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

[MarkusNeusinger]

Summary

Updated altair 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 correlation heatmap of weather metrics (Temperature, Humidity, Wind Speed, Pressure, Visibility, Cloud Cover, Precipitation, UV Index). A diverging blue-white-red color scheme is used, centered at zero — dark blue represents strong positive correlations (e.g., diagonal = 1.00) and dark red represents strong negative correlations (e.g., Visibility ↔ Cloud Cover = −0.73). Each cell contains a bold numeric annotation showing the correlation coefficient to two decimal places. Text color adapts — white on saturated cells, dark gray on lighter cells. X-axis labels appear at the top, Y-axis labels on the left. White borders separate cells cleanly. A vertical colorbar on the right labeled "Correlation" ranges from −1.0 to 1.0. The title reads "Weather Metrics Correlation · heatmap-basic · altair · pyplots.ai" at the top-left. The overall layout is square (3600×3600 px).

Quality Score: 85/100

Criteria Checklist

Visual Quality (25/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 28pt, axis labels 16pt, annotations 16pt bold, legend title 18pt/labels 16pt. Perfectly readable.
• VQ-02: No Overlap (6/6) — No overlapping text anywhere. Cells sized well for annotation text.
• VQ-03: Element Visibility (6/6) — Cells optimally sized for 8×8 matrix, white borders provide clear separation.
• VQ-04: Color Accessibility (2/4) — The "redblue" diverging scheme is problematic for red-green colorblind users. A "blueorange" or "brownbluegreen" scheme would be more accessible.
• VQ-05: Layout & Canvas (2/4) — Square layout suits a correlation matrix. However, some wasted space on right side near colorbar, and the layout feels slightly unbalanced with the legend area.
• VQ-06: Axis Labels & Title (1/2) — Axis titles set to None. Variable names serve as labels and are descriptive, but no axis title context (e.g., "Weather Variables"). Correlation matrix is unitless so no units expected.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: diverging palette is effective, white cell borders add polish, clean typography hierarchy. Above configured defaults but not at FiveThirtyEight level.
• DE-02: Visual Refinement (4/6) — Good refinement: grid disabled, view stroke removed, explicit font sizing, white cell borders. Could benefit from further polish (e.g., subtle background tint, adjusted margins).
• DE-03: Data Storytelling (3/6) — Injected correlations create meaningful clusters (cloud/rain vs. visibility/UV). Adaptive text color guides the eye. However, no explicit emphasis on strongest correlations beyond color intensity alone.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct chart type: heatmap via mark_rect().
• SC-02: Required Features (4/4) — All spec features present: diverging colormap centered at 0, value annotations, colorbar legend, logically ordered rows/columns.
• SC-03: Data Mapping (3/3) — X/Y correctly assigned, all 64 cells visible and properly mapped.
• SC-04: Title & Legend (2/3) — Title includes "heatmap-basic · altair · pyplots.ai" but prepended with "Weather Metrics Correlation ·", deviating from exact {spec-id} · {library} · pyplots.ai format. Legend label "Correlation" is appropriate.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows positive, negative, and near-zero correlations plus perfect diagonal. Good range of values but could benefit from more extreme negative correlations.
• DQ-02: Realistic Context (5/5) — Weather metrics correlation is a realistic, neutral, scientifically meaningful scenario.
• DQ-03: Appropriate Scale (4/4) — Correlation values properly bounded [-1, 1] with realistic magnitudes from injected correlations.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean flow: imports → data generation → plot → save.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) used.
• CQ-03: Clean Imports (2/2) — All imports (altair, numpy, pandas) are used.
• CQ-04: Code Elegance (2/2) — Clean, well-structured, appropriate complexity.
• CQ-05: Output & API (1/1) — Saves as plot.png and plot.html, no deprecated APIs.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Expertly uses Altair's declarative grammar: encoding types (:N, :Q), alt.Scale with domain/domainMid, alt.Legend, alt.Title, alt.Tooltip, layer composition.
• LM-02: Distinctive Features (4/5) — Uses alt.when().then().otherwise() conditional encoding for adaptive text color (Altair 5.x+ feature), interactive tooltips, and HTML export.

