update(heatmap-basic): plotnine — comprehensive quality review

[MarkusNeusinger]

Summary

Updated plotnine implementation for heatmap-basic.

Changes: Comprehensive quality review — fix weaknesses from prior reviews, preserve strengths, improve quality across all dimensions.

Changes

• Addressed review weaknesses from prior quality assessment
• Improved data choice and visual design
• Enhanced library-specific feature usage
• Updated to current library and Python versions
• Quality self-assessment: pending CI review

Test Plan

• Preview images uploaded to GCS staging
• Implementation file passes ruff format/check
• Metadata YAML updated with current versions
• Automated review triggered

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Generated with Claude Code /update command

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays an 8×8 heatmap titled "Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai". Eight departments (Engineering, Marketing, Sales, Finance, Operations, HR, Research, Support) are on the Y-axis and eight quarters (Q1 '23 through Q4 '24) on the X-axis. Each cell is colored using a diverging blue-white-yellow gradient: blue (#306998) for negative growth, cream/white near zero, and yellow (#FFD43B) for positive growth. Every cell contains a bold annotation showing the growth percentage with a +/- sign. Text color adapts — white on dark blue cells, dark gray (#333) on lighter cells. White borders separate tiles. A colorbar on the right labeled "Growth (%)" ranges from approximately -20 to +20. X-axis labels are rotated 45 degrees. The data tells a clear recovery story: left columns (2023) are predominantly blue (negative), transitioning to yellow (positive) on the right (2024), with varying recovery rates across departments.

Quality Score: 89/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (6/8) - All text readable; title is 22pt (below recommended 24pt), axis text 16pt meets tick label standard, annotations are bold and clear
• VQ-02: No Overlap (6/6) - No overlapping text elements anywhere
• VQ-03: Element Visibility (6/6) - Tiles well-sized, annotations clearly visible in every cell
• VQ-04: Color Accessibility (4/4) - Blue-to-yellow diverging scheme is colorblind-safe
• VQ-05: Layout Balance (3/4) - Good proportions, slight extra whitespace at bottom from rotated labels
• VQ-06: Axis Labels & Title (1/2) - Axes use empty string labels (x="", y=""); colorbar has units "Growth (%)" but axes themselves lack labels

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Custom Python Blue/Yellow diverging palette, conditional text coloring, white tile borders — clearly above defaults
• DE-02: Visual Refinement (5/6) - Grid removed, minimal theme, white backgrounds, tile borders provide clean structure
• DE-03: Data Storytelling (4/6) - Clear temporal recovery narrative visible through color progression left-to-right; departmental variation adds depth

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap using geom_tile
• SC-02: Required Features (4/4) - Diverging colormap ✓, value annotations ✓, colorbar legend ✓, logical ordering ✓
• SC-03: Data Mapping (3/3) - Quarters on X, departments on Y, growth as fill
• SC-04: Title & Legend (3/3) - Title format "{spec-id} · {library} · pyplots.ai" correct, colorbar labeled

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive and negative values, varying magnitudes, clear temporal and departmental patterns
• DQ-02: Realistic Context (5/5) - Quarterly departmental growth rates — realistic business scenario
• DQ-03: Appropriate Scale (4/4) - Growth rates -22% to +29% are plausible for quarterly metrics

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save
• CQ-02: Reproducibility (2/2) - np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, appropriate complexity
• CQ-05: Output & API (1/1) - Saves as plot.png, no deprecated functions

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Excellent ggplot2 grammar: ggplot + aes + geom layers + scale + theme composition
• LM-02: Distinctive Features (4/5) - scale_fill_gradient2 for diverging scale, scale_color_identity for conditional text, pd.Categorical for ordering — distinctively ggplot2-style

Strengths

• Excellent use of Python Blue (#306998) and Python Yellow (#FFD43B) as a thematic diverging palette — on-brand and colorblind-safe
• Conditional text color (white on dark blue, dark on light) ensures readability across the entire range
• Data tells a clear recovery story that is immediately apparent from the color gradient progression
• Clean, idiomatic plotnine code with appropriate use of scale_fill_gradient2 and scale_color_identity
• Bold value annotations with +/- signs make precise values easy to read

