feat(letsplot): implement stereonet-equal-area

[github-actions]

Implementation: stereonet-equal-area - letsplot

Implements the letsplot version of stereonet-equal-area.

File: plots/stereonet-equal-area/implementations/letsplot.py

Parent Issue: #4576

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a Schmidt equal-area (lower-hemisphere) stereonet projection rendered as a square image. A black primitive circle forms the boundary with tick marks every 10° around the perimeter (longer ticks at 30° intervals). Bold cardinal direction labels (N, E, S, W) are positioned outside the circle. Dashed light-gray reference cross lines mark the N-S and E-W axes. Three feature types are color-coded: Bedding (steel blue #306998), Joint (pink #CC79A7), and Fault (amber/orange #E69F00). Great circles are drawn as thin, semi-transparent arcs for each measurement plane. Poles to planes appear as colored dots clustered in distinct regions — Bedding poles cluster in the center-south area, Joint poles spread along the eastern edge, and Fault poles cluster in the southwest. Gray concentric contour segments (Kamb-style density) are visible around the main pole clusters. The legend is positioned to the right showing "Feature Type" with three entries. The title reads "stereonet-equal-area · letsplot · pyplots.ai" at top center.

Score: 79/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	8	10
Library Mastery	4	10
Total	79	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=24, legend title=18, legend text=16, cardinal labels=18 bold). All readable at 3600×3600.
• VQ-02: No Overlap (5/6) - No text overlap. Great circles create visual clutter in the southern region where many arcs converge, but this is partly inherent to stereonet plots.
• VQ-03: Element Visibility (5/6) - Poles are clearly visible (size=5, alpha=0.85). Great circles are intentionally subtle (size=0.5, alpha=0.35) which works for the overlay. Contour segments are faint but visible.
• VQ-04: Color Accessibility (4/4) - Uses a colorblind-safe palette (blue, pink, orange) with good contrast against the white background.
• VQ-05: Layout & Canvas (3/4) - Stereonet is well-centered. Some wasted space below the circle and in corners, but the square format is appropriate for this circular plot. Fills roughly 55% of canvas.
• VQ-06: Axis Labels & Title (2/2) - Axes correctly hidden for stereonet. Cardinal directions labeled. Title in correct format.

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom colorblind-safe palette, intentional layering (reference lines → contours → great circles → poles → boundary), subtle alpha on great circles. Above defaults but not publication-level polish.
• DE-02: Visual Refinement (4/6) - All axes, grid, and ticks properly removed. Custom tick marks at perimeter. Dashed reference lines are subtle. Good whitespace management.
• DE-03: Data Storytelling (3/6) - Three distinct geological feature types with visually separable clusters. Density contours highlight preferred orientations. However, no annotation or emphasis draws the viewer to key structural relationships.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct: Schmidt equal-area lower-hemisphere stereographic projection.
• SC-02: Required Features (4/4) - Great circles ✓, poles to planes ✓, density contours ✓, color-coded by feature_type ✓, tick marks every 10° ✓, N label ✓, legend ✓, subtle grid/reference lines ✓.
• SC-03: Data Mapping (3/3) - Strike/dip correctly converted to Lambert azimuthal equal-area projection coordinates. Poles at 90° to planes.
• SC-04: Title & Legend (3/3) - Title "stereonet-equal-area · letsplot · pyplots.ai" correct. Legend labels match data (Bedding, Joint, Fault).

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Three feature types with distinct orientations: moderate-dip bedding (NE strike), steep joints (two sets: N-S and E-W), moderate-steep faults (NW-SE). Good variety but a fourth feature type (e.g., foliation) would demonstrate more range.
• DQ-02: Realistic Context (5/5) - Realistic structural geology field measurements. Strike/dip values are plausible for a typical mapping campaign. Feature types are standard geological terminology.
• DQ-03: Appropriate Scale (4/4) - Strike values 0-360°, dip values 5-89°, realistic cluster spreads (σ=6-12°). All values physically meaningful.

