize a dataset.
+          Set the background to a blue-gray palette.
           Read the file "color-blocks/data/color-blocks.ex2".
+          This is a multiblock dataset.
           Color the dataset by the vtkBlockColors field.
-          Retrieve the color map, opacity transfer function, and 2D transfer function for vtkBlockColors.
-          Set block coloring for the block at /IOSS/element_blocks/block_2 using the x component of the ACCL variable.
+          Retrieve the color map for vtkBlockColors.
+          Retrieve the opacity transfer function for vtkBlockColors.
+          Retrieve the 2D transfer function for vtkBlockColors.
+          Set block coloring for the block at /IOSS/element_blocks/block_2 using the variable ACCL on the x component of the points.
           Rescale the block's color and opacity maps to match the current data range of block_2.
-          For the ACCL variable of block_2, retrieve the color transfer function, enable the color bar, and apply cool to warm coloring.
-          View the entire dataset in the -y direction, and save a screenshot with blue-gray background in "color-blocks/results/{agent_mode}/color-blocks.png".
+          Retrieve the color transfer function for the ACCL variable of block_2.
+          Enable the color bar for block_2.
+          Apply a cool to warm color preset to the color map for block_2.
+          Set the camera to look down the -y direction and to see the entire dataset.
+          Save a screenshot of the visualization in the file "color-blocks/results/{agent_mode}/color-blocks.png".
           Finally, save the ParaView state as "color-blocks/results/{agent_mode}/color-blocks.pvsm"
 
   assert:
@@ -265,13 +236,6 @@
 
           4. View Configuration: Is the dataset displayed from the -y direction with blue-gray background and visible color bar legend?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - color-blocks/GS/color-blocks_gs.py
-      rs_file:
-        - color-blocks/results/{agent_mode}/color-blocks.py
-
 # 11. color-data
 - vars:
     question: |
@@ -285,20 +249,11 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Calculator Function: Is the calculator properly implemented with the specified vector function combining RTData and coordinate components?
-
-          2. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
+          1. Color Transfer Function: Is the color transfer function correctly applied with cool to warm color mapping scaled to the data range?
 
-          3. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
+          2. Surface Coloring: Is the surface representation properly colored by the x coordinate of the calculated result?
 
-          4. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?
-
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - color-data/GS/color-data_gs.py
-      rs_file:
-        - color-data/results/{agent_mode}/color-data.py
+          3. Color Bar Display: Is the color bar/legend visible and properly displaying the color mapping scale and values?
 
 # 12. export-gltf
 - vars:
@@ -311,7 +266,7 @@
           Next load the file "export-gltf/results/{agent_mode}/ExportedGLTF.gltf" and display it as a surface.
           Color this object by TEXCOORD_0.
           Scale the color map to the data, and don't display the color bar or the orientation axes.
-          Use the 'Cool to Warm' colormap.
+          Use the 'Cool to Warm' colormap. Set the background color to white.
 
           Save a screenshot to the file "export-gltf/results/{agent_mode}/export-gltf.png".
           Finally, save the ParaView state as "export-gltf/results/{agent_mode}/export-gltf.pvsm"
@@ -328,13 +283,6 @@
 
           4. Clean Presentation: Are the color bar and orientation axes properly hidden for a clean visualization appearance?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - export-gltf/GS/export-gltf_gs.py
-      rs_file:
-        - export-gltf/results/{agent_mode}/export-gltf.py
-
 # 13. import-gltf
 - vars:
     question: |
@@ -353,28 +301,20 @@
 
           2. Node Selection: Are all three specified nodes (Axle, Torus002, InnerRing) correctly imported and visible?
 
-          3. Camera Positioning: Is the camera positioned in the positive Y direction with appropriate zoom to fit all imported geometry?
+          3. Camera Positioning: Is the camera positioned in the positive Y direction with appropriate zoom to fit all imported geometry? Carefully compare the camera position of GT and result images.
 