Weaknesses

• Title font size is 22pt, below the recommended 24pt minimum for the output resolution
• Axis labels are empty strings — for a standalone visualization, even minimal labels like "Quarter" and "Department" would improve clarity
• Layout has some extra whitespace at the bottom due to rotated x-axis labels; tighter margins could improve canvas utilization

Verdict: REJECTED

[Copilot]

Pull request overview

Updates the plotnine implementation and related spec/metadata for the heatmap-basic plot, aiming to improve visual quality and incorporate a refreshed review pass.

Changes:

• Updated heatmap-basic specification metadata (timestamp + tags) and adjusted guidance in the spec markdown.
• Refreshed plotnine metadata (versions, generator info) and modified the stored quality score field.
• Reworked the plotnine heatmap implementation (new dataset, styling, labeling, and theming).

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.

File	Description
plots/heatmap-basic/specification.yaml	Updates updated timestamp and adds matrix to data_type tags.
plots/heatmap-basic/specification.md	Revises guidance from “clustering” to explicit row/column ordering guidance.
plots/heatmap-basic/metadata/plotnine.yaml	Updates plotnine/Python versions and generator metadata; changes quality_score to null.
plots/heatmap-basic/implementations/plotnine.py	Updates the plotnine heatmap code (data + styling) and adjusts the header metadata block.

[Copilot]

quality_score was changed from a numeric value to null. If downstream consumers expect a number (and other plot metadata files use numeric scores), this will break sorting/quality gating. Consider keeping the previous score until a new evaluation is available, or use the repository’s standard placeholder (if supported) consistently across metadata + header.

[Copilot]

The implementation header is not in the standard format and appears malformed: it uses """pyplots.ai (no space), has Quality: /100 (missing score or placeholder), and switches Created: to Updated:. This can break tooling that parses headers for quality/version metadata. Please align with the repository’s 4-line header convention (include a numeric quality score or use the usual pending placeholder, and keep the Created: field formatting consistent).

Suggested change

	Quality: 89/100 | Updated: 2026-02-15
	Quality: 89/100 | Created: 2026-02-15

[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 an 8×8 heatmap titled "Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai." Eight departments (Engineering, Marketing, Sales, Finance, Operations, HR, Research, Support) are arranged on the y-axis, and eight quarters (Q1 '23 through Q4 '24) on the x-axis. A diverging color scheme transitions from steel blue (#306998) for negative values through off-white for near-zero to golden yellow (#FFD43B) for positive values. Each cell contains a bold annotation showing the exact growth percentage with a +/- sign. White borders cleanly separate the tiles. The colorbar on the right is labeled "Growth (%)" with a scale from approximately -20 to +20. X-axis labels are rotated 45 degrees for readability. The visual story is immediately clear: a recovery trend flows from left (blue/negative) to right (yellow/positive), with departmental variation adding depth.

Quality Score: 89/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, axis titles 20pt, axis text 16pt, legend 14-16pt
• VQ-02: No Overlap (6/6) — No overlapping text; rotated x-labels and well-sized annotations
• VQ-03: Element Visibility (6/6) — Tiles perfectly fill the grid, annotations clearly visible
• VQ-04: Color Accessibility (3/4) — Blue-to-yellow diverging is colorblind-friendly; light yellow cells have slightly lower contrast with dark text
• VQ-05: Layout Balance (3/4) — Good canvas utilization; slight excess whitespace on left side
• VQ-06: Axis Labels & Title (1/2) — "Quarter" and "Department" are descriptive but lack units; growth % only appears in colorbar legend

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom Python Blue-to-Yellow diverging palette is thematic and intentional; bold annotations, white tile borders, clearly above defaults
• DE-02: Visual Refinement (4/6) — Grid removed, minimal theme, white backgrounds, some polish visible but not fully publication-level
• DE-03: Data Storytelling (4/6) — Recovery trend from left to right is immediately visible through color gradient; department variation adds narrative depth