Code Quality (8/10)

• CQ-01: KISS Structure (2/3) - Linear flow (imports → data → projection → plot → save), no functions/classes. However, the manual marching squares contour extraction (~45 lines) and manual KDE make the code quite verbose.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used: numpy, pandas, lets_plot, ggsave.
• CQ-04: Code Elegance (1/2) - The manual marching squares implementation is overly complex. The manual KDE loop is acceptable but verbose. Could benefit from scipy for KDE/contouring.
• CQ-05: Output & API (1/1) - Saves as plot.png via ggsave with scale=3. Also exports HTML.

Library Mastery (4/10)

• LM-01: Idiomatic Usage (3/5) - Uses ggplot grammar correctly with layered geoms (geom_path, geom_point, geom_segment, geom_text), aes mappings, scale_color_manual, coord_fixed, and theme customization. However, most of the heavy lifting (projection math, KDE, contouring) is done manually in numpy.
• LM-02: Distinctive Features (1/5) - Generic usage. The implementation could be done with any ggplot-based library with minimal changes. No lets-plot specific features leveraged (e.g., interactive tooltips, coord_polar exploration).

Score Caps Applied

• None

Strengths

• Mathematically correct equal-area projection with proper Lambert azimuthal formulas
• All spec requirements implemented: great circles, poles, density contours, tick marks, cardinal labels, legend
• Clean colorblind-safe palette with intentional visual layering
• Realistic geological data with meaningful feature type clusters
• Good visual refinement with removed axes and custom stereonet chrome

Weaknesses

• Manual marching squares contour extraction is overly verbose and complex (~45 lines)
• No lets-plot distinctive features used — the library is only used for final rendering
• Great circle visual clutter in the southern hemisphere where many arcs converge
• No visual emphasis or annotation to guide the viewer to key structural relationships
• Could use a background fill or more polished contour rendering

Issues Found

1. CQ-04 LOW: Manual marching squares contour extraction is over-engineered for this use case
   • Fix: Consider using scipy.ndimage or matplotlib.contour for contour path extraction, or simplify to filled density using geom_tile with alpha
2. LM-02 LOW: No distinctive lets-plot features used
   • Fix: Consider leveraging lets-plot tooltips, coord_polar, or other library-specific capabilities
3. DE-03 MEDIUM: Data is displayed but no visual hierarchy guides the viewer
   • Fix: Add subtle annotations for cluster centroids or mean orientations, vary point sizes, or use stronger color contrast to create focal points

AI Feedback for Next Attempt

Reduce code complexity by replacing the manual marching squares contour extraction with a simpler density visualization (e.g., geom_tile with alpha for filled density, or use scipy for contour paths). Improve data storytelling by adding annotations for mean pole orientations or cluster labels. Explore lets-plot specific features to raise LM-02. Consider adding a subtle background gradient or improving the contour rendering for more polished density visualization.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a Schmidt equal-area (lower-hemisphere) stereonet projection on a white/light gray circular canvas. The primitive circle is drawn as a thick black outline with tick marks every 10° (longer every 30°) around the perimeter. Cardinal directions N, E, S, W are labeled in bold black text outside the circle. Dashed light gray reference cross-lines mark the N-S and E-W axes. Four feature types are plotted: Bedding (blue #306998), Joint (pink #CC79A7), Fault (amber #E69F00), and Foliation (green #009E73). Great circles are drawn as thin colored arcs at low opacity (0.3). Poles to planes appear as colored dots clustered in distinct groups. Gray density contours (geom_density2d) overlay the pole data highlighting preferred orientations. Dark diamond markers with italic labels show mean pole positions with circular-mean strike/dip values (e.g., "Bedding 43/33", "Joint 356/78", "Fault 151/55", "Foliation 268/28"). A legend titled "Feature Type" appears on the right side. The title "stereonet-equal-area · letsplot · pyplots.ai" is centered at the top.

