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

[MarkusNeusinger]

Summary

Updated matplotlib 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 department correlation matrix heatmap using the RdBu_r diverging colormap. Colors range from dark blue (-1.0) through white (0.0) to dark red (+1.0). All 64 cells contain bold numeric annotations with adaptive text color — white text on strongly colored cells (|value| > 0.55) and black text on lighter cells. The diagonal shows perfect correlation (1.00, dark red). Notable patterns: Sales-Marketing shows high positive correlation (0.82), Dev-Finance shows negative correlation (-0.25), HR-Legal has moderate positive correlation (0.52). White grid lines cleanly separate cells. Both axes are labeled "Department" with department names as tick labels. The colorbar on the right is labeled "Correlation Coefficient" ranging from -1.00 to 1.00. The title reads "Department Correlation · heatmap-basic · matplotlib · pyplots.ai". The layout uses a square format appropriate for a correlation matrix.

Quality Score: 86/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, axis labels 20pt, ticks 16pt, annotations 14pt, colorbar label 18pt
• VQ-02: No Overlap (6/6) — No overlapping text; 8 department labels fit cleanly with rotation
• VQ-03: Element Visibility (5/6) — Cells well-sized and annotations clear; slightly dense at 8×8 but fully readable
• VQ-04: Color Accessibility (4/4) — RdBu_r is a standard colorblind-safe diverging colormap
• VQ-05: Layout & Canvas (2/4) — Square 12×12 figure works for matrix but colorbar and title create slight imbalance; title crowds the colorbar top area
• VQ-06: Axis Labels & Title (2/2) — "Department" on both axes, "Correlation Coefficient" on colorbar

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: diverging palette centered at zero, adaptive text colors, clean white grid lines, polished colorbar with hidden outline. Above defaults but not publication-magazine level
• DE-02: Visual Refinement (4/6) — Good: tick length zeroed, colorbar outline removed, white cell borders. Could benefit from removing frame/spines around the matrix
• DE-03: Data Storytelling (4/6) — Data tells a story through color intensity: Sales-Marketing cluster, Dev-Finance opposition, HR-Legal connection all immediately visible through visual hierarchy

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct heatmap
• SC-02: Required Features (4/4) — All spec features present: diverging colormap for pos/neg values, cell annotations, colorbar legend, logically ordered labels
• SC-03: Data Mapping (3/3) — Symmetric matrix with correct diagonal
• SC-04: Title & Legend (2/3) — Title format includes spec-id, library, pyplots.ai correctly. No legend needed for heatmap. Minor: title has descriptive prefix before the required format elements

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows positive, negative, near-zero, and strong correlations. Symmetric matrix with 1.0 diagonal. Could use more extreme negative values for fuller range
• DQ-02: Realistic Context (5/5) — Department correlation in a company — real, neutral business scenario
• DQ-03: Appropriate Scale (4/4) — All values in [-1, 1]; relationships are plausible (Sales-Marketing high, Dev-Finance low)

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Flat imports → data → plot → save
• CQ-02: Reproducibility (2/2) — Fully deterministic hardcoded matrix
• CQ-03: Clean Imports (2/2) — All imports used: plt, np, TwoSlopeNorm
• CQ-04: Code Elegance (1/2) — Clean overall but separate loop for grid lines is slightly verbose; could integrate styling more compactly
• CQ-05: Output & API (1/1) — Saves as plot.png, dpi=300, bbox_inches='tight', no deprecated API

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — Idiomatic matshow, TwoSlopeNorm for diverging normalization, fig.colorbar configuration, ax.text annotations
• LM-02: Distinctive Features (3/5) — TwoSlopeNorm is a distinctive matplotlib feature for centering diverging colormaps; matshow is matplotlib-specific. Good but not exceptional

Strengths

• Excellent use of TwoSlopeNorm for proper diverging color normalization centered at zero
• Adaptive text color (white/black) based on cell value magnitude ensures all annotations are readable
• Realistic department correlation data with plausible relationships (Sales-Marketing high, Dev-Finance negative)
• Clean white grid lines provide clear cell separation
• All font sizes explicitly configured for the target resolution

Weaknesses

• Layout balance could improve: the title overlaps with the colorbar area, and the square format creates some wasted canvas space in the upper-right corner
• The matrix frame/spines around the heatmap are still visible — removing them would give a cleaner, more modern look
• The grid line loop (lines 59-61) adds borders separately from the heatmap; a cleaner approach might use matshow parameters or pcolormesh with edgecolors
• Negative correlation range is underrepresented (minimum is only -0.25); more extreme negative values would better showcase the diverging colormap
• The colorbar shrink=0.78 doesn't perfectly align with the matrix height

Verdict: REJECTED

[Copilot]

Pull request overview

Updates the heatmap-basic plot spec and the matplotlib implementation to reflect a refreshed “quality review” pass (data, styling, and metadata/version bookkeeping) within the plot-centric plots/heatmap-basic/ directory.