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct heatmap via geom_tile
• SC-02: Required Features (4/4) — Diverging colormap, value annotations, colorbar legend, logical ordering — all present
• SC-03: Data Mapping (3/3) — X/Y correctly assigned
• SC-04: Title Format (3/3) — Correct format with spec-id, library, pyplots.ai; legend properly labeled

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows positive/negative values, temporal trend, departmental variation; could benefit from more extreme outliers
• DQ-02: Realistic Context (5/5) — Quarterly department growth rates — realistic, neutral business scenario
• DQ-03: Appropriate Scale (4/4) — Values from -22% to +30% are realistic for quarterly performance

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Imports → Data → Plot → Save, no functions/classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42)
• CQ-03: Clean Imports (2/2) — All imports used
• CQ-04: Code Elegance (2/2) — Clean conditional text color logic, appropriate complexity
• CQ-05: Output & API (1/1) — Saves as plot.png, no deprecated functions

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Expert grammar-of-graphics composition, proper aes(), scales, themes, pd.Categorical ordering
• LM-02: Distinctive Features (4/5) — scale_fill_gradient2 for diverging, scale_color_identity for conditional text, pd.Categorical for axis ordering

Strengths

• Excellent use of plotnine's grammar of graphics with scale_fill_gradient2 for a Python-themed diverging palette (blue-to-yellow)
• Bold cell annotations with conditional text color (white on dark, dark on light) ensure readability across all cells
• Data tells a compelling recovery story — the left-to-right blue-to-yellow progression is immediately apparent
• Clean KISS code structure with appropriate use of pd.Categorical for axis ordering
• All spec requirements fully met: diverging colormap, annotations, colorbar, logical ordering

Weaknesses

• Axis labels "Quarter" and "Department" lack units or additional context — consider appending "(% YoY)" or similar to y-axis or subtitle
• Light yellow cells (high positive values) could benefit from slightly stronger saturation for better visual contrast
• Left margin has slightly more whitespace than needed, reducing canvas efficiency

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays an 8×8 heatmap titled "Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai" with a subtitle "Year-over-year growth rate (%) across departments, Q1 2023 – Q4 2024". The Y-axis lists eight departments (Engineering, Marketing, Sales, Finance, Operations, HR, Research, Support) and the X-axis shows eight quarters (Q1 '23 through Q4 '24, rotated 45°). Each cell is colored using a diverging scheme: steel blue for negative values, off-white/cream for near-zero, and gold/amber for positive values. Every cell contains a bold annotation showing the growth rate with a sign prefix (e.g., "-32.5", "+38.2"). A vertical colorbar legend on the right labeled "Growth (%)" shows the scale from approximately -20 to +40. The overall layout has a light gray (#fafafa) background, white cell borders separating tiles, and no grid lines. The data tells a clear recovery story: the left columns are predominantly blue (negative growth) transitioning to gold (positive growth) on the right. Notable extremes include HR at -32.5% in Q1 '23 (deepest blue) and Sales at +38.2% in Q4 '24 (deepest gold). Text contrast is managed with white text on dark blue cells, dark gray on mid-range cells, and dark brown/amber on bright gold cells.

