update(arc-basic): seaborn — comprehensive quality review

[MarkusNeusinger]

Summary

Updated seaborn implementation for arc-basic.

Changes: Comprehensive quality review and update

Changes

• Updated implementation with improved code quality and visual design

Test Plan

• Preview images uploaded to GCS staging
• Implementation file passes ruff format/check
• Metadata YAML updated with current versions
• Automated review triggered

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Generated with Claude Code /update command

[Copilot]

Pull request overview

Updates the seaborn implementation for the arc-basic plot and refreshes associated metadata to reflect the latest run/version info.

Changes:

• Improved arc styling by mapping interaction weights to a sequential colormap and adding a colorbar.
• Refined plot layout/typography and simplified presentation (no legend, cleaner axes).
• Updated metadata (timestamps, generator, Python version, quality score field).

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

File	Description
plots/arc-basic/metadata/seaborn.yaml	Refreshes run metadata (timestamps, generator, Python version, quality score).
plots/arc-basic/implementations/seaborn.py	Updates arc rendering to use weight-based coloring + colorbar and adjusts styling/layout.

Comments suppressed due to low confidence (2)

plots/arc-basic/metadata/seaborn.yaml:13

• Changing quality_score from an integer to null alters the field’s type and may break consumers that expect a numeric score (e.g., dashboards, thresholds, sorting). Consider either (a) providing the updated numeric score, or (b) omitting the field entirely if unknown, or (c) introducing a separate field like quality_score_pending: true while keeping quality_score numeric.

quality_score: null

plots/arc-basic/implementations/seaborn.py:4

• The docstring header has an incomplete quality value (Quality: /100). This reads like a formatting regression and also diverges from the metadata file’s prior numeric score. Consider restoring the numeric score (e.g., Quality: 88/100) or removing the quality segment entirely if it isn’t intended to be populated.

Quality: /100 | Updated: 2026-02-23

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a basic arc diagram with 12 character nodes (Alice, Bob, Carol, Dave, Eve, Frank, Grace, Henry, Ivy, Jack, Kate, Leo) arranged along a horizontal baseline as dark blue (#306998) circles. Connections between characters are drawn as curved arcs above the baseline using the "flare" colormap — arcs range from light salmon/orange (weight 1) to deep purple (weight 5). Arc thickness also scales with weight. Arc height is proportional to the distance between connected nodes, making the Alice–Leo long-range arc the tallest and most prominent. A colorbar on the right edge displays "Interaction Strength" from 1 to 5. The title "arc-basic · seaborn · pyplots.ai" appears at the top. All spines are removed, axes are hidden, and a subtle semi-transparent horizontal baseline anchors the nodes. Node labels appear below the baseline in Python Blue with medium font weight.

Score: 83/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	5	10
Total	83	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) - Title at 24pt, node labels at 16pt with medium weight, colorbar label at 16pt, colorbar ticks at 14pt. All readable but colorbar label slightly below recommended 20pt for labels.
• VQ-02: No Overlap (6/6) - No text overlaps. Arcs overlap naturally but semi-transparency handles this well.
• VQ-03: Element Visibility (5/6) - Nodes at s=600 are clearly visible for 12 points. Thinner low-weight arcs (weight=1) are somewhat subtle against the white background.
• VQ-04: Color Accessibility (3/4) - Flare palette (orange→purple) is perceptually uniform and mostly colorblind-safe, but not among the recommended optimal palettes (viridis, cividis).
• VQ-05: Layout & Canvas (3/4) - Good canvas utilization overall. Arcs fill vertical space well. Some wasted whitespace below the baseline.
• VQ-06: Axis Labels & Title (2/2) - Title format correct. Colorbar labeled "Interaction Strength." No traditional axes needed for arc diagram — node labels serve as categorical axis.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Thoughtful design choices: "flare" colormap creates warm-to-cool arc gradient, consistent Python Blue for nodes and baseline, clean composition. Clearly above library defaults but not publication-exceptional.
• DE-02: Visual Refinement (5/6) - All spines removed, grid disabled, ticks hidden, subtle baseline at alpha=0.3, well-proportioned colorbar (shrink=0.35). Clean whitespace. Minor issue: empty space below baseline.
• DE-03: Data Storytelling (3/6) - Weight-based color and thickness create some visual hierarchy — strong connections (thick/dark) are distinguishable from weak ones (thin/light). The long-range Alice–Leo arc draws the eye. However, there's no explicit focal point or narrative emphasis. Viewer must explore connections independently.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct arc diagram: nodes along horizontal line with curved arcs above.
• SC-02: Required Features (4/4) - All spec features present: ordered nodes, weighted edges, arc height proportional to distance, semi-transparency for overlapping arcs, color coding by weight, readable node labels.
• SC-03: Data Mapping (3/3) - X positions correctly assigned to nodes. Arcs correctly connect paired nodes. Height proportional to node distance.
• SC-04: Title & Legend (3/3) - Title "arc-basic · seaborn · pyplots.ai" in correct format. Colorbar provides weight legend with descriptive label and integer ticks.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows both short-range and long-range connections, weight variety from 1 to 5, dense connection areas (around Dave-Eve) and sparse areas. Could benefit from showing an isolated node or more extreme connectivity patterns.
• DQ-02: Realistic Context (5/5) - "Character interactions in a story" is a real, comprehensible, neutral scenario. Character names are common and appropriate. Interaction strengths are plausible.
• DQ-03: Appropriate Scale (4/4) - 12 nodes (within recommended 10-50 range), 18 edges providing good connectivity without clutter, weights 1-5 on sensible integer scale.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot setup → draw arcs → labels → styling → save.
• CQ-02: Reproducibility (2/2) - Fully deterministic: hardcoded nodes, edges, and weights. No random elements.
• CQ-03: Clean Imports (2/2) - All imports used: matplotlib.patches (Arc), plt (figure/colorbar), numpy (arange/zeros), pandas (DataFrame), seaborn (theme/scatterplot/palette).
• CQ-04: Code Elegance (2/2) - Clean loop for arc drawing, good use of normalization for color mapping, appropriate complexity for the visualization.
• CQ-05: Output & API (1/1) - Saves as plot.png at dpi=300 with bbox_inches="tight". No deprecated API calls.