Score: 84/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	84	100

Visual Quality (25/30)

• VQ-01: Text Legibility (6/8) - Font sizes explicitly set (title 24, legend title 18, legend text 16, cardinal labels 18). Mean pole annotation labels at size 11 are somewhat small for the canvas size.
• VQ-02: No Overlap (5/6) - Minor overlap between mean pole labels and nearby data points (e.g., "Bedding 43/33" overlaps its cluster center).
• VQ-03: Element Visibility (5/6) - Poles well-sized at size 5/alpha 0.85. Great circles intentionally subtle at alpha 0.3. Some great circle arcs extend slightly beyond the primitive circle boundary due to projection math not being clipped.
• VQ-04: Color Accessibility (4/4) - Excellent colorblind-safe palette (#306998, #CC79A7, #E69F00, #009E73) with good contrast between all four categories.
• VQ-05: Layout & Canvas (3/4) - Square 1200×1200 with coord_fixed is appropriate. Legend placement on right slightly offsets the stereonet from center. Some great circles extend beyond the primitive circle.
• VQ-06: Axis Labels & Title (2/2) - Title correctly formatted. Axes intentionally blank (appropriate for stereonet). Cardinal directions and tick marks serve as orientation.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom colorblind-safe palette with intentional visual hierarchy (faint great circles → visible poles → emphasized diamond mean-pole markers). Subtle #FAFAFA panel background. Above defaults but not publication-quality polish.
• DE-02: Visual Refinement (4/6) - All axes, grid, ticks removed and replaced with manual stereonet elements. Dashed reference lines are subtle. Clean overall appearance with good whitespace.
• DE-03: Data Storytelling (4/6) - Mean pole annotations with strike/dip values guide the viewer to cluster centers. Density contours highlight preferred orientations. Visual hierarchy from faint great circles to emphasized mean poles creates a clear reading order.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Schmidt equal-area lower-hemisphere stereonet projection with proper Lambert azimuthal equal-area math.
• SC-02: Required Features (4/4) - All features present: great circles for planes, poles to planes, density contours (Kamb-style via geom_density2d), tick marks every 10°, color-coding by feature_type, legend, N label, subtle reference grid.
• SC-03: Data Mapping (3/3) - Strike/dip correctly mapped through equal-area projection. Four feature types with distinct orientations visible in proper positions.
• SC-04: Title & Legend (3/3) - Title "stereonet-equal-area · letsplot · pyplots.ai" correct. Legend "Feature Type" with matching labels.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Four distinct structural feature types (Bedding, Joint, Fault, Foliation) with different strike/dip distributions covering various orientations and dip angles.
• DQ-02: Realistic Context (5/5) - Realistic structural geology field campaign data with plausible orientations: NE-striking bedding at moderate dip, near-vertical N-S joints, NW-SE faults, E-W foliation.
• DQ-03: Appropriate Scale (4/4) - 70 total measurements (25+20+13+12) within the spec's 30-200 range. Strike values 0-360°, dip values properly clipped to 5-89°.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Flat script: imports → data generation → projection math → plot → save.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used, specific imports from lets_plot.
• CQ-04: Code Elegance (2/2) - Well-structured with clear variable naming, appropriate complexity for manual projection implementation. No fake UI.
• CQ-05: Output & API (1/1) - ggsave to plot.png with scale=3, current API.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Good ggplot grammar: layered geom composition, aes mappings, scale_color_manual, coord_fixed, theme customization. Uses layer_tooltips for interactive hover.
• LM-02: Distinctive Features (2/5) - Uses layer_tooltips (lets-plot interactive feature) and geom_density2d with kernel/adjust params. However, most of the implementation is manual geometry construction (projection math, circle drawing, tick marks) rather than leveraging lets-plot's unique capabilities. HTML export is a nice touch but not distinctive enough.

Score Caps Applied

• None

Strengths

• Mathematically correct Lambert azimuthal equal-area projection implemented from scratch
• Excellent data quality with four realistic geological feature types at distinct orientations
• Good visual hierarchy: great circles (faint) → poles (visible) → mean poles (emphasized diamonds)
• Density contours via geom_density2d effectively highlight clustering
• Mean pole annotations with circular-mean strike/dip values add geological context
• Interactive tooltips via layer_tooltips showing strike/dip per pole

Weaknesses

• Great circle arcs extend beyond the primitive circle boundary (need clipping to unit circle)
• Mean pole annotation labels (size 11) are small and overlap with nearby data points
• Legend placement offsets the stereonet from visual center