Strengths

• Excellent use of Altair's declarative API with layer composition (heatmap + text) for annotations
• Adaptive text color via alt.when().then().otherwise() conditional encoding — a distinctive Altair 5.x+ feature that improves readability on both light and saturated cells
• Realistic weather correlation data with injected correlations that produce meaningful patterns (cloud cover clusters, inverse visibility relationships)
• Clean KISS code structure with appropriate complexity
• White cell borders (stroke="#ffffff", strokeWidth=1.5) add visual polish and separation
• Interactive tooltips and HTML export leverage Altair's interactive capabilities

Weaknesses

• Color scheme "redblue" is not ideal for colorblind accessibility — consider switching to "blueorange" or using a perceptually-uniform diverging scheme
• Title prepends "Weather Metrics Correlation ·" before the required format — should be exactly heatmap-basic · altair · pyplots.ai
• Layout has some wasted space, especially around the colorbar area on the right side
• No explicit visual emphasis or storytelling beyond the diverging color — strongest correlations could be highlighted more prominently
• Axis titles are set to None; even for a correlation matrix, a subtle axis label or subtitle could provide context

Verdict: REJECTED

[Copilot]

Pull request overview

Updates the Altair implementation for heatmap-basic and refreshes the plot specification/metadata to reflect the new design and tracking info.

Changes:

• Updated heatmap-basic spec tracking (updated timestamp) and expanded classification tags.
• Refined the specification guidance notes for row/column ordering.
• Refreshed Altair metadata (generator, Python version, timestamp, quality score field) and revised the Altair implementation to use a correlation-matrix dataset with updated styling/encoding.

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	Updates spec tracking timestamp and adds matrix to data_type tags.
plots/heatmap-basic/specification.md	Updates guidance note to emphasize logical row/column ordering.
plots/heatmap-basic/metadata/altair.yaml	Refreshes Altair metadata fields (versions/timestamps/generator/quality score).
plots/heatmap-basic/implementations/altair.py	Reworks the Altair heatmap to a correlation-matrix example with updated encodings, styling, and exports.

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays an 8×8 pairwise Pearson correlation matrix heatmap for weather variables: Temperature, Humidity, Wind Speed, Pressure, Visibility, Cloud Cover, Precipitation, and UV Index. The color scheme uses a blue-orange diverging palette (Altair's "blueorange") centered at 0, where orange/brown indicates positive correlations and blue indicates negative correlations. Each cell contains a numeric annotation showing the correlation coefficient to two decimal places. Cells with strong correlations (|r| ≥ 0.7) are highlighted with darker borders. Text color adapts — white on dark cells, dark gray on light cells. The diagonal shows 1.00 values. X-axis labels are positioned at the top at a -30° angle; Y-axis labels are on the left. Both axes are labeled "Weather Variable." A vertical color gradient legend labeled "Correlation" appears on the right side ranging from -1.0 to 1.0. The title reads "heatmap-basic · altair · pyplots.ai" with subtitle "Pairwise Pearson correlation coefficients for 8 weather metrics." White cell borders with rounded corners separate the cells. Notable correlations include Cloud Cover ↔ Visibility (-0.77), Cloud Cover ↔ Precipitation (0.63), and Humidity ↔ Cloud Cover (0.54).

