feat(letsplot): implement manhattan-gwas

[github-actions]

Implementation: manhattan-gwas - python/letsplot

Implements the python/letsplot version of manhattan-gwas.

File: plots/manhattan-gwas/implementations/python/letsplot.py

Parent Issue: #2925

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays on a warm off-white background (#FAF8F1) with clear dark text. Manhattan plot structure shows alternating green (#009E73) and orange (#D55E00) chromosome bands along the x-axis for all 22 chromosomes. Y-axis shows -log₁₀(p-value) ranging from 0-12. A yellow/orange dashed threshold line marks genome-wide significance at y≈7.3, with a subtle dotted line at y≈5 for suggestive threshold. Significant SNPs are highlighted in brighter orange above the main threshold. Title, axis labels ('Chromosome' and '-log₁₀(p-value)'), and chromosome numbers are all clearly readable against the light background. No text legibility issues.

Dark render (plot-dark.png): The same plot renders identically on a warm near-black background (#1A1A17) with light text. The data colors (green and orange) are identical to the light render—only the chrome (background, text, grid, threshold lines) has adapted to the dark theme. Title text is in light color (#F0EFE8), axis labels and ticks in secondary light color (#B8B7B0). Grid lines and threshold lines are properly visible. No dark-on-dark failures observed. All text is clearly readable against the dark background, confirming correct theme-adaptive implementation where data colors remain constant across themes while only non-data elements flip.

Score: 92/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	10	10
Total	92	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title 28pt, labels 22pt, ticks 16pt); perfectly readable
• VQ-02: No Overlap (6/6) - All text fully readable, no collisions
• VQ-03: Element Visibility (6/6) - 44k points with optimal alpha=0.7; significant SNPs clearly highlighted
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette, CVD-safe, good contrast
• VQ-05: Layout & Canvas (4/4) - Perfect 4800×2700px, well-balanced layout
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units
• VQ-07: Palette Compliance (2/2) - Okabe-Ito colors correct, theme chrome proper, data colors identical across renders

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (4/8) - Well-configured defaults, not exceptional
• DE-02: Visual Refinement (4/6) - Subtle grid, good whitespace, visible customization
• DE-03: Data Storytelling (4/6) - Threshold lines create hierarchy, effective GWAS narrative

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Manhattan plot
• SC-02: Required Features (4/4) - All features present: alternating colors, thresholds, density adaptation, highlight
• SC-03: Data Mapping (3/3) - X/Y correctly assigned
• SC-04: Title & Legend (3/3) - Title format correct, legend appropriately absent

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows peaks, suggestive signals, background noise
• DQ-02: Realistic Context (5/5) - Real scientific GWAS context, neutral
• DQ-03: Appropriate Scale (4/4) - Correct thresholds and data ranges

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow
• CQ-02: Reproducibility (2/2) - Seed set
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: Code Elegance (2/2) - Clean, Pythonic
• CQ-05: Output & API (1/1) - Correct output format

Library Mastery (10/10)

• LM-01: Idiomatic Usage (5/5) - Expert grammar-of-graphics usage
• LM-02: Distinctive Features (5/5) - Uses ggsize(), ggsave(scale=), scale_color_identity()

Score Caps Applied

• None

Strengths

• Perfect technical execution across all visual quality criteria
• Excellent theme-adaptive design with proper color token usage for both light and dark renders
• Realistic, comprehensive GWAS data example with significant peaks and suggestive signals
• Clean, reproducible, idiomatic letsplot code following grammar of graphics idioms
• All specification requirements met with attention to detail

Weaknesses

• Design excellence relies on library defaults rather than unique aesthetic innovation

AI Feedback for Next Attempt

Publication-quality implementation that meets all technical requirements. The plot effectively communicates GWAS results with proper visualization of significance thresholds and chromosome-level variation. Theme adaptation is flawless across both renders.

Verdict: APPROVED