Issues Found

1. VQ-03/VQ-05 MODERATE: Great circles extend beyond the primitive circle — the projection scale factor 1.0 / sqrt(1.0 - pz) can produce coordinates > 1.0 for points near the horizontal plane
   • Fix: Clip great circle points to the unit circle or filter out points where x² + y² > 1
2. VQ-02 MINOR: Mean pole labels overlap with nearby data points, especially "Bedding 43/33"
   • Fix: Increase nudge_y offset or use a slightly larger nudge to separate labels from markers
3. LM-02 LOW: Implementation relies heavily on manual geometry rather than library-specific features
   • Fix: Consider if lets-plot has any polar or projection utilities that could be leveraged

AI Feedback for Next Attempt

Fix the great circle clipping issue — filter or clip great circle coordinates to stay within the unit circle (x² + y² ≤ 1). Increase mean pole label nudge to avoid overlap with pole markers. Consider increasing mean pole label font size from 11 to 13 for better legibility. These fixes should push the score above 90.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a Schmidt equal-area (lower-hemisphere) stereonet projection on a square canvas. A dark primitive circle forms the boundary with tick marks every 10° around the perimeter. Cardinal directions (N, E, S, W) are labeled in bold. Dashed light-gray reference lines cross at the center (N-S and E-W). Four geological feature types are plotted: Bedding (blue, clustered in the S-SE area), Joint (pink, clustered near E), Fault (orange/yellow, clustered in the SW area), and Foliation (green/teal, clustered in the NW area). Poles to planes appear as colored dots, great circles as thin transparent curved lines, and gray density contours overlay the pole data. Semi-transparent confidence ellipses surround each cluster. Dark diamond markers indicate mean pole positions, with italic labels showing mean strike/dip values (e.g., "Bedding 43/33", "Joint 356/78", "Fault 151/55", "Foliation 268/28"). A legend on the right identifies the four feature types. The background is a subtle off-white (#FAFAFA).

Score: 85/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	85	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) - Font sizes explicitly set (title=24, legend=18/16, cardinal=18, mean labels=14). All readable; mean labels slightly small but clear.
• VQ-02: No Overlap (4/6) - Minor overlap between "Joint 356/78" label and the E cardinal direction label. Some mean pole labels crowd nearby poles.
• VQ-03: Element Visibility (5/6) - Poles well-sized (size=5, alpha=0.85), great circles appropriately subtle (size=0.4, alpha=0.3). Mean markers prominent at size=10.
• VQ-04: Color Accessibility (4/4) - Excellent colorblind-safe palette (#306998, #CC79A7, #E69F00, #009E73) from Okabe-Ito family.
• VQ-05: Layout & Canvas (3/4) - Good use of square 1200×1200 canvas with coord_fixed. Slight imbalance with legend on right creating asymmetry.
• VQ-06: Axis Labels & Title (2/2) - Axes appropriately hidden for stereonet; cardinal directions serve as orientation labels.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Above defaults: Okabe-Ito palette, confidence ellipses with alpha fill, diamond mean markers, italic annotation labels, subtle off-white background. Clearly beyond library defaults.
• DE-02: Visual Refinement (4/6) - Axes/grid fully removed, subtle #FAFAFA panel background, dashed reference lines, tick marks well-designed with longer marks at 30° intervals.
• DE-03: Data Storytelling (4/6) - Mean pole annotations with strike/dip create clear focal points. Visual hierarchy: poles > mean markers > ellipses > great circles > density contours. Viewer can immediately identify cluster orientations.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct equal-area (Schmidt net) lower-hemisphere projection with Lambert azimuthal math.
• SC-02: Required Features (4/4) - Great circles ✓, poles to planes ✓, density contours (geom_density2d) ✓, color by feature_type ✓, legend ✓, tick marks every 10° ✓, N label ✓, Kamb-style density overlay ✓.
• SC-03: Data Mapping (3/3) - Strike/dip correctly converted to equal-area projection coordinates. Pole and great circle math is correct.
• SC-04: Title & Legend (3/3) - Title "stereonet-equal-area · letsplot · pyplots.ai" ✓. Legend "Feature Type" with correct labels ✓.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Four distinct feature types (bedding, joints, faults, foliation) with different orientations, dip angles (gentle to steep), and clustering patterns.
• DQ-02: Realistic Context (5/5) - Authentic structural geology mapping scenario with realistic feature types and orientations.
• DQ-03: Appropriate Scale (4/4) - Strike values at realistic azimuths, dip values appropriate per feature type (25° foliation, 35° bedding, 60° faults, 78° joints).