Quality Score: 86/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) - Font sizes explicitly set (title=22, subtitle=15, axis titles=18, axis text=15, annotations size=10). Title slightly under the 24pt guideline but all text clearly readable.
• VQ-02: No Overlap (6/6) - No overlapping text anywhere. Rotated x-axis labels, well-spaced cells.
• VQ-03: Element Visibility (6/6) - Tiles perfectly sized for 8×8 grid, annotations clear and well-positioned.
• VQ-04: Color Accessibility (3/4) - Blue-to-gold diverging scheme avoids red-green issues. Mid-range contrast between near-zero values and off-white midpoint could be slightly stronger.
• VQ-05: Layout Balance (3/4) - Good canvas utilization overall. Minor wasted space at top-left and between subtitle and heatmap body.
• VQ-06: Axis Labels & Title (1/2) - "Quarter" and "Department" are descriptive but categorical axes lack units. Subtitle provides "(%)" context.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) - Correct heatmap using geom_tile.
• SC-02: Required Features (4/4) - All spec features present: diverging colormap, value annotations, colorbar legend, logically ordered rows/columns.
• SC-03: Data Mapping (3/3) - X=Quarter, Y=Department, value mapped to fill correctly.
• SC-04: Title & Legend (2/3) - Title format correct with descriptive prefix. Colorbar legend title appropriate. Minor: no separate categorical legend needed but title could be tighter.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Custom diverging palette (steel blue → off-white → gold), intentional typography hierarchy, conditional text coloring for contrast. Clearly above defaults.
• DE-02: Visual Refinement (4/6) - Grid removed, minimal theme, white cell borders, light background, explicit panel/plot background styling. Good but not every detail perfected.
• DE-03: Data Storytelling (4/6) - Clear recovery narrative from Q1'23 to Q4'24. Extreme values create focal points. Conditional text coloring adds visual hierarchy. Color gradient naturally guides the eye.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive/negative values, extreme outliers, gradual trends, departmental variation. Good breadth.
• DQ-02: Realistic Context (5/5) - Realistic business scenario with department growth rates. Neutral and comprehensible.
• DQ-03: Appropriate Scale (4/4) - Growth rates from -32.5% to +38.2% are realistic for business context.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used.
• CQ-04: Code Elegance (1/2) - Slightly verbose with nested loops for matrix construction and DataFrame building. Conditional text color lambda is functional but adds complexity.
• CQ-05: Output & API (1/1) - Saves as plot.png, no deprecated functions.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Excellent ggplot2-style grammar: aes mappings, geom_tile + geom_text layering, scale_fill_gradient2 with midpoint, scale_color_identity, theme customization.
• LM-02: Distinctive Features (4/5) - Uses plotnine-specific patterns: grammar composition with +, scale_fill_gradient2 for diverging colors, scale_color_identity trick for conditional text, pd.Categorical for ordered factor levels.

Strengths

• Excellent use of plotnine's grammar of graphics with idiomatic layer composition (geom_tile + geom_text)
• Smart conditional text coloring via scale_color_identity ensures readability across all cell backgrounds
• Clear data storytelling with recovery trend from negative (blue) to positive (gold) across quarters
• Custom diverging palette (steel blue → off-white → gold) with well-chosen midpoint=0
• Deliberate extreme values (HR -32.5%, Sales +38.2%) create visual focal points
• Realistic, neutral business scenario with plausible growth rate values
• All spec requirements met: diverging colormap, cell annotations, colorbar, logical ordering

Weaknesses

• Title font size 22pt slightly under the 24pt guideline for optimal readability at full resolution
• Mid-range color contrast could be stronger — values near zero on off-white background are less visually distinct
• Code uses nested loops for matrix construction where vectorized numpy operations could be more concise
• Categorical axis labels "Quarter" and "Department" lack units (though units aren't natural for categories)

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[github-actions]

❌ AI Review Failed

The AI review action completed but did not produce valid output files.

What happened:

• The Claude Code Action ran
• No quality_score.txt file was created
• No review data was extracted

Action required:
Re-run the impl-review workflow manually:

gh workflow run impl-review.yml -f pr_number=4257

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🤖 impl-review

[claude]

AI Review - Attempt 3/3

Image Description

The heatmap displays an 8×8 grid of quarterly growth rates (%) across 8 departments (Engineering, Marketing, Sales, Finance, Operations, HR, Research, Support) over 8 quarters (Q1 '23 through Q4 '24). A diverging color scheme is used: dark steel blue for strongly negative values, cream/beige for near-zero values, and gold/amber for strongly positive values. Each cell contains a bold, signed numeric annotation (e.g., "+38.2", "-32.5"). Text color adapts to background: white on dark blue cells, dark gray on mid-range cells, and dark brown on gold cells. The title "Quarterly Growth by Department · heatmap-basic · plotnine · pyplots.ai" is centered and bold at the top, with a subtitle describing the data. X-axis labels are rotated 45° showing quarter names, Y-axis shows department names. A vertical colorbar legend labeled "Growth (%)" appears on the right with a range from approximately -20 to 40. White borders separate cells cleanly. The background is light gray (#f7f7f7). The plot clearly shows a recovery narrative from left (negative) to right (positive).

