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

[MarkusNeusinger]

Summary

Updated altair 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

This PR updates the altair implementation for the arc-basic plot specification as part of a comprehensive quality review. The changes modernize the metadata (Python 3.14.3, claude-opus-4-6) and significantly enhance the visualization with improved visual hierarchy, interactive tooltips, better legends, and node sizing based on connection weights.

Changes:

• Updated metadata with current Python version (3.14.3), model version (claude-opus-4-6), and timestamp
• Enhanced arc visualization with weighted opacity and thickness for visual hierarchy
• Added interactive tooltips for both arcs (connection pairs and strength) and nodes (character names and total connection weight)
• Implemented proportional node sizing based on total connection weight
• Added informative legend for interaction strength with custom labels (Weak/Moderate/Strong)
• Improved canvas utilization with tighter y-domain calculations

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
plots/arc-basic/metadata/altair.yaml	Updated metadata with current versions (Python 3.14.3, claude-opus-4-6), timestamp, and set quality_score to null pending automated review
plots/arc-basic/implementations/altair.py	Comprehensive quality improvements including enhanced visual hierarchy, interactive tooltips, proportional node sizing, informative legends, and improved code comments

[Copilot]

The header quality field shows '/100' without a score value, creating an awkward format. When the quality score is pending or null, consider either omitting the quality line entirely or using a placeholder like 'Quality: Pending | Updated: 2026-02-23' for better readability.

Suggested change

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

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a basic arc diagram with 10 character nodes (Alice, Bob, Carol, David, Eve, Frank, Grace, Henry, Iris, Jack) arranged along a horizontal baseline at the bottom. Curved semicircular arcs connect nodes above the baseline, with arc height proportional to the distance between connected nodes — the tallest arc spans from Alice to Jack across the entire width. Arcs are rendered in steel blue (#306998) with varying thickness and opacity based on interaction strength: thicker, more opaque arcs indicate stronger connections (weight 3), while thinner, more transparent arcs indicate weaker ones (weight 1). Nodes are yellow circles (#FFD43B) with blue borders, sized proportionally to total connection weight — Alice and David appear largest as the most connected characters. Bold blue labels appear below each node. The title "Character Interactions · arc-basic · altair · pyplots.ai" is centered at the top. A legend in the top-right corner shows "Interaction Strength" with Weak, Moderate, and Strong categories distinguished by line thickness. The background is clean white with no axes or grid lines.

Score: 86/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	86	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 28pt, labels 18pt bold, legend title 16pt). Legend label font at 14pt is slightly below the 16pt tick guideline.
• VQ-02: No Overlap (6/6) — No text overlaps. Node labels well-spaced along baseline. Arc overlaps are expected and handled with semi-transparency.
• VQ-03: Element Visibility (5/6) — Arcs clearly visible with weight-based thickness/opacity differentiation. Nodes are somewhat small relative to the arc canvas, though still visible.
• VQ-04: Color Accessibility (4/4) — Blue/yellow color scheme is colorblind-safe. Weight differentiation uses thickness AND opacity, not color alone.
• VQ-05: Layout & Canvas (3/4) — Good canvas utilization overall. Jack's node and label are tight against the right edge. Some unused space in the upper-right around the legend.
• VQ-06: Axis Labels & Title (2/2) — Descriptive title with context. Axes appropriately hidden for arc diagram.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Intentional Python blue/yellow palette with good color harmony. Weight-based opacity+thickness creates clear hierarchy. Above well-configured defaults but not at publication-level polish.
• DE-02: Visual Refinement (4/6) — Axes hidden, view border removed (strokeWidth=0), legend styled with transparent stroke and padding. Clean, minimal chrome. Could benefit from more refined whitespace balance.
• DE-03: Data Storytelling (4/6) — Weight encoding (thickness + opacity) and node sizing by connection count create visual hierarchy. Viewer can quickly identify central characters (Alice, David) and strong connections. Arc height naturally reveals connection range.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct arc diagram with nodes along horizontal line and curved arcs above.
• SC-02: Required Features (4/4) — All spec features present: arc height proportional to distance, semi-transparent arcs, readable labels, color coding by weight, weight-based thickness.
• SC-03: Data Mapping (3/3) — Nodes correctly positioned along x-axis. Arcs connect correct nodes. Height proportional to span distance.
• SC-04: Title & Legend (2/3) — Title includes all required elements (arc-basic · altair · pyplots.ai) but adds extra "Character Interactions ·" prefix. Legend is well-labeled with Weak/Moderate/Strong.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows short-range (Alice-Bob) and long-range (Alice-Jack) connections. All three weight levels represented. Varying node importance through connection count. Good density of 15 edges across 10 nodes.
• DQ-02: Realistic Context (5/5) — "Character interactions in a story chapter" is a real, comprehensible, neutral scenario directly from the spec's applications list.
• DQ-03: Appropriate Scale (4/4) — 10 nodes is within the spec's recommended 10-50 range. 15 edges provide good density without clutter. Weight values 1-3 are sensible.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean imports → data → plot → save flow. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set. Data is deterministic (manually defined edges).
• CQ-03: Clean Imports (2/2) — All imports used: altair, numpy (linspace, pi, cos, sin), pandas (DataFrame).
• CQ-04: Code Elegance (2/2) — Appropriate complexity for manual arc generation. Clean arc path computation. No over-engineering.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0. Also saves plot.html. Uses current Altair 6.x API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of Altair's declarative grammar: Chart → mark → encode → properties → layer composition. Proper encoding types and scales. Arc path generation is necessarily manual since Altair has no native arc mark.
• LM-02: Distinctive Features (3/5) — Uses labelExpr for legend label customization (Vega-Lite expression), tooltips for HTML interactivity, layer composition via alt.layer(), configure_view/configure_legend for global styling.