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data generation → projection math → plot → save. No functions/classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used, no unused imports.
• CQ-04: Code Elegance (2/2) - Projection math is necessarily complex but well-organized. Confidence ellipses via eigenvalue decomposition is elegant.
• CQ-05: Output & API (1/1) - Saves as plot.png via ggsave with scale=3.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of ggplot grammar with layered geoms, aesthetic mappings, scales, and theme customization. Proper use of geom_density2d for contouring.
• LM-02: Distinctive Features (3/5) - Uses layer_tooltips() for interactive hover info (lets-plot distinctive feature), geom_density2d with kernel/adjust parameters, and HTML export alongside PNG.

Score Caps Applied

• None

Strengths

• Excellent projection math: correct Lambert azimuthal equal-area implementation for both poles and great circles
• Strong spec compliance: all required features implemented including density contours, great circles, poles, tick marks, and cardinal labels
• Sophisticated data storytelling with mean pole annotations, confidence ellipses, and clear visual hierarchy
• Colorblind-safe palette and good use of alpha blending for layered elements
• Clean, well-structured code despite mathematical complexity

Weaknesses

• Minor label overlap near the E cardinal direction with the Joint mean annotation
• Legend positioned outside the circle creates slight layout asymmetry
• Great circles extend slightly beyond the primitive circle in some areas despite clipping logic

Issues Found

1. VQ-02 MINOR: "Joint 356/78" label overlaps with "E" cardinal direction label
   • Fix: Adjust nudge position or label placement for mean annotations near cardinal directions
2. VQ-05 MINOR: Legend placement creates slight right-heavy layout
   • Fix: Consider legend_position inside the plot or bottom placement

AI Feedback for Next Attempt

Strong implementation at 85/100. The stereonet is technically excellent with correct equal-area projection, all spec features present, and good visual hierarchy through mean annotations and confidence ellipses. To reach 90+, address the minor label overlap near cardinal directions and consider refining the layout balance. The colorblind-safe palette and layered transparency approach are well done.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a Schmidt equal-area (lower-hemisphere) stereonet projection on a white/light gray circular canvas. Four geological feature types are plotted as colored poles (dots): Bedding (blue, clustered in the lower-center), Joint (pink, clustered on the right near E), Fault (amber/orange, in the lower-left), and Foliation (teal/green, near the top-center). Great circles are drawn as thin, semi-transparent colored arcs for each measurement. Gray density contours (geom_density2d) overlay the pole data showing clustering patterns. Each cluster has a semi-transparent confidence ellipse fill and a dark diamond-shaped mean pole marker with italic annotation showing mean strike/dip (Bedding 43/33, Joint 356/78, Fault 151/55, Foliation 268/28). The primitive circle boundary is drawn in dark gray with tick marks every 10° (longer every 30°). Cardinal directions N, E, S, W are labeled in bold. Dashed reference cross lines mark N-S and E-W axes. A legend in the lower-right corner shows "Feature Type" with all four categories. Title reads "stereonet-equal-area · letsplot · pyplots.ai" in bold at the top.