Quality Score: 92/100

Criteria Checklist

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, axis labels 20pt, ticks 16pt, annotations bold size=10. All text perfectly readable.
• VQ-02: No Overlap (6/6) - No overlapping text anywhere. Rotated x-axis labels, well-spaced annotations.
• VQ-03: Element Visibility (6/6) - Tiles fill the grid perfectly, annotations well-sized and clearly visible in every cell.
• VQ-04: Color Accessibility (3/4) - Diverging blue-cream-gold palette avoids red-green issues. Not a standard perceptually-uniform palette but still colorblind-friendly.
• VQ-05: Layout Balance (3/4) - Good canvas utilization, plot fills substantial area. Minor wasted space on the right near the legend.
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels ("Department", "Quarter"), values include units (%) in annotations and legend.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (7/8) - Custom diverging palette (steel blue → cream → gold), conditional text colors for contrast, white cell borders, light gray background, bold subtitle — publication-quality design.
• DE-02: Visual Refinement (5/6) - Grid lines removed, panel background white, plot background subtly tinted, generous whitespace, spines absent — highly polished.
• DE-03: Data Storytelling (4/6) - Data tells a clear recovery narrative (negative Q1 '23 → positive Q4 '24). Injected extreme focal points (HR -32.5 crisis, Sales +38.2 recovery). Conditional text color guides attention.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap using geom_tile.
• SC-02: Required Features (4/4) - All spec features present: diverging colormap for +/- values, value annotations in cells, colorbar legend, logically ordered rows/columns.
• SC-03: Data Mapping (3/3) - X=Quarter, Y=Department, fill=Growth (%) — all correctly assigned.
• SC-04: Title & Legend (3/3) - Title contains "heatmap-basic · plotnine · pyplots.ai", legend labeled "Growth (%)".

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive/negative values, near-zero, extremes, variation across both dimensions. Could have shown a slightly wider range of patterns.
• DQ-02: Realistic Context (5/5) - Realistic business scenario: quarterly departmental growth rates.
• DQ-03: Appropriate Scale (4/4) - Growth rates -32.5% to +38.2% are plausible for quarterly departmental metrics.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports are used.
• CQ-04: Code Elegance (2/2) - Well-organized, appropriate complexity, vectorized data generation.
• CQ-05: Output & API (1/1) - Saves as plot.png, no deprecated functions.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Idiomatic grammar of graphics: ggplot + aes + geom_tile + geom_text + scales + theme layering.
• LM-02: Distinctive Features (4/5) - Uses plotnine-distinctive features: scale_fill_gradient2 with midpoint, scale_color_identity for conditional text colors, pd.Categorical for ordering, theme composition.

Strengths

• Excellent diverging color palette (steel blue → cream → gold) with well-chosen midpoint at 0, creating an intuitive visual encoding of positive/negative growth
• Conditional text color logic (white on dark, dark gray on mid, dark brown on gold) ensures all annotations are readable against their cell background
• Data tells a compelling recovery story with intentional extreme values (HR -32.5 crisis, Sales +38.2 surge) serving as visual focal points
• Clean, idiomatic plotnine code with vectorized data generation and meshgrid-based long-form DataFrame construction
• Fully polished theme: removed grid lines, custom background, generous margins, rotated tick labels

Weaknesses

• The colorbar legend could benefit from more tick marks to better communicate the full range of the scale
• Minor wasted horizontal space on the right side between the plot and the legend area

Verdict: APPROVED