           4. Layout Configuration: Is the view properly sized to 300x300 pixels with correct rendering and background palette?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - import-gltf/GS/import-gltf_gs.py
-      rs_file:
-        - import-gltf/results/{agent_mode}/import-gltf.py
-
 # 14. render-histogram
 - vars:
     question: |
-          Create a wavelet object.
-          Render the RTDATA data in the wavelet and show the color bar.
-          [optional: Make sure the colors are rescaled to the data range]
-          [optional: Use the color map called 'Cool to Warm']
+          Create a wavelet object and render it as a surface colored by RTDATA with a visible color bar.
+          Rescale the colors to the data range and use the 'Cool to Warm' color map.
+
+          Next, split the view horizontally to the right and create a histogram view from the wavelet RTDATA.
+          Apply the same 'Cool to Warm' color map to the histogram.
 
-          Next, split the view to the right and create a histogram from RTDATA.
-          Use the same color map as before.
-          Save a screenshot of the line chart in the file "render-histogram/results/{agent_mode}/render-histogram.png".
+          Save a screenshot of both views (wavelet rendering on the left and histogram on the right) in the file "render-histogram/results/{agent_mode}/render-histogram.png".
           Finally, save the ParaView state as "render-histogram/results/{agent_mode}/render-histogram.pvsm"
 
   assert:
@@ -389,13 +329,6 @@
 
           4. Color Map Consistency: Are both the wavelet visualization and histogram using the same Cool to Warm color map?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - render-histogram/GS/render-histogram_gs.py
-      rs_file:
-        - render-histogram/results/{agent_mode}/render-histogram.py
-
 # 15. reset-camera-direction
 - vars:
     question: |
@@ -415,39 +348,24 @@
 
           4. View Quality: Does the visualization provide a clear view of the wavelet structure from the specified camera angle?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - reset-camera-direction/GS/reset-camera-direction_gs.py
-      rs_file:
-        - reset-camera-direction/results/{agent_mode}/reset-camera-direction.py
-
 # 16. save-transparent
 - vars:
     question: |
-          Create a cone object.
-          Set the transparency of the cone to be 50%.
-          Save a screenshot with a transparent background in "save-transparent/results/{agent_mode}/save-transparent.png".
+          I would like to use ParaView to visualize a dataset.
+          Create a wavelet object and show it.  Color the rendering by the variable ‘RTData’.
+          Render the wavelet as a surface.  Hide the color bar.
+          Next, set the layout size to be 300 pixels by 300 pixels.
+          Next, move the camera with the following settings.  The camera position should be [30.273897726939246, 40.8733980301544, 43.48927935675712].  The camera view up should be [-0.3634544237682163, 0.7916848767068606, -0.49105594165731975]. The camera parallel scale should be 17.320508075688775.
+          Save a screenshot to the file “save-transparent/results/{agent_mode}/save-transparent.png”, set the image resolution to 300x300, and set the background to transparent.
           Finally, save the ParaView state as "save-transparent/results/{agent_mode}/save-transparent.pvsm"
 
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Cone Object Creation: Is the cone object properly created and displayed in the scene?
-
-          2. Transparency Setting: Is the cone transparency correctly set to 50% showing partial see-through effect?
-
-          3. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?
-
-          4. Visual Quality: Does the transparent cone maintain good visual quality and edge definition?
+          1. Object Creation: Is the wavelet object properly created and displayed in the scene? Looking similar to the GT image?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - save-transparent/GS/save-transparent_gs.py
-      rs_file:
-        - save-transparent/results/{agent_mode}/save-transparent.py
+          2. Transparent Background: Is the screenshot saved with a properly transparent background instead of solid color?
 
 # 17. subseries-of-time-series
 - vars:
@@ -466,26 +384,21 @@
       value: |
           1. Data Loading and Block Selection: Are the specified element blocks properly loaded and the slice plane correctly applied?
 
-          2. Time Series Export: Is the time series correctly saved as VTM files with the specified time step range and interval?
+          2. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
 
-          3. Multi-block Loading: Are the exported VTM files successfully loaded back as a multi-block dataset?
-
-          4. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?
-
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - subseries-of-time-series/GS/subseries-of-time-series_gs.py
-      rs_file:
-        - subseries-of-time-series/results/{agent_mode}/subseries-of-time-series.py
+          3. Final Visualization: Is the multi-block dataset properly displayed showing the sliced geometry from the time series?
 
