feat(seaborn): implement bar-stacked

[github-actions]

Implementation: bar-stacked - python/seaborn

Implements the python/seaborn version of bar-stacked.

File: plots/bar-stacked/implementations/python/seaborn.py

Parent Issue: #1947

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The stacked bar chart displays six months of sales data (Jan-Jun) with four product categories (Electronics, Clothing, Home & Garden, Sports) stacked in each bar. The background is a warm off-white (#FAF8F1), with dark text labels throughout. The title "bar-stacked · seaborn · anyplot.ai" is bold and prominently displayed. Axis labels are clear: "Month" on the x-axis and "Sales (Thousands $)" on the y-axis. All six categories have tick labels that are easily readable in dark gray/black. The stacked bars use Okabe-Ito colors starting with #009E73 (Electronics, teal/green) as the base, followed by #D55E00 (Clothing, orange), #0072B2 (Home & Garden, blue), and #CC79A7 (Sports, pink). Total value labels ($285K–$435K) sit above each stack in bold dark text and are fully legible. The legend is positioned to the right with a light background, clear title "Product Category", and all text readable. Subtle y-axis grid lines at 10% alpha provide reference without competing with data. All elements are crisp and readable against the light surface. Legibility verdict: PASS

Dark render (plot-dark.png): The same stacked bar chart renders on a warm near-black background (#1A1A17) with light text throughout. The title, axis labels, and tick labels are all in a light off-white color and remain fully readable against the dark surface. Most importantly, the data colors are identical to the light render—#009E73 green, #D55E00 orange, #0072B2 blue, #CC79A7 pink—confirming that only the chrome (background, text, grid, legend frame) has flipped while data integrity is preserved. The total value labels are rendered in light text above the bars and are completely legible. The grid lines remain subtle at 10% alpha. The legend maintains the same structure with light text on the elevated dark background (#242420). No dark-on-dark failures are present; all text, grid, and UI elements are theme-adapted correctly. Legibility verdict: PASS

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	6	10
Total	89	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — Font sizes explicitly set and readable in both themes
• VQ-02: No Overlap (6/6) — All text positioned without collisions; total labels clear
• VQ-03: Element Visibility (6/6) — Bars well-defined with subtle edges; all segments distinguishable
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette; colorblind-safe; good contrast
• VQ-05: Layout & Canvas (4/4) — 16:9 proportions; nothing cut off; generous whitespace
• VQ-06: Axis Labels & Title (2/2) — Proper format with units; descriptive labels
• VQ-07: Palette Compliance (2/2) — First series #009E73; Okabe-Ito order; theme-correct backgrounds; both renders adapted properly

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Intentional use of Okabe-Ito palette; professional appearance but fairly standard seaborn styling
• DE-02: Visual Refinement (4/6) — Spines removed (top/right); subtle grid; generous whitespace; legend custom-styled
• DE-03: Data Storytelling (4/6) — Total labels create hierarchy; seasonal patterns visible; product order intentional; could emphasize insights further

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct stacked bar implementation showing part-to-whole
• SC-02: Required Features (4/4) — All required features present: categories, components, values, totals
• SC-03: Data Mapping (3/3) — Axes correctly mapped; all data visible; range sensible
• SC-04: Title & Legend (3/3) — Correct title format; legend labels match; legend title descriptive

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Demonstrates all aspects: multiple categories and components
• DQ-02: Realistic Context (5/5) — Monthly sales data plausible; product categories realistic; seasonal patterns authentic
• DQ-03: Appropriate Scale (4/4) — Thousands scale sensible; value range realistic

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — No functions/classes; straightforward imperative script
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ensures deterministic output
• CQ-03: Clean Imports (2/2) — Only necessary imports; nothing unused
• CQ-04: Code Elegance (2/2) — Appropriate complexity; no fake UI
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png; current API

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Uses sns.histplot with weights for stacking; sns.set_theme for theme management; mostly idiomatic
• LM-02: Distinctive Features (2/5) — Uses theme system appropriately; palette mapping standard; could leverage more seaborn-specific features

Score Caps Applied

• None

Strengths

• Excellent theme adaptation with proper light/dark text and background colors
• Clean, professional styling with correct Okabe-Ito palette compliance
• Well-positioned total value labels that enhance readability
• Proper handling of categorical data ordering (products by total sales)
• Reproducible code with explicit seed; readable at both sizes
• All text legible in both light and dark renders

Weaknesses

• Design excellence is solid but not exceptional—fairly standard seaborn styling without distinctive visual enhancements
• Library Mastery score reflects good but generic usage—could use more sophisticated techniques like custom styling or advanced seaborn patterns
• Could emphasize the seasonal trends or compositioninsights more through visual hierarchy

Issues Found

None — implementation is solid and production-ready.

AI Feedback for Next Attempt

This is a strong implementation that meets all requirements. Consider for future enhancements: using custom spines styling for a more refined look, or adding a subtle emphasis to highlight the most significant sales contributor or seasonal pattern.

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

[github-actions]

🔧 AI Review Produced No Score — Auto-Retrying

The Claude Code Action ran but didn't write quality_score.txt. Auto-retrying review once...

