feat(letsplot): implement scatter-marginal

[github-actions]

Implementation: scatter-marginal - python/letsplot

Implements the python/letsplot version of scatter-marginal.

File: plots/scatter-marginal/implementations/python/letsplot.py

Parent Issue: #2005

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Displays a scatter plot on warm off-white background (#FAF8F1) with 200 data points in brand green (#009E73) showing positive correlation between Measurement A and B. Title "scatter-marginal · letsplot · anyplot.ai" is dark and clearly readable. Axis labels in dark text are prominent. Top marginal histogram shows X distribution with semi-transparent green histogram bars (alpha=0.5) and orange vermillion KDE line overlay (#D55E00). Right marginal shows Y distribution with same color scheme, flipped orientation. Grid lines are subtle and visible. All text elements including tick labels in soft gray (#4A4A44) are readable against the light background with no contrast issues.

Dark render (plot-dark.png): Same composition on warm near-black background (#1A1A17). Title and axis labels in light text (#F0EFE8) are clearly visible. All tick labels in soft light gray (#B8B7B0) are readable with no dark-on-dark contrast failures. Scatter points remain in identical brand green (#009E73) as light render, confirming data colors are preserved across themes. Marginal distributions show identical colors and pattern. Grid lines are subtle and visible. Both renders demonstrate proper theme adaptation with correct color tokens applied throughout.

Score: 89/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	89	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - Font sizes match spec (24pt title, 20pt axis labels, 16pt tick labels), all readable in both themes
• VQ-02: No Overlap (6/6) - All text readable, no overlapping elements
• VQ-03: Element Visibility (6/6) - All scatter points, histogram bars, KDE lines clearly visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette (#009E73, #D55E00), colorblind-safe
• VQ-05: Layout & Canvas (4/4) - Good proportions, well-organized layout via ggbunch
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with context
• VQ-07: Palette Compliance (2/2) - First series #009E73, backgrounds #FAF8F1/#1A1A17 correct, both renders theme-correct

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Thoughtful color and transparency choices but relatively standard implementation
• DE-02: Visual Refinement (4/6) - Uses theme_minimal, subtle grid, appropriate whitespace but minimal customization
• DE-03: Data Storytelling (4/6) - Marginal distributions provide context but visual hierarchy is functional

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct scatter plot with marginal distributions
• SC-02: Required Features (4/4) - All features present: scatter, top marginal, right marginal, KDE overlay
• SC-03: Data Mapping (3/3) - X/Y mapped correctly, axes aligned
• SC-04: Title & Legend (3/3) - Correct title format and descriptive labels

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Comprehensive coverage of scatter plot with marginals
• DQ-02: Realistic Context (5/5) - Bivariate normal data with correlation, neutral
• DQ-03: Appropriate Scale (4/4) - Sensible value ranges

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple, direct implementation
• CQ-02: Reproducibility (2/2) - np.random.seed(42) for determinism
• CQ-03: Clean Imports (2/2) - Only necessary imports
• CQ-04: Code Elegance (2/2) - No fake UI
• CQ-05: Output & API (1/1) - Correct output format (plot-{THEME}.png/.html)

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Good idiomatic lets-plot API usage
• LM-02: Distinctive Features (4/5) - Uses ggbunch for composite visualization, KDE overlay

Score Caps Applied

• None

Strengths

• Perfect spec compliance with all required features implemented (scatter plot + marginal distributions with KDE overlay)
• Excellent theme adaptation across both light and dark renders with proper color tokens and readable text in both contexts
• Professional use of Okabe-Ito palette with thoughtful accent color choices (brand green #009E73 + vermillion #D55E00)
• Clean, organized layout using ggbunch with intentional transparency values (alpha 0.5, 0.65, 0.8) that reveal data patterns
• Idiomatic lets-plot code with proper use of high-level API functions and correct output format

Weaknesses

• Design excellence scores baseline - uses theme_minimal() and standard customization approach without exceptional visual embellishments

Issues Found

1. DE-01 BASELINE: Generic styling with standard theme_minimal() approach
   • Fix: Consider custom spine handling, refined typography, or emphasized visual hierarchy for next implementation

AI Feedback for Next Attempt

Strong technical execution with perfect spec compliance. Design excellence is solid but uses baseline styling. Future refinements could include: custom spine styling (keeping only specific axes), emphasized visual hierarchy through strategic color use or data emphasis, refined typography with intentional weight variations, or more distinctive visual treatment of marginal plot scales.