Score: 88/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=26, cardinal=20, legend title=18, legend text=16). Mean annotation labels at size=14 are slightly small but still readable.
• VQ-02: No Overlap (5/6) - Joint label "356/78" is close to the "E" cardinal label; smart nudge logic helps but proximity remains. Density contours extend slightly beyond the primitive circle boundary.
• VQ-03: Element Visibility (5/6) - Poles clearly visible (size=5, alpha=0.85). Great circles appropriately subtle (size=0.35, alpha=0.25). Mean diamonds prominent (size=10).
• VQ-04: Color Accessibility (4/4) - Excellent colorblind-safe palette (#306998 blue, #CC79A7 pink, #E69F00 amber, #009E73 teal) following Okabe-Ito conventions.
• VQ-05: Layout & Canvas (3/4) - Square 1200×1200 canvas is appropriate for stereonet. Stereonet fills space well. Minor issue: density contours bleed beyond the primitive circle, creating visual noise outside the projection boundary.
• VQ-06: Axis Labels & Title (2/2) - Axes correctly blank for stereonet. Cardinal direction labels serve as orientation markers. Title in correct format.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: custom colorblind-safe palette, confidence ellipses with alpha fill, italic mean annotations with strike/dip, diamond mean markers, clean legend with border. Clearly above defaults.
• DE-02: Visual Refinement (5/6) - All axes/ticks/grid removed via theme. Subtle dashed reference cross lines. Custom tick marks at perimeter. Clean white/#FAFAFA background. Good attention to detail throughout.
• DE-03: Data Storytelling (4/6) - Mean annotations with strike/dip values guide interpretation. Confidence ellipses show cluster spread. Four distinct structural domains create clear visual groupings. Viewer can immediately see preferred orientations.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Schmidt equal-area lower-hemisphere stereonet projection with proper Lambert azimuthal equal-area math.
• SC-02: Required Features (4/4) - All required: great circles for planes, poles as points, density contours (geom_density2d), color-coding by feature_type, legend, 10° tick marks (longer at 30°), N label, equal-area projection.
• SC-03: Data Mapping (3/3) - Strike/dip correctly converted to equal-area projection coordinates. Pole computation and great circle paths are mathematically correct.
• SC-04: Title & Legend (3/3) - Title "stereonet-equal-area · letsplot · pyplots.ai" in correct format. Legend shows all four feature types with descriptive labels.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Four distinct structural feature types (Bedding, Joint, Fault, Foliation) with different mean orientations and dip angles, showing clear clustering patterns and spread variation.
• DQ-02: Realistic Context (5/5) - Geological structural data representing a field mapping campaign. Strike/dip combinations are geologically realistic.
• DQ-03: Appropriate Scale (4/4) - Strike values 0-360°, dip values appropriate per type (steep joints ~78°, moderate bedding ~35°, gentle foliation ~25°). Total 70 measurements is realistic for a field campaign stereonet.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → projection math → plot construction → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set at the beginning.
• CQ-03: Clean Imports (2/2) - All imports used. Clean explicit imports from lets_plot.
• CQ-04: Code Elegance (2/2) - Well-organized with clear section comments. Projection math is necessarily complex but cleanly implemented. Smart label nudge logic to avoid cardinal label overlap.
• CQ-05: Output & API (1/1) - Saves as plot.png via ggsave() with scale=3. Also exports HTML. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of ggplot2 grammar with layered geom_* calls, aes(), scale_*_manual(), coord_fixed(), and theme(). Proper layer composition pattern.
• LM-02: Distinctive Features (3/5) - Uses layer_tooltips() for interactive hover info on poles (showing strike/dip), HTML export for interactivity, and geom_density2d with kernel/adjust parameters. These are lets-plot specific features.

Score Caps Applied

• None applied

Strengths

• Mathematically correct equal-area projection with proper Lambert azimuthal formulas
• Comprehensive feature set: great circles, poles, density contours, confidence ellipses, mean annotations
• Excellent colorblind-safe palette following Okabe-Ito conventions
• Smart label nudge logic to avoid overlap with cardinal direction labels
• Interactive tooltips on poles showing strike/dip values (lets-plot distinctive feature)
• Clean, professional visual design with appropriate use of alpha blending and layering

Weaknesses

• Density contours (geom_density2d) extend beyond the primitive circle boundary, creating visual noise outside the stereonet
• Mean annotation label font size (14) is slightly small relative to other text elements
• Joint cluster labels crowd near the "E" cardinal label despite nudge logic

Issues Found

1. VQ-05 MINOR: Density contours bleed beyond the primitive circle boundary — in a proper stereonet, all data representation should be contained within the net
   • Fix: Clip density contours to the primitive circle or mask the exterior
2. VQ-01 MINOR: Mean annotation labels at size=14 are slightly small compared to other text (cardinal=20, legend=16-18)
   • Fix: Increase mean annotation text size to 16

AI Feedback for Next Attempt

Strong implementation with correct projection math and comprehensive feature coverage. Two minor visual issues remain: density contours extending beyond the primitive circle boundary (could be clipped/masked), and slightly small mean annotation labels. These are minor polish items that don't significantly impact usability. The implementation demonstrates good library mastery with interactive tooltips and clean ggplot grammar usage.

Verdict: REJECTED