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) - Uses sns.set_theme() for global styling, sns.scatterplot() for nodes with DataFrame, and sns.color_palette() for colormapping. However, the core arc drawing relies entirely on matplotlib patches — seaborn doesn't natively support arc diagrams, so this is expected but limits the idiomatic score.
• LM-02: Distinctive Features (2/5) - Uses seaborn-specific features: sns.color_palette("flare", as_cmap=True) for the colormap and sns.set_theme(style="white", context="talk") for theming. These add value but are primarily cosmetic — the core visualization could be replicated with matplotlib alone.

Score Caps Applied

• None — no cap conditions triggered

Strengths

• Perfect spec compliance: all arc diagram features implemented correctly with proper height-distance proportionality
• Clean, polished design with thoughtful color choices (flare palette, consistent Python Blue)
• Excellent visual refinement: all spines removed, subtle baseline, clean colorbar placement
• Well-chosen example data with meaningful character interaction context
• Clean, deterministic, well-structured code

Weaknesses

• Library mastery limited by seaborn's lack of native arc diagram support — most work is matplotlib
• Low-weight arcs (weight=1) are somewhat faint and could benefit from a higher minimum linewidth or opacity
• Colorbar label and node labels could use larger font sizes (20pt for labels)
• No visual storytelling emphasis — the plot displays connections but doesn't highlight a narrative insight
• Canvas has some wasted whitespace below the baseline

Issues Found

1. LM-01/LM-02 LOW: Core visualization is matplotlib patches with seaborn only for styling/theming
   • Fix: Explore using seaborn's built-in features more creatively — e.g., use a seaborn heatmap-style approach for edge weight encoding, or leverage seaborn's statistical features
2. DE-03 LOW: No clear data storytelling or focal point emphasis
   • Fix: Use color contrast, annotation, or size emphasis to highlight key narrative connections (e.g., strongest or most distant connections). Consider grouping or clustering nodes to create visual narrative
3. VQ-01 MINOR: Colorbar label at 16pt is below recommended 20pt for label text
   • Fix: Increase colorbar label fontsize to 20 and tick labels to 16
4. VQ-03 MINOR: Low-weight arcs are faint
   • Fix: Increase minimum linewidth or raise alpha slightly for low-weight connections

AI Feedback for Next Attempt

Improve library mastery by finding more creative ways to leverage seaborn's distinctive features. Strengthen data storytelling by adding visual emphasis to key connections — consider annotating the strongest or most distant arcs, or using size/color contrast to create a clear focal point. Increase colorbar label to 20pt and ensure low-weight arcs are more visible (raise minimum linewidth). Reduce wasted whitespace below the baseline by adjusting ylim.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a basic arc diagram with 12 character nodes (Alice through Leo) arranged along a horizontal baseline. Curved arcs above the baseline connect pairs of characters, with arc height proportional to the distance between connected nodes. Arcs are color-coded using a reversed viridis palette across 5 interaction strength levels: Weak (yellow-green), Light (green), Moderate (teal), Strong (blue), and Intense (dark purple). Arc thickness also varies with weight — thicker arcs indicate stronger connections. Two italic annotations with arrows provide narrative context: "Weakest link, longest reach" points to the tall Alice–Leo arc spanning the full diagram, while "Strongest local bonds" points to the cluster of short, thick arcs near Dave–Eve. Nodes are rendered as dark blue (#306998) dots with white edges. The title "arc-basic · seaborn · pyplots.ai" appears at the top. A legend in the upper right shows all 5 strength categories. All spines and ticks are removed, and a subtle light-blue horizontal baseline anchors the nodes.

