feat(matplotlib): implement scatter-animated-controls

[github-actions]

Implementation: scatter-animated-controls - matplotlib

Implements the matplotlib version of scatter-animated-controls.

File: plots/scatter-animated-controls/implementations/matplotlib.py

Parent Issue: #3067

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 2x2 grid of scatter plots showing "GDP vs Life Expectancy Over Time" for 8 countries across 4 key years (2000, 2006, 2013, 2019). Each panel features bubble markers where size represents population. Colors include blue (#306998), yellow (#FFD43B), orange (#E69F00), cyan (#56B4E9), green (#009E73), lime (#F0E442), dark blue (#0072B2), and dark orange (#D55E00). Each panel has a large watermark year in the background (gray, low opacity). The main title "scatter-animated-controls · matplotlib · pyplots.ai" is at the top with an italic subtitle. A legend at the bottom shows all 8 countries, and a stylized "▶ Play | || Pause | Timeline" indicator appears in the bottom right with a yellow background. Axes show GDP per Capita (thousands $) on X and Life Expectancy (years) on Y. Country labels (D, F, B, H, C) appear inside larger bubbles.

Quality Score: 88/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (8/10) - Title at 24pt, axis labels at 14pt (below recommended 20pt), tick labels at 12pt (below 16pt). Text is readable but not optimal for 4800×2700.
• VQ-02: No Overlap (8/8) - No overlapping text, all labels clearly visible
• VQ-03: Element Visibility (7/8) - Bubble sizes are appropriate for data density, alpha 0.7 works well, but some smaller countries are hard to distinguish
• VQ-04: Color Accessibility (4/5) - Uses a colorblind-friendly palette, but yellow (#FFD43B) and lime (#F0E442) may be hard to distinguish
• VQ-05: Layout Balance (5/5) - Good 2x2 grid layout, plots fill canvas well, legend properly positioned
• VQ-06: Axis Labels (2/2) - Descriptive with units: "GDP per Capita (thousands $)", "Life Expectancy (years)"
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha 0.3), but legend at bottom is slightly cramped with 8 items

Spec Compliance (22/25 pts)

• SC-01: Plot Type (8/8) - Correct: faceted scatter plot as static alternative for animation
• SC-02: Data Mapping (5/5) - X=GDP, Y=Life Expectancy, Size=Population correctly mapped
• SC-03: Required Features (4/5) - Shows time evolution via facets, includes play control indicator, but lacks actual interactivity (expected for static matplotlib)
• SC-04: Data Range (3/3) - Consistent axis limits (0-80 GDP, 50-88 Life Expectancy) across all panels
• SC-05: Legend Accuracy (2/2) - All 8 countries correctly labeled
• SC-06: Title Format (0/2) - Title format is correct but uses "·" separator correctly: "scatter-animated-controls · matplotlib · pyplots.ai" ✓

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows evolution over time, various bubble sizes, different growth trajectories. Could show more diverse starting points.
• DQ-02: Realistic Context (7/7) - Excellent Gapminder-inspired scenario with plausible GDP/life expectancy relationship
• DQ-03: Appropriate Scale (4/5) - GDP 5-50k and life expectancy 55-85 are realistic, but some growth rates are aggressive

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean script: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses subplots for faceting, annotate for labels, text for watermarks. Could leverage FuncAnimation for actual animation.

Strengths

• Excellent Gapminder-inspired data storytelling with realistic GDP/life expectancy evolution
• Creative static interpretation of animation via 2x2 faceted time series
• Good use of watermark years in background for visual impact
• Clean colorblind-friendly palette with white edge outlines on bubbles
• Well-organized KISS code structure with clear data generation

Weaknesses

• Font sizes are below recommended guidelines (labels 14pt vs 20pt, ticks 12pt vs 16pt)
• Yellow (#FFD43B) and lime (#F0E442) colors are very similar, may cause confusion
• Play/Pause indicator is purely decorative rather than functional matplotlib widgets

Verdict: REJECTED

Rationale: Score of 88 is below the 90-point threshold for immediate approval. The main issues are suboptimal font sizes for the 4800×2700 canvas and similar yellow colors. These are minor fixes that can be addressed in the repair loop.

[github-actions]

🔧 Repair Attempt 1/3

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

The plot displays a 2×2 faceted scatter plot showing GDP per Capita vs Life Expectancy across four time points (2000, 2006, 2013, 2019). Each panel features 8 countries represented as colored bubbles where size encodes population. The main title "scatter-animated-controls · matplotlib · pyplots.ai" appears at the top with a gray italic subtitle explaining the visualization. Each panel has a large watermark year in the background (gray, 15% alpha). The color palette includes blue (#306998), yellow/orange (#E69F00), pink (#CC79A7), light blue (#56B4E9), green (#009E73), dark orange (#D55E00), darker blue (#0072B2), and purple (#882255). A legend at the bottom shows all 8 countries in a 4-column layout. Below the legend, there's a play/pause control panel with a timeline slider showing range 2000-2019 with current position at 2015.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, axis labels 20pt, tick labels 16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, country labels inside large bubbles don't conflict
• VQ-03: Element Visibility (7/8) - Bubbles are well-sized and visible, alpha=0.7 appropriate; minor deduction as some smaller bubbles could be slightly larger
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette avoiding red-green conflicts
• VQ-05: Layout Balance (4/5) - Good 2×2 grid layout with consistent axis limits; slight deduction for tight spacing between legend and control panel
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "GDP per Capita (thousands $)", "Life Expectancy (years)"
• VQ-07: Grid & Legend (0/2) - Legend well-placed but control panel partially obscures the legend area; the legend text "▶ Play | || Pause | --- Timeline" shown in bottom right corner looks garbled/corrupted

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct Gapminder-style bubble chart with faceted time evolution
• SC-02: Data Mapping (5/5) - X=GDP, Y=Life Expectancy, Size=Population correctly mapped
• SC-03: Required Features (4/5) - Has play/pause buttons and timeline slider as static representation; missing trail visualization option mentioned in spec
• SC-04: Data Range (3/3) - Consistent axes 0-80 GDP, 50-88 Life Expectancy across all panels
• SC-05: Legend Accuracy (2/2) - All 8 countries correctly labeled
• SC-06: Title Format (1/2) - Uses correct format but main title position could be better integrated

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows temporal evolution well; countries show varying growth rates and trajectories
• DQ-02: Realistic Context (7/7) - Gapminder-inspired country development data is highly realistic and neutral
• DQ-03: Appropriate Scale (5/5) - GDP 5-45K, Life expectancy 55-85 years, Population 25-200M are all realistic

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only matplotlib and numpy used
• CQ-04: No Deprecated API (1/1) - Modern matplotlib API used
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of subplots, annotations, custom axes for controls; could leverage FuncAnimation or more advanced matplotlib features

Strengths

• Excellent Gapminder-style visualization with clear temporal storytelling across 4 key time points
• Play/pause controls and timeline slider visually represented, showing the intended interactivity concept
• Colorblind-safe palette with good distinction between 8 countries
• Prominent year watermarks in each panel enhance time-context awareness
• Clean code structure following KISS principles with realistic country development data

Weaknesses

• The control panel rendering shows some visual artifacts in the final image (timeline legend text appears garbled as "▶ Play | || Pause | --- Timeline")
• Missing optional trail visualization to show entity paths over time as mentioned in spec notes

Verdict: APPROVED