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): The plot displays a scatter plot with marginal distributions on a warm off-white background (#FAF8F1). The main scatter plot shows 200 points in bluish green (#009E73, Okabe-Ito position 1) with transparency (alpha=0.65) revealing the positive correlation between Measurement A (x-axis) and Measurement B (y-axis). The top marginal histogram shows the x-distribution with green histogram bars (alpha=0.5) overlaid with an orange KDE curve (#D55E00, Okabe-Ito position 2). The right marginal histogram shows the y-distribution with identical styling. The title "scatter-marginal · letsplot · anyplot.ai" is displayed in dark ink (#1A1A17), axis labels are in the same dark color (explicitly 20pt), and tick labels are in softer ink (#4A4A44, 16pt). All text is clearly readable against the light background with excellent contrast. Grid lines are subtle and do not distract. Legibility verdict: PASS — all elements are readable in both high contrast and at full resolution.

Dark render (plot-dark.png): The same plot renders on a warm near-black background (#1A1A17). The scatter points remain the identical bluish green (#009E73), confirming theme-independent data color integrity. All text (title, axis labels, tick labels) is now in light colors (#F0EFE8 for primary, #B8B7B0 for secondary) with excellent contrast against the dark background — no dark-on-dark failures. The marginal histograms maintain their green bars and orange KDE curves, identical to the light render. Grid lines remain subtle. The plot backgrounds, text, and elements all adapt correctly to the dark theme while preserving data color fidelity. Legibility verdict: PASS — all text is readable with no dark-on-dark issues; data colors are identical to light render.

Score: 95/100

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	9	10
Total	95	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All sizes explicitly set (title 24pt, labels 20pt, ticks 16pt), perfectly readable in both themes
• VQ-02: No Overlap (6/6) - No overlapping text elements, all content fully readable
• VQ-03: Element Visibility (6/6) - 200 markers with alpha=0.65 perfectly adapted to density, KDE curves visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette CVD-safe, good contrast, no red-green sole signal
• VQ-05: Layout & Canvas (4/4) - Perfect composition with marginals aligned via ggbunch, balanced margins, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - Title format correct, axis labels descriptive (Measurement A/B)
• VQ-07: Palette Compliance (2/2) - Primary series #009E73 (correct), backgrounds #FAF8F1/#1A1A17 (correct), theme-adaptive chrome perfect in both renders

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Theme tokens throughout, intentional transparency hierarchy, professional polish — above defaults but not FiveThirtyEight level
• DE-02: Visual Refinement (5/6) - Spines removed, subtle grid (INK_SOFT 0.2), generous whitespace, clean minimal aesthetic
• DE-03: Data Storytelling (5/6) - Clear focal point in main scatter, visual hierarchy guides reader through title → plot → margins, correlation pattern and distributions evident

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct scatter plot with marginal distributions, histograms + KDE overlay per spec
• SC-02: Required Features (4/4) - Main scatter, top marginal (x), right marginal (y), aligned axes, histogram+KDE, transparency all present
• SC-03: Data Mapping (3/3) - X/Y correctly assigned, all 200 points visible, axes show full data range
• SC-04: Title & Legend (3/3) - Title format correct "scatter-marginal · letsplot · anyplot.ai", legend appropriately omitted for single-series

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows all aspects: bivariate relationship, x-distribution shape, y-distribution shape, both with histogram+KDE
• DQ-02: Realistic Context (5/5) - Real-world plausible bivariate measurements with positive correlation, neutral scenario
• DQ-03: Appropriate Scale (4/4) - Values sensible (x: ~4-16, y: ~2-17), bivariate normal with realistic correlation evident

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No functions/classes, straightforward imports→data→plot→save
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set, deterministic output
• CQ-03: Clean Imports (2/2) - Only used imports (os, numpy, pandas, lets_plot)
• CQ-04: Code Elegance (2/2) - Pythonic, appropriate complexity, no fake functionality
• CQ-05: Output & API (1/1) - Saves plot-{THEME}.png and plot-{THEME}.html correctly

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Expert ggplot2-style grammar, proper aes(), geom_, theme_, ggbunch() composition, scale_*_continuous() axis config
• LM-02: Distinctive Features (4/5) - Uses letsplot's ggbunch() for multi-plot composition, theme tokens for adaptive rendering, export capabilities

Score Caps Applied

• None — no caps apply

Strengths

• Perfect visual quality: All text legible in both themes, no overlap, optimal element visibility
• Exemplary palette compliance: Correct Okabe-Ito colors, perfect theme adaptation (light/dark), background colors correct
• Complete specification compliance: All required features present and correctly implemented
• Expert idiomatic usage: ggplot2-style grammar used perfectly, high-level API throughout
• Theme-aware architecture: Theme tokens used consistently throughout, both renders render correctly
• Strong visual storytelling: Clear hierarchy guides viewer from title to main insight to marginal context
• Reproducible & deterministic: Seed set for consistent output across runs

Weaknesses

• None identified

Verdict: APPROVED