Quality Score: 87/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 28pt, axis title 20pt, tick labels 16pt, annotations 16pt bold, legend title 18pt, legend labels 16pt. All perfectly readable.
• VQ-02: No Overlap (5/6) - X-axis labels angled at -30° mostly avoid overlap, but "Temperature" and "Precipitation" labels at top are slightly cramped at the ends.
• VQ-03: Element Visibility (6/6) - Cells are well-sized with strong color differentiation. All 64 cells clearly visible.
• VQ-04: Color Accessibility (4/4) - Blue-orange diverging scheme is colorblind-safe with good contrast between extremes.
• VQ-05: Layout & Canvas (2/4) - Square layout (1200×1200 units) is appropriate for correlation matrix, but noticeable empty space around the plot. Colorbar legend is somewhat isolated to the right.
• VQ-06: Axis Labels & Title (1/2) - "Weather Variable" is descriptive but redundant on both axes. No units (though units don't apply to correlation matrix axes).

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: blue-orange diverging scheme well chosen, white cell borders with rounded corners add polish, border highlighting on strong correlations, adaptive text color. Clearly above defaults but not quite publication-level.
• DE-02: Visual Refinement (4/6) - Grid removed, view stroke removed, white cell borders, rounded corners, generous padding. Good refinement visible.
• DE-03: Data Storytelling (4/6) - Strong-correlation highlighting (dark borders on |r| ≥ 0.7) creates visual hierarchy. Correlation clusters visible (e.g., Cloud Cover ↔ Visibility at -0.77). Subtitle provides context.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap using mark_rect().
• SC-02: Required Features (4/4) - All spec features present: diverging colormap ✓, value annotations ✓, colorbar legend ✓, logical ordering ✓.
• SC-03: Data Mapping (3/3) - X/Y correctly mapped as column/row variables.
• SC-04: Title & Legend (3/3) - Title format "heatmap-basic · altair · pyplots.ai" correct, colorbar legend labeled "Correlation."

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive, negative, and near-zero correlations across a good range. Diagonal 1.00 values present. Could benefit from a stronger negative correlation (most negative is -0.77).
• DQ-02: Realistic Context (5/5) - Weather metrics correlation is a real, neutral, comprehensible scenario.
• DQ-03: Appropriate Scale (4/4) - All values in [-1, 1] range, realistic for weather data correlations.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Clean flow: imports → data generation → plot → save. No functions/classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used (altair, numpy, pandas).
• CQ-04: Code Elegance (1/2) - Slightly verbose: abs_value column in dataframe only used for highlight filter, could be inline. Records list-append loop could be more concise.
• CQ-05: Output & API (1/1) - Saves as plot.png and plot.html, no deprecated APIs.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Expert use of Altair's declarative grammar: proper encoding types (N, Q), alt.X/Y with axis config, alt.Color with Scale, alt.Legend, alt.Title with subtitle.
• LM-02: Distinctive Features (4/5) - Uses Altair-specific features: layer composition (heatmap + highlight + text), alt.when().then().otherwise() for conditional color encoding, tooltip encoding, declarative mark composition.

Strengths

• Excellent use of Altair's conditional encoding (alt.when().then().otherwise()) for adaptive text color — this is a distinctively Altair feature that enhances readability
• Strong-correlation border highlighting (dark borders on |r| ≥ 0.7) adds meaningful visual hierarchy and guides the reader to important relationships
• Well-crafted data with realistic, injected correlations that create a believable weather scenario with both positive and negative relationships
• White cell borders with rounded corners and clean grid-free design create a polished, modern look
• Informative subtitle contextualizes the data being displayed

Weaknesses

• Canvas utilization could be improved — there is noticeable empty space around the heatmap, and the colorbar legend floats somewhat isolated to the right
• Both axes labeled "Weather Variable" is redundant — consider differentiating or removing one axis title
• DE-01 could reach 7-8 with a more refined color palette or additional design polish (e.g., subtle background tint, more intentional spacing)
• Minor label cramping on some angled x-axis labels at the edges

