feat(bokeh): implement scatter-marginal

[github-actions]

Implementation: scatter-marginal - python/bokeh

Implements the python/bokeh version of scatter-marginal.

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

Parent Issue: #2005

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (): The plot displays a clean scatter-marginal visualization on a warm off-white background (#FAF8F1). The central scatter plot shows 200 correlated points in Okabe-Ito green (#009E73) with moderate transparency (alpha=0.65), demonstrating a positive correlation between Height (cm) and Weight (kg). The top histogram shows the X-axis distribution with 30 bins in matching green, and the right histogram shows the Y-axis distribution with excellent axis alignment. All typography is clearly readable: the title "scatter-marginal · bokeh · anyplot.ai" in dark ink, axis labels in primary dark text (22pt), and tick labels in soft secondary text (18pt). Grid lines are subtle at 0.10 alpha. The four-panel layout (top histogram + corner, scatter + right histogram) is well-proportioned and balanced. All text is readable and no light-on-light contrast issues are present.

Dark render (): On the warm near-black background (#1A1A17), the same visualization adapts perfectly. The scatter points, histograms, and all chrome elements maintain theme consistency—data colors (Okabe-Ito green) are identical to light render, while the background and text have flipped to light values. Title remains readable in light ink (#F0EFE8), axis labels in light primary text, and tick labels in light secondary text (#B8B7B0). Grid lines are subtle and visible. All elements are clearly legible with no dark-on-dark failures. The layout remains balanced and professional. Both renders demonstrate proper theme-adaptive chrome implementation throughout.

Score: 90/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	13	15
Code Quality	10	10
Library Mastery	9	10
Total	90	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All text properly sized and colored for both themes
• VQ-02: No Overlap (6/6) - Clean spacing, no colliding elements
• VQ-03: Element Visibility (6/6) - Scatter points, histograms, and grid all clearly visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito brand green, single series, accessible
• VQ-05: Layout & Canvas (4/4) - Well-proportioned marginal layout, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - Title format correct, labels descriptive with units
• VQ-07: Palette Compliance (2/2) - First series #009E73, backgrounds correct for both themes

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Solid default implementation with clear design intent
• DE-02: Visual Refinement (4/6) - Subtle grid, removed spines, generous whitespace
• DE-03: Data Storytelling (4/6) - Marginal distributions support narrative of joint relationships

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct scatter plot with marginal distributions
• SC-02: Required Features (4/4) - Top/right marginals, proper alignment
• SC-03: Data Mapping (3/3) - X/Y axes correct, full data range shown
• SC-04: Title & Legend (3/3) - Title format correct, no legend needed for single series

Data Quality (13/15)

• DQ-01: Feature Coverage (6/6) - Shows scatter, X-distribution, Y-distribution
• DQ-02: Realistic Context (4/5) - Height/Weight domain with minor scale consideration
• DQ-03: Appropriate Scale (3/4) - 200 points adequate, value ranges slightly unconventional

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple procedural script, no unnecessary abstraction
• CQ-02: Reproducibility (2/2) - Deterministic with np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports used (os, time, pathlib, numpy, bokeh, selenium)
• CQ-04: Code Elegance (2/2) - No fake functionality, idiomatic Bokeh patterns
• CQ-05: Output & API (1/1) - Outputs plot-{THEME}.png + .html, current API

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Proper figure creation, ColumnDataSource, layout composition
• LM-02: Distinctive Features (4/5) - Good use of Bokeh layouts and range linking

Score Caps Applied

• None applied

Strengths

• Excellent theme-adaptive styling with no readability issues in either render
• Proper use of Bokeh layouts and composition patterns for multi-panel visualization
• Clean, professional typography hierarchy with correct font sizing for 4800×2700px canvas
• Data colors (Okabe-Ito #009E73) are consistently applied and accessible
• Well-aligned marginal histograms that enhance understanding of distributions
• Deterministic data generation with clear structure and proper Selenium export setup

Weaknesses

• Design excellence is solid but not exceptional—meets all requirements without distinctive refinement
• Minor domain realism consideration: Height values (20-80cm range) are unconventional for Height(cm) variable

Issues Found

None blocking approval. Implementation fully meets specification.

AI Feedback for Next Attempt

N/A — approved on first attempt.

Verdict: APPROVED