Changes:

• Refined the heatmap spec guidance/tags (adds matrix data type; updates a best-practice bullet).
• Updated matplotlib implementation to use TwoSlopeNorm + matshow, revised labels/styling, and replaced the data with a fixed “department correlation” matrix.
• Refreshed matplotlib metadata (timestamps, generator, Python version, quality score field).

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
plots/heatmap-basic/specification.yaml	Bumps updated timestamp and adds matrix to data_type tags.
plots/heatmap-basic/specification.md	Replaces clustering suggestion with explicit row/column ordering guidance.
plots/heatmap-basic/metadata/matplotlib.yaml	Updates generator/version metadata and sets quality_score to null.
plots/heatmap-basic/implementations/matplotlib.py	Switches to TwoSlopeNorm + matshow, updates data + styling, and adjusts plot labeling.

[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 correlation heatmap showing inter-department relationships (Sales, Marketing, Support, Dev, HR, Finance, Ops, Legal). A diverging RdBu_r colormap is used, centered at zero via TwoSlopeNorm, with deep red for strong positive correlations (e.g., Sales-Marketing 0.82) and deep blue for strong negative correlations (e.g., Dev-Finance -0.65). Each cell contains a bold numeric annotation with adaptive text color — white on dark cells, black on light cells. Cells are separated by white edge lines (1.5px). The diagonal displays 1.00 (self-correlation). A vertical colorbar on the right is labeled "Correlation Coefficient" spanning -1.0 to 1.0. X-axis labels are rotated 45° for readability. Both axes are labeled "Department". The title reads "heatmap-basic · matplotlib · pyplots.ai" in the correct format. All spines are removed for a clean, modern look. The layout is near-square.

Quality Score: 86/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, axis labels 20pt, ticks 16pt, annotations 14pt, colorbar label 18pt
• VQ-02: No Overlap (6/6) - No overlapping text; rotated x-axis labels fit cleanly, cell annotations well-spaced
• VQ-03: Element Visibility (6/6) - Cells large and clearly visible, appropriate for 8×8 matrix density
• VQ-04: Color Accessibility (2/4) - RdBu_r diverging colormap works but red-blue can be problematic for some colorblind viewers; not as safe as viridis/cividis
• VQ-05: Layout & Canvas (2/4) - figsize (14, 12) produces 4200×3600px at dpi=300, which doesn't match standard formats (4800×2700 or 3600×3600). Near-square but not exactly 1:1. Some wasted whitespace
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels ("Department") and colorbar labeled "Correlation Coefficient"

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: diverging colormap, white cell edges, removed spines, adaptive text color. Above defaults but not FiveThirtyEight-level polish
• DE-02: Visual Refinement (5/6) - All spines removed, colorbar outline hidden, clean cell edges, good padding via tight_layout. Minor: some extra whitespace could be tightened
• DE-03: Data Storytelling (3/6) - Data itself tells a story (Dev-Finance negative correlation is notable), diverging colormap draws attention to extremes. No additional visual hierarchy or emphasis on key findings

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap chart type
• SC-02: Required Features (4/4) - All spec features present: diverging colormap for pos/neg values, value annotations, colorbar legend, logically ordered departments
• SC-03: Data Mapping (3/3) - X/Y correctly assigned to department labels, values correctly color-mapped
• SC-04: Title & Legend (3/3) - Title exactly matches required format; colorbar serves as legend

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive, negative, and zero-range correlations; perfect diagonal; good magnitude variety. Could show more extreme values for stronger visual contrast
• DQ-02: Realistic Context (5/5) - Real business scenario with plausible department correlations (Sales-Marketing high positive, Dev-Finance negative)
• DQ-03: Appropriate Scale (4/4) - All values within [-1, 1], symmetric matrix, perfect diagonal — mathematically correct

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save flow, no functions/classes
• CQ-02: Reproducibility (2/2) - Fully deterministic (hardcoded matrix), no randomness
• CQ-03: Clean Imports (2/2) - All three imports used (plt, np, TwoSlopeNorm)
• CQ-04: Code Elegance (1/2) - Clean overall but the [::-1] reversal pattern for pcolormesh adds some complexity; annotation loop is slightly verbose
• CQ-05: Output & API (1/1) - Saves as plot.png at dpi=300 with bbox_inches='tight'

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Good use of axes methods, pcolormesh with edgecolors, TwoSlopeNorm. Could be more idiomatic using imshow for grid-aligned data
• LM-02: Distinctive Features (4/5) - TwoSlopeNorm and pcolormesh with edgecolors are distinctive matplotlib features not easily replicated elsewhere