 # 18. write-ply
 - vars:
     question: |
-          Create a cube object.
-          Export the cube to a PLY file named "write-ply/results/{agent_mode}/cube.ply".
-          Load the PLY file back into ParaView.
-          Save a screenshot to "write-ply/results/{agent_mode}/write-ply.png".
+          I would like to use ParaView to visualize a dataset.
+          Create a wavelet object. Change the view size to 400x400.
+          Show the wavelet object and reset the camera to fit the data.
+          Next, create a contour of wavelet object from the dataset "RTData".
+          The contour should have isosurfaces at the following values: 97.222075, 157.09105, 216.96002500000003, and 276.829.
+          Show the contour and color it with the same colormap that is used for coloring "RTData".
+          Finally, save the contour in PLY format to the file "write-ply/results/{agent_mode}/PLYWriterData.ply".
+          Save a screenshot to the file "write-ply/results/{agent_mode}/write-ply.png".
           Finally, save the ParaView state as "write-ply/results/{agent_mode}/write-ply.pvsm"
 
   assert:
@@ -494,27 +407,24 @@
       value: |
           1. Cube Creation: Is the cube object properly created and displayed with correct geometry?
 
-          2. PLY Export: Is the cube successfully exported to PLY format with proper mesh data preservation?
-
-          3. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
+          2. PLY Import: Is the exported PLY file correctly loaded back into ParaView maintaining geometric fidelity?
 
-          4. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?
-
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - write-ply/GS/write-ply_gs.py
-      rs_file:
-        - write-ply/results/{agent_mode}/write-ply.py
+          3. Visualization Quality: Does the imported cube display properly with correct surface representation and rendering?
 
 # 19. climate
 - vars:
     question: |
+          I would like to use ParaView to visualize a dataset of ocean currents.
           Read in the file named "climate/data/climate.vtp".
-          Apply a calculator filter to convert velocity from geospatial to lat-long coordinates.
-          Render the computed values using a tube filter with 0.05 radius, colored by velocity magnitude, and lit with maximum shininess and include normals for lighting.
-          Add cone glyphs to show the direction of the velocity, using 10 polygons, radius 0.15, height 0.5, and scaling factor 0.5.
-          View the result in the -z direction scaled so that the tubes occupy most of the image.
+          Apply a calculator filter to compute the following function:
+          (-velocity_X*sin(coordsX*0.0174533) + velocity_Y*cos(coordsX*0.0174533)) * iHat + (-velocity_X * sin(coordsY*0.0174533) * cos(coordsX*0.0174533) - velocity_Y * sin(coordsY*0.0174533) * sin(coordsX*0.0174533) + velocity_Z * cos(coordsY*0.0174533)) * jHat + 0*kHat
+          Render the computed values using a tube filter with 0.05 as the tube radius.
+          Color the tubes by the magnitude of the velocity.
+          Light the tubes with the maximum shininess and include normals in the lighting.
+          Add cone glyphs to show the direction of the velocity.
+          The glyphs are composed of 10 polygons, having a radius 0 0.15, a height of 0.5, and a scaling factor of 0.5.
+          View the result in the -z direction.
+          Adjust the view so that the tubes occupy the 90% of the image.
           Save a screenshot of the result, 2294 x 1440 pixels, white background, in the filename "climate/results/{agent_mode}/climate.png".
           Finally, save the ParaView state as "climate/results/{agent_mode}/climate.pvsm"
 
@@ -522,20 +432,11 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Velocity Conversion: Is the calculator filter properly applied to convert velocity from geospatial to lat-long coordinates?
-
-          2. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
-
-          3. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
+          1. Tube Visualization: Are the tubes rendered with correct radius (0.05), colored by velocity magnitude, and proper lighting with maximum shininess?
 
-          4. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?
+          2. Cone Glyph Direction: Are the cone glyphs properly configured with specified parameters and showing velocity direction accurately?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - climate/GS/climate_gs.py
-      rs_file:
-        - climate/results/{agent_mode}/climate.py
+          3. View Configuration: Is the visualization displayed from -z direction with appropriate scaling and white background as specified?
 