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🤖 impl-review

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): A stacked bar chart on a warm off-white (#FAF8F1) surface displaying monthly sales across four product categories. The title "bar-stacked · seaborn · anyplot.ai" is clearly visible in bold dark text (fontsize 24). The x-axis label "Month" and y-axis label "Sales (Thousands $)" are both dark text at fontsize 20, fully readable. Six months (Jan–Jun) are spaced evenly on the x-axis. Each bar is composed of four distinct colored segments using the Okabe-Ito palette: green (#009E73) for Electronics at the base, orange (#D55E00) for Clothing, blue (#0072B2) for Home & Garden, and pink (#CC79A7) for Sports. Total value labels ($285K, $300K, $375K, $435K, $435K, $390K) are positioned above each bar in bold 16pt dark text, clearly legible. The legend titled "Product Category" is positioned to the right of the plot in a light (#FFFDF6) box with dark text. Tick labels on both axes are in a softer gray (#4A4A44) at fontsize 16, readable without competing with the data. A subtle y-axis grid (alpha=0.10) is visible. All text is dark on light background with excellent contrast—no readability issues.

Dark render (plot-dark.png): The same stacked bar chart rendered on a warm near-black (#1A1A17) surface. The title, axis labels, tick labels, and total value labels are all rendered in light text (#F0EFE8 for primary, #B8B7B0 for secondary), providing excellent contrast against the dark background. The four product categories maintain identical data colors to the light render (#009E73, #D55E00, #0072B2, #CC79A7)—only the chrome (background, text, grid) has flipped theme-appropriately. The legend box background is dark (#242420) with light text edges. All text elements are clearly readable at full size—no dark-on-dark failures. The grid is rendered in light (rgba(240,239,232,0.10)) for visibility against the dark background. Both renders meet theme-readability standards.

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Score: 84/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	10	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	4	10
Total	84	100

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Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set. Perfect readability in both themes.
• VQ-02: No Overlap (6/6) — All text fully readable, no collisions.
• VQ-03: Element Visibility (6/6) — Bar segments perfectly visible with distinct colors and edges.
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is CVD-safe with good contrast.
• VQ-05: Layout & Canvas (4/4) — 16:9 landscape uses 60–70% of canvas with balanced margins.
• VQ-06: Axis Labels & Title (2/2) — Labels include units ("Sales (Thousands $)"), title format correct.
• VQ-07: Palette Compliance (2/2) — First series #009E73, data colors identical across themes, backgrounds correct.

VISUAL QUALITY: 30/30 ✓

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) — Well-configured defaults with theme tokens and spine removal, but not exceptional custom design.
• DE-02: Visual Refinement (4/6) — Good refinement: spines removed, subtle grid, bar edges. Attention to detail visible.
• DE-03: Data Storytelling (2/6) — Data choice smart but storytelling implicit. No visual emphasis on key insights.

DESIGN EXCELLENCE: 10/20

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct stacked bar chart.
• SC-02: Required Features (4/4) — All features present: stacking, categories, components, total labels.
• SC-03: Data Mapping (3/3) — X/Y axes correctly assigned, all data visible.
• SC-04: Title & Legend (3/3) — Title and legend format correct.

SPEC COMPLIANCE: 15/15 ✓

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Multiple categories, growth trends, seasonality, composition shifts.
• DQ-02: Realistic Context (5/5) — Real, neutral business scenario.
• DQ-03: Appropriate Scale (4/4) — Realistic values, sensible proportions, reproducible (seed 42).

DATA QUALITY: 15/15 ✓

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow, no unnecessary complexity.
• CQ-02: Reproducibility (2/2) — Seed set, deterministic.
• CQ-03: Clean Imports (2/2) — All imports used.
• CQ-04: Code Elegance (2/2) — Clean, Pythonic, no fake functionality.
• CQ-05: Output & API (1/1) — Correct output format and API.

CODE QUALITY: 10/10 ✓

Library Mastery (4/10)

• LM-01: Idiomatic Usage (3/5) — Uses sns.histplot() with categorical stacking (workaround, not idiomatic).
• LM-02: Distinctive Features (1/5) — Generic usage, could replicate in matplotlib.

LIBRARY MASTERY: 4/10

Strengths

• Perfect visual quality: all text explicitly sized and readable in both themes
• Perfect spec compliance and code quality: clean KISS structure, reproducible
• Perfect data quality: realistic business scenario demonstrating growth, seasonality
• Excellent theme implementation: both renders theme-correct with data colors identical
• Well-configured design: spine removal, subtle grid, legend styling show refinement

Weaknesses

• LM-01: sns.histplot() for categorical stacking is unconventional. More idiomatic: sns.barplot() with pivoted data or pandas.DataFrame.plot(stacked=True).
• DE-03: Data storytelling is implicit. Viewer must extract insights (Electronics growth, Clothing seasonality). Consider visual emphasis or annotation.

Verdict: APPROVED ✓

Score 84/100 meets Attempt 2 threshold (≥80). Recommend for merge.