feat(plotnine): implement scatter-basic

[github-actions]

Implementation: scatter-basic - python/plotnine

Implements the python/plotnine version of scatter-basic.

File: plots/scatter-basic/implementations/python/plotnine.py

Parent Issue: #611

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot renders on a warm off-white #FAF8F1 background. The title "scatter-basic · plotnine · anyplot.ai" appears in dark ink (#1A1A17) at the top-left in a clear 24pt typeface. Axis labels — "Study Hours per Week" (X) and "Exam Score (points)" (Y) — are rendered in dark ink at 20pt and are clearly readable. Tick labels along both axes use the secondary #4A4A44 ink at 16pt and are fully legible. Data: 150 teal-green points (#009E73) with alpha=0.7 and size=4, depicting a moderate positive correlation between study hours and exam scores. No tick marks are shown; only subtle major grid lines are visible. An L-shaped axis frame (bottom + left) defines the plot area. All text is readable against the light background. No light-on-light text issues.

Dark render (plot-dark.png): The same layout renders on a warm near-black #1A1A17 background. The title appears in light ink (#F0EFE8), clearly readable against the dark surface. Axis labels and tick labels are rendered in #B8B7B0, legible against the dark background without any dark-on-dark failure. Data points are identical in color (#009E73) and position to the light render — only the chrome (background, text, grid, axis lines) has flipped. The brand green reads well on the dark surface. Subtle grid lines remain visible. No dark-on-dark text issues detected.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 89/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	89	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All text sizes explicitly set (24pt title, 20pt axis, 16pt ticks); fully readable in both light and dark themes
• VQ-02: No Overlap (6/6) — No text collisions; 150-point scatter with alpha=0.7 avoids visual clutter
• VQ-03: Element Visibility (5/6) — Markers clearly visible in both themes, but stroke=0.4 uses same brand green as fill — white edge stroke would improve definition in the dense 1–4 hour region
• VQ-04: Color Accessibility (2/2) — Single series uses #009E73 (Okabe-Ito Add workflow diagram for new prototype discovery #1), CVD-safe, no red-green sole-signal issue
• VQ-05: Layout & Canvas (4/4) — 16:9 landscape, generous whitespace, data fully within bounds, nothing clipped
• VQ-06: Axis Labels & Title (2/2) — "Study Hours per Week" and "Exam Score (points)" are descriptive and include contextual units
• VQ-07: Palette Compliance (2/2) — Single series = #009E73 ✓; backgrounds #FAF8F1 (light) / #1A1A17 (dark) ✓; both renders theme-correct ✓

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (6/8) — Above defaults: brand color, explicit spine removal (panel_border=element_blank()), tick marks removed, left-aligned title (ha="left"), typography hierarchy fully specified. Not exceptional — no layered emphasis or annotation.
• DE-02: Visual Refinement (4/6) — Good refinement: panel border removed, minor grid blank, axis ticks blank, L-frame axis lines. Generous canvas.
• DE-03: Data Storytelling (3/6) — The study-hours/exam-scores context creates a natural narrative and the positive correlation is evident, but no design-intentional focal point (e.g., trend line, outlier annotation, or regression guide).

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D scatter plot
• SC-02: Required Features (4/4) — alpha=0.7 ✓, grid lines ✓, axis labels ✓, appropriate point size ✓
• SC-03: Data Mapping (3/3) — X = study hours, Y = exam scores; 150 points within the 50–500 spec range; moderate positive correlation visible
• SC-04: Title & Legend (3/3) — Title is exactly scatter-basic · plotnine · anyplot.ai ✓; no legend (correct for single series) ✓

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Demonstrates correlation, spread, noise, and a visible outlier (score ~19 at ~1.5 hours); covers all aspects of a basic scatter plot
• DQ-02: Realistic Context (5/5) — Study hours vs exam scores: real-world plausible, educationally neutral, no controversial content
• DQ-03: Appropriate Scale (4/4) — Study hours 1–10, exam scores 15–100; both ranges are domain-sensible

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat script, no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ✓
• CQ-03: Clean Imports (2/2) — All imported names are used; no dead imports
• CQ-04: Code Elegance (2/2) — Appropriate complexity, clean grammar-of-graphics composition, no fake UI
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png using f-string; verbose=False ✓

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Correct grammar-of-graphics pattern: ggplot + aes + geom_point + scale + labs + theme_minimal + theme; element_* functions used idiomatically. Slightly below full marks as no advanced API features demonstrated.
• LM-02: Distinctive Features (3/5) — Uses scale_x_continuous/scale_y_continuous for explicit axis control and theme() layering. The layered ggplot2-style composition is plotnine's signature, but no distinctive features like stat_smooth, facet_wrap, or position_jitter are used.

Score Caps Applied

• None

Strengths

• Full theme-adaptive chrome: both renders are visually correct with proper backgrounds, text tokens, and grid opacity
• Perfect spec compliance: all required features (alpha, grid, labels, point size) implemented correctly
• Flat, reproducible code with seed, clean imports, and correct output filenames
• Realistic and neutral educational dataset with clear correlation signal

Weaknesses

• Marker stroke color is same as fill (color=BRAND, fill=BRAND, stroke=0.4) — no white edge to differentiate overlapping points in the dense 1–4 hour region; style guide recommends white edges for overlapping scatter markers
• Design excellence is functional but not elevated — no trend line, no annotated outlier, no focal emphasis; a geom_smooth or reference annotation would meaningfully improve storytelling
• Library mastery doesn't showcase plotnine-distinctive features; stat_smooth(method='lm') would demonstrate grammar-of-graphics stat layer capability

Issues Found

1. VQ-03 MINOR: Marker edges are same color as fill; overlapping points in dense region lack visual separation
   • Fix: Add stroke=0.5 with a contrasting edge, e.g., use shape='o' with white edge or reduce fill alpha while keeping higher stroke opacity
2. DE-03 LOW: No visual storytelling — correlation is visible but not emphasized
   • Fix: Add stat_smooth(method='lm', color='#D55E00', fill='#D55E00', alpha=0.15) for a regression band, or annotate the outlier point

AI Feedback for Next Attempt

The core implementation is solid: theme tokens, colors, and code structure are all correct. To improve: (1) add a white or contrasting edge to scatter markers to improve definition in dense areas — use color='white', fill=BRAND or similar shape with explicit edge color; (2) add + stat_smooth(method='lm', color='#D55E00', fill='#D55E00', alpha=0.15, size=1) to show the regression trend and demonstrate plotnine's stat layer capability; (3) optionally annotate the visible outlier near (1.5, 19) to create a clear focal point.

Verdict: APPROVED