Score Caps Applied

• None — no cap conditions triggered

Strengths

• Excellent data quality with realistic character interaction scenario covering all arc diagram features
• Thoughtful visual hierarchy through weight-based thickness AND opacity encoding plus node sizing
• Clean, idiomatic Altair code with proper declarative grammar and layer composition
• Colorblind-safe blue/yellow palette with good color harmony
• Good use of Altair-specific features (labelExpr, tooltips, HTML export)

Weaknesses

• Legend label font size at 14pt is below the 16pt minimum guideline
• Nodes appear somewhat small relative to the arc canvas area
• Right canvas edge is tight near Jack's node — risk of clipping
• Title format prepends "Character Interactions" beyond the standard {spec-id} · {library} · pyplots.ai format
• Design could be further elevated (e.g., more refined palette, stronger focal emphasis)

Issues Found

1. VQ-01 MINOR: Legend label fontSize at 14pt below 16pt minimum
   • Fix: Increase legend labelFontSize to at least 16pt
2. VQ-03 MINOR: Nodes appear small relative to arc canvas
   • Fix: Increase node size range (e.g., range=[450, 1000]) for better visibility
3. VQ-05 MINOR: Right edge tight near Jack node
   • Fix: Add padding to x-domain or reduce x-range to ensure all nodes have margin
4. SC-04 MINOR: Title format has extra descriptive prefix
   • Fix: Use exact format "arc-basic · altair · pyplots.ai" for the title
5. DE-01 MODERATE: Design is above defaults but not publication-level
   • Fix: Consider a more refined multi-tone palette, stronger typographic hierarchy
6. LM-02 MODERATE: Could leverage more distinctive Altair features
   • Fix: Consider using selection parameters, conditional encoding, or gradient strokes

AI Feedback for Next Attempt

Fix the title to use exact format "arc-basic · altair · pyplots.ai". Increase legend label font size to ≥16pt and node size range for better visibility. Add x-axis padding so edge nodes aren't clipped. For design elevation, consider using gradient or multi-hue arcs to distinguish connection types, add subtle background shading, or use Altair selection parameters for conditional highlighting to showcase library mastery. Focus on making the strongest connections (Alice-Bob, Carol-David) stand out more dramatically as focal points.

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 10 character nodes (Alice, Bob, Carol, David, Eve, Frank, Grace, Henry, Iris, Jack) arranged along a horizontal baseline near the bottom. Nodes are rendered as yellow (#FFD43B) circles with dark blue (#152d4a) strokes, with sizes proportional to each character's total connection weight — Alice and David are visibly the largest. Curved semicircular arcs connect pairs of nodes above the baseline, with arc height proportional to the distance between connected nodes. Arc thickness ranges from thin (~1.5px) for weight-1 connections to thick (~6px) for weight-3 connections. Arc color uses a blue gradient: light steel blue (#7daed4) for weak connections, medium blue (#306998) for moderate, and dark navy (#152d4a) for strong. Arc opacity also varies (0.3–0.8) by weight. A legend in the top-right labeled "Interaction Strength" shows Weak, Moderate, and Strong categories. The title "arc-basic · altair · pyplots.ai" is centered at the top in bold 28pt font. The background is clean white with no axes, grid, or chart border.

