feat(plotly): implement manhattan-gwas

[github-actions]

Implementation: manhattan-gwas - python/plotly

Implements the python/plotly version of manhattan-gwas.

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

Parent Issue: #2925

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): A Manhattan plot displayed on a warm off-white background (#FAF8F1) showing GWAS results across all chromosomes. The plot uses alternating green (#009E73) and orange (#D55E00) for chromosome regions, creating a clear visual rhythm. The x-axis displays chromosome labels (1-22) centered below their genomic regions. The y-axis shows -log₁₀(p-value) with subtle gridlines. Two horizontal dashed threshold lines are annotated: the genome-wide significance threshold at ~7.3 and the suggestive threshold at 5. Significant SNPs are highlighted as orange diamond markers with white edges, standing out above the genome-wide significance line. The title "manhattan-gwas · plotly · pyplots.ai" is clearly visible in dark text. All text elements—title, axis labels, tick labels—are legible against the light background with no readability issues. The layout is clean with appropriate margins and spacing.

Dark render (plot-dark.png): The same plot rendered on a warm near-black background (#1A1A17). All structural elements remain identical: the alternating chromosome colors (green and orange) are exactly the same as the light render, confirming only chrome has flipped between themes. Title and all text elements are now rendered in light colors and are fully readable against the dark background—no "dark-on-dark" failures. The grid, threshold lines, and annotations are all visible with proper contrast. The significant SNP diamonds maintain their orange color with white edges, standing out clearly against the dark canvas. Both renders demonstrate proper theme adaptation with accessible contrast throughout.

Score: 88/100

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	6	10
Total	88	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - Font sizes properly scaled (28px title, 22px labels, 18px ticks); all readable in both themes
• VQ-02: No Overlap (6/6) - Clean spacing, no text collisions, annotations positioned with yshift
• VQ-03: Element Visibility (6/6) - Opacity 0.7 appropriate for dense scatter; significant SNPs highlighted with diamonds
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette (#009E73, #D55E00) is CVD-safe
• VQ-05: Layout & Canvas (4/4) - 4800×2700 px output; margins 80/50/80/80
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with proper notation (-log₁₀)
• VQ-07: Palette Compliance (2/2) - First categorical #009E73; backgrounds #FAF8F1/#1A1A17; both renders theme-correct

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) - Professional polish, proper theme adaptation, intentional color alternation for chromosomes
• DE-02: Visual Refinement (3/6) - Subtle grid styling, generous whitespace; straightforward customization
• DE-03: Data Storytelling (4/6) - Threshold lines guide the eye, peaks clearly emphasized, visual rhythm from alternating colors

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Manhattan plot with cumulative positions and -log10 p-values
• SC-02: Required Features (4/4) - Alternating chromosome colors, genome-wide and suggestive thresholds, significant SNPs highlighted
• SC-03: Data Mapping (3/3) - X-axis cumulative position, Y-axis -log10 p-value, all data visible
• SC-04: Title & Legend (3/3) - Title format correct; legend shows "Significant SNPs"; thresholds annotated

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Simulated realistic GWAS with peaks on chromosomes 2, 8, 15; main scatter + significant SNPs
• DQ-02: Realistic Context (5/5) - Realistic chromosome lengths, p-value distribution with noise and peaks, no controversial data
• DQ-03: Appropriate Scale (4/4) - Y-axis range appropriate; standard thresholds (7.3, 5) correctly applied

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No functions/classes; straight implementation
• CQ-02: Reproducibility (2/2) - np.random.seed(42) ensures deterministic output
• CQ-03: Clean Imports (2/2) - Only used imports: os, numpy, pandas, plotly.graph_objects
• CQ-04: Code Elegance (2/2) - Appropriate complexity; no fake UI
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html correctly

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Proper go.Scatter, add_shape for thresholds, add_annotation for labels, sophisticated hover templates
• LM-02: Distinctive Features (2/5) - Hover tooltips with customdata; interactive HTML export; mostly standard usage

Score Caps Applied

• None

Strengths

• Excellent theme adaptation with proper token usage throughout both renders
• Clean, professional GWAS visualization meeting all specification requirements
• Realistic synthetic data with interpretable peaks and threshold annotations
• Proper use of plotly features (manual ticks, shapes, annotations, hover tooltips)
• Readable text and markers across both light and dark themes

Weaknesses

• Design is straightforward without exceptional polish or custom touches beyond requirements
• Limited use of advanced plotly features (could leverage more interactive capabilities)

Issues Found

• None — implementation meets all requirements and quality standards

AI Feedback for Next Attempt

This is a solid, professional implementation. No fixes needed. The visualization correctly implements the GWAS Manhattan plot specification with proper theme adaptation, clear data storytelling through thresholds and peaks, and clean code. Consider it complete.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 1/4

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

Light render (plot-light.png): Manhattan plot rendered on warm off-white (#FAF8F1) surface. Alternating bluish-green (#009E73) and vermillion (#D55E00) blocks represent 22 chromosomes along the x-axis with cumulative genomic position. Dense scatter points (size 5, opacity 0.7) show -log₁₀(p-values) for each SNP. Dashed horizontal line at -log₁₀(5×10⁻⁸) ≈ 7.3 marks genome-wide significance threshold; dotted line at 5 marks suggestive threshold. Significant SNPs above the threshold are highlighted with larger orange diamond markers (size 10) with white borders, creating clear focal points on chromosomes 2, 8, and 15. All text (title, axis labels, tick labels at 28/22/18px) is clearly readable against the light background. Y-axis grid is subtle and visible. Legend shows "Significant SNPs" in top left.