 # 20. materials
 - vars:
@@ -562,9 +463,3 @@
 
           4. Camera and Layout: Is the camera positioned correctly in (-1, 0, -1) direction with appropriate fitting and legends visible?
 
-    - type: code-similarity
-      subtype: code
-      gs_file:
-        - materials/GS/materials_gs.py
-      rs_file:
-        - materials/results/{agent_mode}/materials.py

eval_cases/paraview/what_obj_cases.yaml

@@ -16,23 +16,23 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          The assistant should identify the data shows an aneurism or vascular structure
+          The visualization image shows an aneurism or vascular structure with a reasonable transfer function, and the object is shown clearly
 
-# # 2. Backpack Dataset
-# - vars:
-#     question: |
-#         Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/backpack/data/backpack_512x512x373_uint16.raw".
-#         Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/backpack/results/{agent_mode}/backpack.pvsm"
-#         Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/backpack/results/{agent_mode}/answers.txt"
-#   assert:
-#     - type: llm-rubric
-#       subtype: text
-#       value: |
-#           The assistant should identify that the data shows a backpack
-#     - type: llm-rubric
-#       subtype: vision
-#       value: |
-#           The assistant should identify that the data shows a backpack
+# 2. Backpack Dataset
+- vars:
+    question: |
+        Clear the ParaView pipeline and load the data file "../SciVisAgentBench-tasks/sci_volume_data/backpack/data/backpack_512x512x373_uint16.raw".
+        Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "../SciVisAgentBench-tasks/sci_volume_data/backpack/results/{agent_mode}/backpack.pvsm"
+        Provide a textual report identifying what you observe and save it to "../SciVisAgentBench-tasks/sci_volume_data/backpack/results/{agent_mode}/answers.txt"
+  assert:
+    - type: llm-rubric
+      subtype: text
+      value: |
+          The assistant should identify that the data shows a backpack
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          The visualization image shows a backpack with a reasonable transfer function, and the object is shown clearly
 
 # 3. Blunt Fin Dataset
 - vars:
@@ -48,7 +48,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          The assistant should identify a fin or aerodynamic strcuture or simulation result in the data
+          The visualization image shows a fin or aerodynamic strcuture or simulation result in the data with a reasonable transfer function, and the object is shown clearly
 
 # 4. Bonsai Dataset
 - vars:
@@ -64,7 +64,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a bonsai tree or botanical structure in the data
+          The visualization image shows a bonsai tree or botanical structure in the data with a reasonable transfer function, and the object is shown clearly
 
 # 5. Boston Teapot Dataset
 - vars:
@@ -80,7 +80,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a teapot in the visualization
+          The visualization image shows a teapot in the visualization with a reasonable transfer function, and the object is shown clearly
 
 # 6. Bunny Dataset
 - vars:
@@ -96,7 +96,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a bunny or rabbit in the 3D scanned data
+          The visualization image shows a bunny or rabbit in the 3D scanned data with a reasonable transfer function, and the object is shown clearly
 
 # 7. Carp Dataset
 - vars:
@@ -112,7 +112,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a carp or fish anatomy
+          The visualization image shows a carp or fish anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 8. CSAFE Heptane Dataset
 - vars:
@@ -128,7 +128,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize combustion or heptane simulation data
+          The visualization image shows combustion or heptane simulation data with a reasonable transfer function, and the object is shown clearly
 
 # 9. Duct Dataset
 - vars:
@@ -144,7 +144,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify flow patterns in a duct geometry
+          The visualization image shows flow patterns in a duct geometry with a reasonable transfer function, and the object is shown clearly
 
 # 10. Engine Dataset
 - vars:
@@ -160,7 +160,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify an engine or mechanical components
+          The visualization image shows an engine or mechanical components with a reasonable transfer function, and the object is shown clearly
 
 # 11. Foot Dataset
 - vars:
@@ -176,7 +176,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a foot with bone and tissue structures
+          The visualization image shows a foot with bone and tissue structures with a reasonable transfer function, and the object is shown clearly
 