Score: 91/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	91	100

Visual Quality (28/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 24pt, legend 16/20pt, node labels 16pt). Annotation text at 13pt is slightly small relative to other elements but still legible.
• VQ-02: No Overlap (6/6) — No text overlap. Node labels well-spaced, annotations positioned in clear areas, legend in upper right doesn't collide with text.
• VQ-03: Element Visibility (5/6) — Arcs well-visible with sizes 2.0–6.0 and alpha=0.7. Some arc crowding in the left-center region (Dave–Eve–Frank cluster) makes individual arcs harder to distinguish, but colors and thickness still differentiate them.
• VQ-04: Color Accessibility (4/4) — Viridis palette is perceptually uniform and colorblind-safe. Good contrast between all 5 strength levels.
• VQ-05: Layout & Canvas (4/4) — 16:9 canvas well-utilized. Arcs span the full horizontal range, vertical space used by the tall Alice–Leo arc. Balanced margins, nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — Axes appropriately hidden for arc diagram. Nodes labeled directly below baseline. Title descriptive.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: reversed viridis palette sampled at specific values for semantic encoding (darker = stronger), Python Blue (#306998) for nodes creating thematic cohesion, styled legend with fancybox/framealpha/edgecolor, and white node edges for visual separation. Clearly above configured defaults.
• DE-02: Visual Refinement (5/6) — All spines removed, all ticks hidden, clean white background, subtle baseline at alpha=0.3, no grid (appropriate for this plot type), well-styled legend with subtle border. Very polished.
• DE-03: Data Storytelling (5/6) — Two annotations create a compelling narrative contrast: the tallest arc (Alice–Leo) is the weakest connection ("Weakest link, longest reach") while the strongest bonds are between nearby nodes ("Strongest local bonds"). Color+thickness double encoding reinforces the hierarchy. Viewer immediately grasps the insight.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct arc diagram: nodes on horizontal line, curved arcs above.
• SC-02: Required Features (4/4) — All spec features present: arc height proportional to distance (h = dist * 0.4), semi-transparent arcs (alpha=0.7), readable node labels, color coding by weight, thickness by weight.
• SC-03: Data Mapping (3/3) — Nodes correctly positioned along x-axis. Arcs connect correct pairs. Height, color, and thickness all correctly mapped to edge properties.
• SC-04: Title & Legend (3/3) — Title "arc-basic · seaborn · pyplots.ai" in correct format. Legend titled "Interaction Strength" with descriptive labels (e.g., "1 · Weak", "5 · Intense").

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — 12 nodes and 18 edges showcase all arc diagram features: short-range connections (adjacent nodes), long-range connections (Alice–Leo, Frank–Kate), varied weights (1–5), both dense and sparse regions.
• DQ-02: Realistic Context (5/5) — "Character interactions in a story" is a real, comprehensible, neutral scenario. One of the canonical use cases listed in the spec.
• DQ-03: Appropriate Scale (4/4) — 12 characters within the recommended 10–50 node range. 18 edges provide good density without overcrowding. Weights 1–5 are a sensible interaction scale.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data → arc coordinate generation → DataFrame → theme → plot → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — All data is hardcoded and deterministic. No random generation.
• CQ-03: Clean Imports (2/2) — All four imports (plt, np, pd, sns) are used. No unused imports.
• CQ-04: Code Elegance (2/2) — Clean arc coordinate generation loop, good use of pd.Categorical for ordered legend, appropriate legend handle filtering. No over-engineering or fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png at dpi=300 with bbox_inches='tight'. No deprecated functions.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of seaborn's high-level API: sns.lineplot with units/hue/size for multi-arc rendering, sns.scatterplot for nodes, sns.set_theme for global styling, sns.despine for axis cleanup. Falls back to matplotlib for arc coordinate computation and annotations, which is expected for this non-standard chart type.
• LM-02: Distinctive Features (3/5) — The units parameter with estimator=None is distinctively seaborn, enabling per-edge line drawing without aggregation. Simultaneous hue+size encoding leverages seaborn's multi-dimensional mapping. Long-form DataFrame approach follows seaborn's data paradigm. However, core arc geometry is pure matplotlib math.

Score Caps Applied

• None — no failing conditions triggered

Strengths

• Excellent data storytelling with two annotations creating a compelling narrative contrast between long-range weak and short-range strong connections
• Cohesive color scheme: reversed viridis for semantic encoding (darker = stronger) plus Python Blue nodes
• All spines/ticks removed with subtle baseline creates a clean, modern look
• Full spec compliance with all required features (proportional height, semi-transparency, color coding, thickness)
• Well-crafted legend with styled frame, appropriate title, and descriptive category labels

Weaknesses

• Annotation text at fontsize=13 is noticeably smaller than other text elements (16–24pt)
• Some arc crowding in the Dave–Eve–Frank cluster makes individual arcs harder to trace

Issues Found

1. VQ-01 MINOR: Annotation text at 13pt is slightly small compared to the 16pt+ used elsewhere
   • Fix: Increase annotation fontsize to 14–15pt for better consistency
2. VQ-03 MINOR: Arc crowding in the left-center cluster
   • Fix: Could slightly increase alpha or adjust arc height multiplier, but this is inherent to the data density

AI Feedback for Next Attempt

Strong implementation that successfully addresses previous review feedback. The storytelling annotations, refined viridis palette, and clean layout elevate this above a typical arc diagram. Minor improvements possible: bump annotation fontsize to 14–15pt for consistency with other text elements.

Verdict: APPROVED