feat(seaborn): implement circos-basic

[github-actions]

Implementation: circos-basic - python/seaborn

Implements the python/seaborn version of circos-basic.

File: plots/circos-basic/implementations/python/seaborn.py

Parent Issue: #3005

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The circos plot displays a software module dependency network on a warm off-white background (#FAF8F1). The outer ring shows 10 modules (Core, Auth, API, Database, Cache, Queue, Logging, Utils, Metrics, Config) as colored arc segments proportional to code volume, using the Okabe-Ito palette with green (#009E73) as the first series. The inner concentric track shows module sizes as bar heights (semi-transparent, alpha=0.5). Ribbons connecting the modules display dependencies with widths proportional to call counts. The title "circos-basic · seaborn · anyplot.ai" is bold and clearly readable at the top. Segment labels are rotated appropriately around the circle with dark text. The legend at the bottom explains the three visualization layers (outer ring, inner track, ribbons) with a light elevated background (#FFFDF6). All text is perfectly readable against the light background with no overlaps or legibility issues.

Dark render (plot-dark.png): The identical circos structure renders on a warm near-black background (#1A1A17). All data colors remain identical to the light render — the Okabe-Ito palette colors (green, orange, blue, purple, etc.) preserve their identity across themes. The title is now light-colored (#F0EFE8) and remains fully readable. Segment labels in light text are clearly visible around the circle with no dark-on-dark failures. The legend background shifts to darker elevated color (#242420) with light text. The ribbons and inner track bars maintain the same data colors and transparency as the light render. The theme adaptation is flawless: data colors are invariant, chrome (background, text, legend) is theme-correct, and all elements remain readable.

Score: 92/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	92	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — Title 26pt, labels 16pt, all explicitly sized and perfectly readable in both themes
• VQ-02: No Overlap (6/6) — Intelligent label rotation, no text collisions, all elements clearly separated
• VQ-03: Element Visibility (6/6) — Outer ring, inner track, and ribbons all clearly visible and well-adapted to visualization complexity
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is CVD-safe with good contrast between all elements
• VQ-05: Layout & Canvas (4/4) — Perfect use of square format (12×12), plot fills ~70% of canvas with balanced margins
• VQ-06: Axis Labels & Title (2/2) — Title includes spec-id, library, and anyplot.ai as required
• VQ-07: Palette Compliance (2/2) — First categorical series is #009E73; backgrounds are #FAF8F1 (light) / #1A1A17 (dark); theme-adaptive text colors correct in both renders

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom bezier curves for ribbons, thoughtful color-cycling through Okabe-Ito palette, sophisticated visual hierarchy with three data layers. Publication-quality composition with intentional design decisions beyond defaults.
• DE-02: Visual Refinement (6/6) — Custom polygon rendering with careful alpha values, no unnecessary grid or borders, generous whitespace, polished every detail
• DE-03: Data Storytelling (4/6) — Strong visual encoding: arc sizes show code volume, bar heights show module size, ribbon widths show call counts. Visual hierarchy guides viewer through the dependency relationships clearly.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct circos plot with all required structure
• SC-02: Required Features (4/4) — Segments on circle, proportional ribbons, distinct colors, concentric inner track present
• SC-03: Data Mapping (3/3) — Source→target mapping correct, values properly encoded to visual dimensions
• SC-04: Title & Legend (3/3) — Title format correct, legend labels clearly explain visualization

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all aspects: varying segment sizes, diverse dependency strengths, inner track variation
• DQ-02: Realistic Context (5/5) — Real, neutral software engineering context (module dependencies)
• DQ-03: Appropriate Scale (4/4) — Proportions are factually plausible (code volume 600–2800 LOC, call counts 12–72)

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Imports → theme setup → data → plot → save; no unnecessary functions
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ensures deterministic output
• CQ-03: Clean Imports (2/2) — Only used imports: os, matplotlib.patches, matplotlib.pyplot, numpy, seaborn
• CQ-04: Code Elegance (2/2) — Appropriate complexity for custom circos; no fake functionality
• CQ-05: Output & API (1/1) — Correctly saves as plot-{THEME}.png with dpi=300

Library Mastery (6/10)

• LM-01: Idiomatic Usage (3/5) — Correctly uses sns.set_theme() and theme tokens for consistent styling. Circos plots inherently require custom drawing (not high-level seaborn functions), so the matplotlib-based approach is appropriate.
• LM-02: Distinctive Features (3/5) — Leverages matplotlib's strength in custom geometric visualization; uses theme-adaptive token system

Score Caps Applied

• None — no disqualifying conditions

Strengths

• Flawless theme adaptation: Data colors identical between light/dark, chrome perfectly theme-correct in both renders. No dark-on-dark or light-on-light failures.
• Sophisticated visualization design: Custom bezier curves for ribbons, proportional encoding (arc size = code volume, bar height = module size, ribbon width = call count) tells a clear story.
• Excellent typography: Font sizes explicitly set, all text readable at full resolution, intelligent label rotation avoids overlaps.
• Publication-ready composition: Balanced layout, generous whitespace, polished details throughout.
• Real-world data context: Software module dependencies is relatable and neutral; proportions are factually plausible.

Weaknesses

• Minor: LM-02 (Distinctive Features) could be 4+ if the implementation showcased more seaborn-specific styling features beyond sns.set_theme(), though the custom circos approach is inherently matplotlib-focused and appropriate.

Issues Found

None. Implementation is excellent across all criteria.

AI Feedback for Next Attempt

N/A — This implementation is approved on first review. No repairs needed.

Verdict: APPROVED