 # 12. Frog Dataset
 - vars:
@@ -192,7 +192,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a frog specimen with internal anatomy
+          The visualization image shows a frog specimen with internal anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 13. Fuel Dataset
 - vars:
@@ -208,7 +208,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify fuel combustion or related simulation
+          The visualization image shows fuel combustion or related simulation with a reasonable transfer function, and the object is shown clearly
 
 # 14. Hydrogen Atom Dataset
 - vars:
@@ -224,7 +224,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize hydrogen atom orbital or probability distribution
+          The visualization image shows hydrogen atom orbital or probability distribution with a reasonable transfer function, and the object is shown clearly
 
 # 15. Lobster Dataset
 - vars:
@@ -240,7 +240,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a lobster or crustacean anatomy
+          The visualization image shows a lobster or crustacean anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 16. Marschner-Lobb Dataset
 - vars:
@@ -256,7 +256,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize Marschner-Lobb synthetic test pattern
+          The visualization image shows Marschner-Lobb synthetic test pattern with a reasonable transfer function, and the object is shown clearly
 
 # 17. MRI Ventricles Dataset
 - vars:
@@ -272,7 +272,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify brain ventricles or ventricular structures
+          The visualization image shows brain ventricles or ventricular structures with a reasonable transfer function, and the object is shown clearly
 
 # 18. MRI Woman Dataset
 - vars:
@@ -288,7 +288,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify human anatomical structures from MRI scan
+          The visualization image shows human anatomical structures from MRI scan with a reasonable transfer function, and the object is shown clearly
 
 # 19. MRT Angio Dataset
 - vars:
@@ -304,7 +304,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify angiography or vascular structures
+          The visualization image shows angiography or vascular structures with a reasonable transfer function, and the object is shown clearly
 
 # 20. Neghip Dataset
 - vars:
@@ -320,7 +320,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should visualize and describe molecule structure
+          The visualization image shows molecule structure with a reasonable transfer function, and the object is shown clearly
 
 # 21. Neocortical Layer 1 Axons Dataset
 - vars:
@@ -336,7 +336,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify neural axons or neocortical network structures
+          The visualization image shows neural axons or neocortical network structures with a reasonable transfer function, and the object is shown clearly
 
 # 22. Nucleon Dataset
 - vars:
@@ -352,7 +352,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should visualize nucleon or particle physics data
+          Should visualize nucleon or particle physics data with a reasonable transfer function, and the object is shown clearly
 
 # 23. Pancreas Dataset
 - vars:
@@ -368,7 +368,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify pancreas or pancreatic anatomy
+          The visualization image shows pancreas or pancreatic anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 24. Shockwave Dataset
 - vars:
@@ -384,7 +384,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify shockwave or wave propagation patterns
+          The visualization image shows shockwave or wave propagation patterns with a reasonable transfer function, and the object is shown clearly
 
 # 25. Silicium Dataset
 - vars:
@@ -400,7 +400,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify silicon crystal or material structure
+          The visualization image shows silicon crystal or material structure with a reasonable transfer function, and the object is shown clearly
 
 # 26. Skull Dataset
 - vars:
@@ -432,7 +432,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a statue leg or sculptural form
+          The visualization image shows a statue leg or sculptural form with a reasonable transfer function, and the object is shown clearly
 
 # 28. Stent Dataset
 - vars:
@@ -459,7 +459,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify supernova or astrophysical explosion simulation
+          The visualization image shows supernova or astrophysical explosion simulation with a reasonable transfer function, and the object is shown clearly
 
 # 30. TACC Turbulence Dataset
 - vars:
@@ -475,7 +475,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify turbulence or vortex flow structures
+          The visualization image shows turbulence or vortex flow structures with a reasonable transfer function, and the object is shown clearly
 
 # 31. Tooth Dataset
 - vars:
@@ -491,7 +491,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify tooth or dental anatomy
+          The visualization image shows tooth or dental anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 32. Tornado Dataset
 - vars:
@@ -507,7 +507,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify tornado or vortex flow pattern
+          The visualization image shows tornado or vortex flow pattern with a reasonable transfer function, and the object is shown clearly
 