Dark render (plot-dark.png): Same plot composition on warm near-black (#1A1A17) surface. Data colors are IDENTICAL to light render—same green and orange for chromosome alternation, confirming theme-independent data encoding. All chrome (text, grid, legend background) adapts to dark theme: primary text in #F0EFE8, secondary in #B8B7B0, grid in RGBA with light opacity. Threshold lines render in theme-adaptive color (lighter than light theme). All text remains clearly readable against dark background—no dark-on-dark failures; axis labels, tick labels, and annotations all have sufficient contrast. Scatter points and diamond markers are as distinct as in light render.

Both renders pass legibility checks: light text on light background in light theme, light text on dark background in dark theme. Data colors (green and orange) are perceptually identical across both, confirming proper theme adaptation.

Score: 89/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	15	20
Spec Compliance	12	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	89	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) - All sizes explicitly set (title 28px, labels 22px, ticks 18px); perfect readability in both light and dark themes
• VQ-02: No Overlap (6/6) - No collisions; chromosome labels, annotations, and legend well-spaced
• VQ-03: Element Visibility (6/6) - Dense scatter (size 5, opacity 0.7) appropriately handled; significant SNPs at size 10 clearly stand out
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is CVD-safe; good contrast; adequate alpha for overlaps
• VQ-05: Layout & Canvas (3/4) - Good proportions and balanced margins; could use marginally more generous whitespace
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with units ("-log₁₀(p-value)") and context ("Chromosome")
• VQ-07: Palette Compliance (2/2) - First categorical series is #009E73 ✓; second is #D55E00 ✓; backgrounds #FAF8F1 (light) and #1A1A17 (dark) correct ✓; text/grid colors theme-adaptive in both ✓

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (5/8) - Intentional palette selection, professional theme tokens, well-implemented light/dark adaptation; above library defaults but not Nature-publication level
• DE-02: Visual Refinement (5/6) - Top/right spines removed (L-shaped frame) ✓; subtle grid on y-axis ✓; generous margins ✓; legend styled with border
• DE-03: Data Storytelling (5/6) - Clear visual hierarchy: alternating colors guide chromosome tracking, size variation (significant SNPs larger) creates emphasis, threshold annotations explain significance. Viewer immediately identifies peaks.

Spec Compliance (12/15)

• SC-01: Plot Type (5/5) - Correct Manhattan plot with cumulative genomic position, alternating chromosome colors
• SC-02: Required Features (4/4) - Alternating colors ✓; genome-wide significance threshold ✓; suggestive threshold ✓; significant SNPs highlighted ✓
• SC-03: Data Mapping (3/3) - X: cumulative position across chromosomes ✓; Y: -log₁₀(p) ✓; chromosome labels centered; all data visible
• SC-04: Title & Legend (1/3) - DEFECT: Title reads "manhattan-gwas · plotly · pyplots.ai" but should be "manhattan-gwas · plotly · anyplot.ai". Legend labels correct.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Displays all GWAS plot aspects: multiple chromosomes, significant and background SNPs, realistic peak patterns on chromosomes 2/8/15
• DQ-02: Realistic Context (5/5) - Plausible simulated GWAS data with authentic p-value distributions, neutral scientific context
• DQ-03: Appropriate Scale (4/4) - Chromosome lengths match human genome; -log₁₀ scale correct for GWAS (significance at 7.3 ≈ 5×10⁻⁸); all proportions factually sound

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → theme config → data generation → figure → save
• CQ-02: Reproducibility (2/2) - Seed 42 set; fully deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports (os, sys, numpy, pandas, plotly)
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, no over-engineering
• CQ-05: Output & API (1/1) - Correct output names (plot-{THEME}.png/.html); current Plotly 6.7.0 API

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Expert Plotly patterns: go.Figure(), add_trace(), add_shape(), add_annotation(), hovertemplate with customdata; all idiomatic
• LM-02: Distinctive Features (3/5) - Uses Plotly interactive hover tooltips, theme-aware paper/plot backgrounds; good library-specific usage

Score Caps Applied

• None (no critical failures that trigger caps)

Strengths

• Clean theme adaptation with proper token usage; both light and dark renders are production-ready and readable
• Proper use of Okabe-Ito palette with correct brand green (#009E73) and alternating orange; data colors stable across themes
• Clear data storytelling through visual hierarchy: size variation (significant SNPs as larger diamonds), color (alternating chromosomes), and annotation (threshold labels)
• Excellent code quality: KISS structure, deterministic seed, idiomatic Plotly patterns
• Strong spec compliance: all required GWAS features present (thresholds, highlights, proper axes), realistic data with authentic peaks
• Expert library mastery: interactive hover tooltips, proper axis customization, professional layout

Weaknesses

• Critical: Title contains "pyplots.ai" instead of "anyplot.ai" — Violates spec format requirement. Fix: Line 221, change title text to "manhattan-gwas · plotly · anyplot.ai"

Issues Found

1. SC-04 CRITICAL: Title domain incorrect
   • Current: "manhattan-gwas · plotly · pyplots.ai"
   • Expected: "manhattan-gwas · plotly · anyplot.ai"
   • Fix: Update line 221 in plotly.py to use correct domain

AI Feedback for Next Attempt

Fix the title domain from "pyplots.ai" to "anyplot.ai" (line 221). This is a one-line change that will bring the score to 90/100 and meet publication standards. The rest of the implementation is excellent.

Verdict: APPROVED

This implementation meets the Attempt 2/3 threshold (≥80). The single defect (title domain) is trivial to fix and should be corrected before final merge.