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 correlation heatmap of weather variables (Temperature, Humidity, Wind Speed, Pressure, Visibility, Cloud Cover, Precipitation, UV Index). The color scheme is a blue-orange diverging palette centered at zero: strong positive correlations appear in dark orange/brown, strong negative correlations in dark blue, and near-zero values in light cream/white. Each cell contains a bold numeric annotation showing the Pearson correlation coefficient to two decimal places. Text color adapts — white on strongly colored cells, dark gray on lighter cells. Cells with |r| ≥ 0.7 have darker highlighted borders. Column labels are rotated at -35° along the top axis, row labels are on the left. A vertical "Correlation" colorbar legend is positioned on the right. The title reads "heatmap-basic · altair · pyplots.ai" with a subtitle "Pairwise Pearson correlation coefficients for 8 weather metrics." The layout is clean with white cell separators and rounded corners.

Quality Score: 89/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 26pt, subtitle 16pt, axis labels 15pt, cell text 15pt bold
• VQ-02: No Overlap (5/6) - Column labels at -35° are slightly tight but still readable, no actual overlap
• VQ-03: Element Visibility (6/6) - Cells optimally sized for 8×8 matrix
• VQ-04: Color Accessibility (4/4) - Blue-orange diverging scheme is colorblind-safe
• VQ-05: Layout & Canvas (2/4) - Output dimensions ~2800×2920 don't match target formats (4800×2700 or 3600×3600). Plot fills canvas reasonably but dimensions are non-standard
• VQ-06: Axis Labels & Title (1/2) - Axis titles set to None; appropriate for correlation matrix but could name the axes

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: white cell borders with corner radius, custom diverging palette, adaptive text color, highlight borders on strong correlations
• DE-02: Visual Refinement (5/6) - Grid removed, view stroke removed, padding configured, white separators, rounded corners
• DE-03: Data Storytelling (4/6) - Highlight borders on strong correlations create visual hierarchy; subtitle provides context; viewer immediately sees strongest relationships

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct heatmap using mark_rect()
• 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 as Column/Row
• SC-04: Title & Legend (3/3) - Correct title format with descriptive subtitle

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows strong positive, strong negative, weak, and diagonal correlations
• DQ-02: Realistic Context (5/5) - Weather metrics correlation is a real, neutral scenario
• DQ-03: Appropriate Scale (4/4) - All values realistic and within [-1, 1]

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only numpy, pandas, altair — all used
• CQ-04: Code Elegance (2/2) - Well-organized, list comprehension, layered composition
• CQ-05: Output & API (1/1) - Saves plot.png and plot.html

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Declarative grammar, encoding types, layered composition, transform_filter, alt.when() conditional
• LM-02: Distinctive Features (4/5) - alt.when().then().otherwise() for adaptive text, transform_filter for highlights, tooltip encoding, + layer operator

Strengths

• Adaptive text color using alt.when() conditional encoding — white on dark cells, dark on light cells — excellent readability
• Highlight borders on strong correlations (|r| ≥ 0.7) via transform_filter create clear visual hierarchy
• Realistic weather correlation data with plausible injected relationships showing variety of positive and negative correlations
• Clean layered composition (heatmap + highlight + text) is idiomatic Altair
• White cell separators with corner radius give a polished, modern appearance

Weaknesses

• Output dimensions (~2800×2920 at scale_factor=4.0) don't match either target format (4800×2700 landscape or 3600×3600 square) — should use width/height that scales to a target size
• Axis titles set to None — while acceptable for a correlation matrix, adding "Weather Variable" or similar would improve self-documentation
• Column label angle of -35° gets tight with longer variable names — a steeper angle or top-padding adjustment would help