Strengths

• Adaptive text color (white on dark cells, black on light) ensures readability across all correlation values
• TwoSlopeNorm provides proper centering of the diverging colormap at zero, a distinctive matplotlib feature
• Realistic department correlation data with meaningful patterns (e.g., Sales-Marketing positive, Dev-Finance negative)
• All spines removed and colorbar outline hidden for a clean, modern aesthetic
• White cell edges via pcolormesh edgecolors provide clear cell separation

Weaknesses

• Figure dimensions (14×12 at 300dpi = 4200×3600) do not match either standard format — should be 16×9 for 4800×2700 or 12×12 for 3600×3600
• RdBu_r colormap is not optimally colorblind-safe — consider coolwarm or a perceptually-uniform diverging alternative
• Data storytelling could be stronger — no visual emphasis on notable correlations beyond colormap encoding
• The [::-1] data reversal for pcolormesh adds unnecessary complexity; imshow with origin parameter could simplify

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays an 8×8 department correlation matrix heatmap using the coolwarm diverging colormap. Eight departments (Sales, Marketing, Support, Dev, HR, Finance, Ops, Legal) are labeled on both axes. Each cell contains the correlation coefficient value annotated in the center. The diagonal cells show 1.00 in dark red. Strong positive correlations (e.g., Sales–Marketing at 0.82) appear in deep red with larger bold white text, while strong negative correlations (e.g., Dev–Finance at -0.65) appear in blue with larger bold white text. Weak correlations use smaller dark gray text on lighter background colors. White grid lines cleanly separate all cells. The colorbar on the right ranges from -1.00 (blue) to 1.00 (red) with 0.00 centered, labeled "Correlation Coefficient". Spines are removed for a modern look. The title reads "heatmap-basic · matplotlib · pyplots.ai" at the top. Both axes are labeled "Department". The square layout fills the canvas well.

Quality Score: 89/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, annotations 13-16pt
• VQ-02: No Overlap (6/6) - No overlapping text; white grid lines cleanly separate all elements
• VQ-03: Element Visibility (6/6) - Cells well-sized for 8×8 matrix, all annotations clearly visible
• VQ-04: Color Accessibility (3/4) - coolwarm is acceptable but not perceptually uniform; red-blue distinction works for most colorblind types but not ideal
• VQ-05: Layout Balance (3/4) - Square layout appropriate for heatmap; slight asymmetry from colorbar on right side
• VQ-06: Axis Labels & Title (1/2) - "Department" is descriptive but generic; both axes have the same label

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design with TwoSlopeNorm, adaptive text colors, emphasis on strong correlations, removed spines; above default but not publication-level polish
• DE-02: Visual Refinement (5/6) - Excellent: spines removed, colorbar outline hidden, white grid lines, tick marks hidden, generous whitespace
• DE-03: Data Storytelling (4/6) - Good visual hierarchy: strong correlations highlighted with larger bold text, drawing viewer attention to key relationships

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap with matrix format and color intensity
• SC-02: Required Features (4/4) - All spec features present: diverging colormap, cell annotations, colorbar, logically ordered labels
• SC-03: Data Mapping (3/3) - Departments on both axes, values correctly mapped to color
• SC-04: Title Format (3/3) - Correct format "heatmap-basic · matplotlib · pyplots.ai"

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive/negative correlations, strong/weak values, symmetric matrix; could show more distinct clustering patterns
• DQ-02: Realistic Context (5/5) - Plausible business scenario: department correlation matrix
• DQ-03: Appropriate Scale (4/4) - All values between -1 and 1, realistic magnitudes, diagonal = 1.00, symmetric

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save flow
• CQ-02: Reproducibility (2/2) - Deterministic hardcoded data array
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: Code Elegance (1/2) - Slightly verbose annotation loop with adaptive sizing logic
• CQ-05: Output & API (1/1) - Saves as plot.png, no deprecated functions

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Expert use of imshow for grid data, TwoSlopeNorm, minor tick grid for cell borders
• LM-02: Distinctive Features (4/5) - TwoSlopeNorm and minor-tick grid trick are distinctive matplotlib techniques

Strengths

• Excellent use of TwoSlopeNorm to properly center the diverging colormap at zero
• Smart adaptive text color (white on dark cells, dark gray on light cells) ensures readability across all values
• Visual emphasis on strong correlations (|r| ≥ 0.6) through bold, larger text creates effective data storytelling
• Clean modern aesthetic with removed spines, hidden tick marks, and white grid line cell separation
• Well-chosen realistic business scenario with plausible correlation values

Weaknesses

• coolwarm colormap is not perceptually uniform; a colormap like RdBu_r might be slightly more accessible
• Both axis labels simply say "Department" which is repetitive — could be differentiated or one removed
• Annotation loop could be slightly more concise

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays an 8×8 department correlation matrix heatmap. The RdBu_r diverging colormap maps strong positive correlations to deep red (e.g., Sales-Marketing at 0.82), strong negative correlations to blue (e.g., Finance-Dev at -0.65), and near-zero values to pale/white tones. All 64 cells contain numeric annotations formatted to two decimal places. The diagonal cells show 1.00 in dark red (self-correlation). White grid lines cleanly separate each cell. Strong correlations (|val| ≥ 0.6) use larger bold white text for emphasis; weaker correlations use smaller dark text. The title reads "heatmap-basic · matplotlib · pyplots.ai" at the top. Y-axis is labeled "Department (row)" and X-axis "Department (column)". X-axis tick labels are rotated 45°. A colorbar on the right displays "Correlation Coefficient" ranging from -1.00 to 1.00. The layout is square and well-filled.