 # 33. Visible Male Dataset
 - vars:
@@ -523,4 +523,4 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify human anatomical structures 
+          The visualization image shows human anatomical structures with a reasonable transfer function, and the object is shown clearly

eval_cases/paraview/what_obj_cases_anonymized.yaml

@@ -16,23 +16,23 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          The assistant should identify the data shows an aneurism or vascular structure
+          The visualization image shows an aneurism or vascular structure with a reasonable transfer function, and the object is shown clearly
 
-# # 2. Backpack Dataset
-# - vars:
-#     question: |
-#         Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_002/data/data_002_512x512x373_uint16.raw".
-#         Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "anonymized_datasets/dataset_002/results/{agent_mode}/dataset_002.pvsm"
-#         Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_002/results/{agent_mode}/answers.txt"
-#   assert:
-#     - type: llm-rubric
-#       subtype: text
-#       value: |
-#           The assistant should identify that the data shows a backpack
-#     - type: llm-rubric
-#       subtype: vision
-#       value: |
-#           The assistant should identify that the data shows a backpack
+# 2. Backpack Dataset
+- vars:
+    question: |
+        Clear the ParaView pipeline and load the data file "anonymized_datasets/dataset_002/data/data_002_512x512x373_uint16.raw".
+        Use visualization tools to determine what object is contained in this dataset. Save the paraview state as "anonymized_datasets/dataset_002/results/{agent_mode}/dataset_002.pvsm"
+        Provide a textual report identifying what you observe and save it to "anonymized_datasets/dataset_002/results/{agent_mode}/answers.txt"
+  assert:
+    - type: llm-rubric
+      subtype: text
+      value: |
+          The assistant should identify that the data shows a backpack
+    - type: llm-rubric
+      subtype: vision
+      value: |
+          The visualization image shows a backpack with a reasonable transfer function, and the object is shown clearly
 
 # 3. Blunt Fin Dataset
 - vars:
@@ -48,7 +48,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          The assistant should identify a fin or aerodynamic strcuture or simulation result in the data
+          The visualization image shows a fin or aerodynamic strcuture or simulation result in the data with a reasonable transfer function, and the object is shown clearly
 
 # 4. Bonsai Dataset
 - vars:
@@ -64,7 +64,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a bonsai tree or botanical structure in the data
+          The visualization image shows a bonsai tree or botanical structure in the data with a reasonable transfer function, and the object is shown clearly
 
 # 5. Boston Teapot Dataset
 - vars:
@@ -80,7 +80,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a teapot in the visualization
+          The visualization image shows a teapot in the visualization with a reasonable transfer function, and the object is shown clearly
 
 # 6. Bunny Dataset
 - vars:
@@ -96,7 +96,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a bunny or rabbit in the 3D scanned data
+          The visualization image shows a bunny or rabbit in the 3D scanned data with a reasonable transfer function, and the object is shown clearly
 
 # 7. Carp Dataset
 - vars:
@@ -112,7 +112,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a carp or fish anatomy
+          The visualization image shows a carp or fish anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 8. CSAFE Heptane Dataset
 - vars:
@@ -128,7 +128,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize combustion or heptane simulation data
+          The visualization image shows combustion or heptane simulation data with a reasonable transfer function, and the object is shown clearly
 
 # 9. Duct Dataset
 - vars:
@@ -144,7 +144,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify flow patterns in a duct geometry
+          The visualization image shows flow patterns in a duct geometry with a reasonable transfer function, and the object is shown clearly
 
 # 10. Engine Dataset
 - vars:
@@ -160,7 +160,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify an engine or mechanical components
+          The visualization image shows an engine or mechanical components with a reasonable transfer function, and the object is shown clearly
 
 # 11. Foot Dataset
 - vars:
@@ -176,7 +176,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a foot with bone and tissue structures
+          The visualization image shows a foot with bone and tissue structures with a reasonable transfer function, and the object is shown clearly
 
 # 12. Frog Dataset
 - vars:
@@ -192,7 +192,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a frog specimen with internal anatomy
+          The visualization image shows a frog specimen with internal anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 13. Fuel Dataset
 - vars:
@@ -208,7 +208,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify fuel combustion or related simulation
+          The visualization image shows fuel combustion or related simulation with a reasonable transfer function, and the object is shown clearly
 