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 correlation matrix heatmap of weather variables (Temperature, Humidity, Wind Speed, Pressure, Visibility, Cloud Cover, Precipitation, UV Index). It uses a blue-orange diverging color scheme centered at 0, with correlation values annotated in each cell using bold text. The title reads "heatmap-basic · altair · pyplots.ai" with two descriptive subtitle lines explaining the data and the bold-border highlighting. Column labels are rotated -45° at the top axis, row labels on the left. A vertical colorbar on the right displays the correlation scale from -1.0 to 1.0 labeled "Correlation". Strong correlations (|r| ≥ 0.7) are highlighted with darker border outlines. Text color adapts automatically — white text on dark-colored cells, dark text on light-colored cells. The diagonal shows perfect self-correlation (1.00). Notable relationships include Visibility vs Cloud Cover at -0.77 (negative), Cloud Cover vs Precipitation at 0.63 (positive), and Humidity vs Cloud Cover at 0.54 (positive). The layout uses a square format with cells separated by thin white borders with rounded corners.

Quality Score: 88/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 26pt, subtitle 16pt, axis labels 15pt, axis titles 18pt, annotations 15pt bold
• VQ-02: No Overlap (6/6) - No overlapping text; labels rotated -45° prevent collision; annotations fit within cells
• VQ-03: Element Visibility (6/6) - Cells well-sized for 8×8 matrix; white borders separate cells cleanly
• VQ-04: Color Accessibility (4/4) - Blue-orange diverging scheme is colorblind-safe
• VQ-05: Layout Balance (2/4) - Square format appropriate for correlation matrix but some whitespace imbalance with colorbar placement
• VQ-06: Axis Labels & Title (1/2) - "Weather Variable" is descriptive but identical for both axes; no units (not applicable for correlation)

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: adaptive text colors, rounded corners, white cell borders, bold-border highlighting, informative subtitle. Above defaults but not quite publication-pinnacle
• DE-02: Visual Refinement (5/6) - Grid removed, view stroke removed, subtle cell borders, generous padding, corner radius styling
• DE-03: Data Storytelling (4/6) - Bold borders guide viewer to strong correlations; subtitle explains what to look for; color naturally highlights extremes

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) - Correct heatmap using mark_rect()
• SC-02: Required Features (4/4) - Diverging colormap ✓, cell annotations ✓, colorbar ✓, logical ordering ✓, strong-correlation highlighting (bonus)
• SC-03: Data Mapping (3/3) - X/Y correctly mapped as Column and Row weather variables
• SC-04: Title & Legend (2/3) - Title format correct; colorbar legend "Correlation" appropriate; both axes share same title "Weather Variable" which is slightly redundant

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive, negative, near-zero correlations and diagonal; good range of strengths
• DQ-02: Realistic Context (5/5) - Weather metrics: real, neutral, scientifically meaningful scenario with realistic relationships
• DQ-03: Appropriate Scale (4/4) - Correlation values -0.77 to 1.00, all plausible for weather data

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean 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 (altair, numpy, pandas)
• CQ-04: Code Elegance (2/2) - Clean list comprehension, layered composition, appropriate complexity
• CQ-05: Output & API (1/1) - Saves plot.png and plot.html, no deprecated functions

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Expert Altair: declarative layers, conditional encoding, transform_filter, proper type encodings (:N, :Q)
• LM-02: Distinctive Features (4/5) - alt.when().then().otherwise() for conditional text color, transform_filter for highlighting, tooltip + HTML export, layer composition

Strengths

• Excellent use of Altair's declarative layer composition (heatmap + highlight borders + text annotations as separate layers)
• Adaptive text color using alt.when() conditional encoding ensures readability on both dark and light cells
• Bold border highlighting for strong correlations (|r| ≥ 0.7) adds effective visual storytelling
• Realistic weather correlation data with meaningful relationships injected via controlled random correlations
• Clean, well-structured code with appropriate complexity for the visualization
• Informative subtitle that explains both the data and the highlighting convention

Weaknesses

• Layout balance could be improved — some whitespace imbalance around the colorbar area
• Both axes share the identical title "Weather Variable" which is redundant; consider differentiating or removing one
• Feature coverage could include a stronger negative correlation (below -0.8) to better demonstrate the full diverging scale

Verdict: APPROVED