Quality Score: 90/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, axis labels 20pt, tick labels 16pt, annotations 13-16pt. All perfectly readable.
• VQ-02: No Overlap (6/6) - No overlapping text. X-axis labels rotated 45° to prevent collision. Cell annotations well-spaced within cells.
• VQ-03: Element Visibility (6/6) - Cells clearly visible with good color saturation. 8×8 matrix well-sized for the square canvas.
• VQ-04: Color Accessibility (4/4) - RdBu_r is a well-established colorblind-friendly diverging colormap. Good contrast between text and background colors.
• VQ-05: Layout & Canvas (2/4) - Square figure works well for correlation matrix, but colorbar creates asymmetric margins with wasted space in upper-right area.
• VQ-06: Axis Labels & Title (1/2) - Labels say "Department (row)" and "Department (column)" — descriptive but "(row)/(column)" are mechanical clarifiers rather than meaningful units.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: RdBu_r with TwoSlopeNorm, removed spines, white grid cell separators, adaptive text color/weight for strong correlations. Clearly above defaults but not quite FiveThirtyEight-level.
• DE-02: Visual Refinement (5/6) - Spines removed, white grid lines as cell separators, colorbar outline hidden, generous padding. Polished look with attention to detail.
• DE-03: Data Storytelling (3/6) - Department clusters are visible through color patterns (Sales/Marketing/Finance cluster positively; Dev/Ops/Support cluster positively). Bold/larger font on strong correlations creates visual hierarchy. However, no explicit grouping indicators to guide the viewer to the clustering insight.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct chart type: basic heatmap using imshow.
• SC-02: Required Features (4/4) - All spec features present: diverging colormap for pos/neg values, value annotations in cells, colorbar legend, logically ordered rows/columns.
• SC-03: Data Mapping (3/3) - Departments correctly mapped to both axes, correlation values mapped to color intensity.
• SC-04: Title & Legend (3/3) - Title exactly matches required format. Colorbar serves as legend (appropriate for heatmaps).

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive, negative, and near-zero correlations plus diagonal self-correlation. Good value range (-0.65 to 1.00), though no extreme negative values near -1.0.
• DQ-02: Realistic Context (5/5) - Real, neutral business scenario: inter-department correlations. Plausible groupings (revenue vs technical vs admin).
• DQ-03: Appropriate Scale (4/4) - All correlation coefficients in [-1, 1]. Symmetric matrix as expected. Values realistic for department-level metrics.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - Fully deterministic: hardcoded numpy array with no random generation.
• CQ-03: Clean Imports (2/2) - Only three imports, all used: matplotlib.pyplot, numpy, TwoSlopeNorm.
• CQ-04: Code Elegance (2/2) - Clean, Pythonic. Appropriate complexity with well-commented sections.
• CQ-05: Output & API (1/1) - Saves as plot.png, dpi=300, bbox_inches='tight'. No deprecated API usage.

Library Mastery (10/10)

• LM-01: Idiomatic Usage (5/5) - Expert matplotlib patterns: imshow for heatmap, TwoSlopeNorm for diverging normalization, minor-tick grid for cell separators, ax.text loop for annotations.
• LM-02: Distinctive Features (5/5) - TwoSlopeNorm for asymmetric normalization, minor-tick grid trick for cell borders, fine-grained colorbar control (fraction/pad/aspect), spine manipulation — all distinctively matplotlib capabilities.

Strengths

• Excellent use of TwoSlopeNorm for proper diverging color normalization centered at zero
• Clever minor-tick grid technique for clean white cell separators — a distinctively matplotlib approach
• Adaptive text styling (bold/large for strong correlations, smaller/dark for weak) creates effective visual hierarchy
• Well-chosen realistic data scenario with clear department clustering patterns
• Clean, well-structured code with appropriate comments

Weaknesses

• Axis labels "Department (row)" and "Department (column)" are mechanical — could be more informative or simply "Department" on each axis
• Some wasted canvas space due to colorbar placement creating asymmetric margins
• Data storytelling could be enhanced — the clustering patterns (revenue/technical/admin departments) are present in the data but not explicitly highlighted for the viewer

Verdict: APPROVED