 # 14. Hydrogen Atom Dataset
 - vars:
@@ -224,7 +224,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize hydrogen atom orbital or probability distribution
+          The visualization image shows hydrogen atom orbital or probability distribution with a reasonable transfer function, and the object is shown clearly
 
 # 15. Lobster Dataset
 - vars:
@@ -240,7 +240,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a lobster or crustacean anatomy
+          The visualization image shows a lobster or crustacean anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 16. Marschner-Lobb Dataset
 - vars:
@@ -256,7 +256,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should recognize Marschner-Lobb synthetic test pattern
+          The visualization image shows Marschner-Lobb synthetic test pattern with a reasonable transfer function, and the object is shown clearly
 
 # 17. MRI Ventricles Dataset
 - vars:
@@ -272,7 +272,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify brain ventricles or ventricular structures
+          The visualization image shows brain ventricles or ventricular structures with a reasonable transfer function, and the object is shown clearly
 
 # 18. MRI Woman Dataset
 - vars:
@@ -288,7 +288,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify human anatomical structures from MRI scan
+          The visualization image shows human anatomical structures from MRI scan with a reasonable transfer function, and the object is shown clearly
 
 # 19. MRT Angio Dataset
 - vars:
@@ -304,7 +304,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify angiography or vascular structures
+          The visualization image shows angiography or vascular structures with a reasonable transfer function, and the object is shown clearly
 
 # 20. Neghip Dataset
 - vars:
@@ -320,7 +320,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should visualize and describe molecule structure
+          The visualization image shows molecule structure with a reasonable transfer function, and the object is shown clearly
 
 # 21. Neocortical Layer 1 Axons Dataset
 - vars:
@@ -336,7 +336,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify neural axons or neocortical network structures
+          The visualization image shows neural axons or neocortical network structures with a reasonable transfer function, and the object is shown clearly
 
 # 22. Nucleon Dataset
 - vars:
@@ -352,7 +352,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should visualize nucleon or particle physics data
+          Should visualize nucleon or particle physics data with a reasonable transfer function, and the object is shown clearly
 
 # 23. Pancreas Dataset
 - vars:
@@ -368,7 +368,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify pancreas or pancreatic anatomy
+          The visualization image shows pancreas or pancreatic anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 24. Shockwave Dataset
 - vars:
@@ -384,7 +384,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify shockwave or wave propagation patterns
+          The visualization image shows shockwave or wave propagation patterns with a reasonable transfer function, and the object is shown clearly
 
 # 25. Silicium Dataset
 - vars:
@@ -400,7 +400,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify silicon crystal or material structure
+          The visualization image shows silicon crystal or material structure with a reasonable transfer function, and the object is shown clearly
 
 # 26. Skull Dataset
 - vars:
@@ -432,7 +432,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify a statue leg or sculptural form
+          The visualization image shows a statue leg or sculptural form with a reasonable transfer function, and the object is shown clearly
 
 # 28. Stent Dataset
 - vars:
@@ -459,7 +459,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify supernova or astrophysical explosion simulation
+          The visualization image shows supernova or astrophysical explosion simulation with a reasonable transfer function, and the object is shown clearly
 
 # 30. TACC Turbulence Dataset
 - vars:
@@ -475,7 +475,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify turbulence or vortex flow structures
+          The visualization image shows turbulence or vortex flow structures with a reasonable transfer function, and the object is shown clearly
 
 # 31. Tooth Dataset
 - vars:
@@ -491,7 +491,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify tooth or dental anatomy
+          The visualization image shows tooth or dental anatomy with a reasonable transfer function, and the object is shown clearly
 
 # 32. Tornado Dataset
 - vars:
@@ -507,7 +507,7 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify tornado or vortex flow pattern
+          The visualization image shows tornado or vortex flow pattern with a reasonable transfer function, and the object is shown clearly
 
 # 33. Visible Male Dataset
 - vars:
@@ -523,4 +523,4 @@
     - type: llm-rubric
       subtype: vision
       value: |
-          Should identify human anatomical structures 
+          The visualization image shows human anatomical structures with a reasonable transfer function, and the object is shown clearly