Score: 90/100

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	9	10
Total	90	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — Title at 28pt, node labels at 18pt bold (slightly below 20pt guideline for labels), legend title/labels at 16pt. All text readable and explicitly sized.
• VQ-02: No Overlap (6/6) — No text collisions. Node labels well-spaced along baseline. Arc overlaps near dense nodes are inherent to the chart type and mitigated by transparency.
• VQ-03: Element Visibility (5/6) — Nodes clearly visible with yellow fill and dark stroke. Strong/moderate arcs are prominent. Weakest arcs (weight=1, opacity 0.3) are somewhat faint, though their long-range height makes them distinguishable by shape.
• VQ-04: Color Accessibility (4/4) — Monochromatic blue palette is inherently colorblind-safe. Yellow nodes contrast well against blue arcs. Weight differentiation uses thickness and opacity in addition to color lightness.
• VQ-05: Layout & Canvas (3/4) — Plot fills ~60-65% of the canvas horizontally. Some empty space in upper-left area. Legend is slightly separated from the main data area. Overall proportions are good.
• VQ-06: Axis Labels & Title (1/2) — Title correctly formatted. Arc diagrams don't use traditional axes; node labels serve as positional identifiers. No units applicable for this chart type.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom blue gradient palette (#7daed4 → #306998 → #152d4a) with contrasting yellow nodes creates a cohesive Python-themed color scheme. Bold typography on labels, intentional visual hierarchy through weight encoding (color + thickness + opacity). Clearly above configured defaults.
• DE-02: Visual Refinement (5/6) — View stroke removed, axes hidden, legend styled with transparent stroke and custom padding/colors, clean white background. Every decorative element is deliberately configured. Minor: legend symbol width doesn't perfectly match arc width variation.
• DE-03: Data Storytelling (4/6) — Triple encoding of weight (color, thickness, opacity) creates effective visual hierarchy. Node sizing by connection weight immediately identifies Alice and David as central characters. The viewer can distinguish long-range vs short-range connections by arc height. Guides the viewer without annotations.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct arc diagram: nodes on horizontal line, semicircular arcs above.
• SC-02: Required Features (4/4) — Arc height proportional to node distance ✓, semi-transparent arcs ✓, readable node labels ✓, color-coded edges by weight ✓, weight affects arc thickness ✓.
• SC-03: Data Mapping (3/3) — Nodes correctly positioned along x-axis. Arcs correctly connect specified pairs. Heights scale with span distance.
• SC-04: Title & Legend (3/3) — Title "arc-basic · altair · pyplots.ai" in correct format. Legend "Interaction Strength" with Weak/Moderate/Strong labels matches data encoding.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows short-range (Alice–Bob), medium-range (David–Frank), and long-range (Alice–Jack) connections. Includes all three weight levels. Varying node connectivity (Alice highly connected, Eve sparse). Demonstrates key arc diagram features comprehensively.
• DQ-02: Realistic Context (5/5) — "Character interactions in a story chapter" is a plausible, neutral scenario directly mentioned in the spec's applications. Character names are generic English names. Weights represent interaction strength.
• DQ-03: Appropriate Scale (4/4) — 10 nodes within spec's recommended 10–50 range. 15 edges with weights 1–3. Sensible density for readability.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data → arc computation → chart layers → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set. Edge data is hardcoded and deterministic.
• CQ-03: Clean Imports (2/2) — All three imports (altair, numpy, pandas) are used.
• CQ-04: Code Elegance (2/2) — Appropriate complexity for generating semicircular arc paths. No fake UI elements or over-engineering. Arc generation loop is clean and readable.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0 and plot.html. Uses current Altair 6.x API.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Declarative grammar with alt.Chart().mark_*().encode() pattern. Layer composition via alt.layer(). Proper encoding types (Q, N). Uses alt.Scale, alt.Legend, alt.Title, alt.condition(), configure_view, configure_legend — expertly idiomatic Altair.
• LM-02: Distinctive Features (4/5) — Interactive hover selection (alt.selection_point(on="pointerover")), conditional encoding (alt.condition(hover, ...)), native tooltips, labelExpr for custom legend labels — all distinctive Altair features not easily replicated in other libraries. HTML export leverages Altair's Vega-Lite foundation.

Score Caps Applied

• None

Strengths

• Effective triple encoding of edge weight through color, thickness, and opacity creates clear visual hierarchy
• Cohesive Python-themed color palette (blue gradient arcs + yellow nodes) with strong contrast
• Full spec compliance with all required features including proportional arc height, semi-transparency, and color coding
• Excellent use of Altair's declarative grammar with interactive hover selection and conditional encoding
• Node sizing by connection weight adds an extra data dimension that aids storytelling

Weaknesses

• Node label font size (18pt) is slightly below the 20pt guideline for labels
• Weakest arcs (weight=1, opacity 0.3) are quite faint and could benefit from a higher minimum opacity
• Legend positioned somewhat far from the main data area with empty space in the upper-left

Issues Found

1. VQ-01 MINOR: Node labels at fontSize=18 are slightly below the 20pt label guideline
   • Fix: Increase node label fontSize from 18 to 20
2. VQ-03 MINOR: Weakest arcs with opacity 0.3 are faint
   • Fix: Raise minimum opacity from 0.3 to 0.4 in the opacity scale range

AI Feedback for Next Attempt

Strong implementation at attempt 2. The weight-based visual hierarchy through triple encoding (color, thickness, opacity) is effective. Consider bumping node label font size to 20pt and raising minimum arc opacity slightly to make weak connections more visible. The legend placement leaves some unused canvas space in the upper-left.

Verdict: APPROVED