feat(altair): implement histogram-2d

[github-actions]

Implementation: histogram-2d - python/altair

Implements the python/altair version of histogram-2d.

File: plots/histogram-2d/implementations/python/altair.py

Parent Issue: #2012

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (): The 2D histogram displays a bivariate normal distribution on a warm off-white background (#FAF8F1). The plot uses a rectangular bin grid (40×40) with viridis colormap ranging from dark purple (low count: 0) to bright yellow (high count: 1). Title "histogram-2d · altair · anyplot.ai" is clearly visible in dark text. Axis labels "Variable X" and "Variable Y" are readable with dark tick labels (INK_SOFT token #4A4A44). The colorbar legend on the right shows "Count" with large readable text (titleFontSize=18, labelFontSize=16). The strong correlation (0.7) is evident from the diagonal density concentration. All text elements are perfectly legible against the light background—no light-on-light text issues. Legibility verdict: PASS

Dark render (): The same histogram on a warm near-black background (#1A1A17). The title remains clearly visible in light text. Axis labels and tick labels are rendered in light color (INK_SOFT token #B8B7B0), providing excellent contrast against the dark background. The viridis colormap is identical to the light render (only chrome flips, not data colors). The correlation pattern is equally clear and visible. Colorbar text is readable with appropriate light coloring. No dark-on-dark text failures present—all chrome tokens correctly adapted to dark theme. Legibility verdict: PASS

Score: 86/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	9	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	86	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set (title 28px, labels 22px, ticks 18px). All text perfectly readable in both light and dark themes via proper token usage.
• VQ-02: No Overlap (6/6) — All text fully readable with no overlapping elements. Clean spacing throughout.
• VQ-03: Element Visibility (6/6) — Bins perfectly adapted to 40×40 grid density. Colorbar clearly visible with appropriate sizing.
• VQ-04: Color Accessibility (2/2) — Viridis is perceptually uniform and colorblind-safe for continuous data.
• VQ-05: Layout & Canvas (4/4) — Excellent proportions. Plot fills 70% of canvas with balanced margins. Nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — Title follows spec format "histogram-2d · altair · anyplot.ai". Axis labels are descriptive ("Variable X", "Variable Y").
• VQ-07: Palette Compliance (2/2) — Viridis colormap is correct for continuous data. Plot backgrounds exactly match spec: #FAF8F1 (light) / #1A1A17 (dark). All text/chrome colors use proper tokens. Both renders are theme-correct.

Design Excellence (9/20)

• DE-01: Aesthetic Sophistication (4/8) — Uses viridis colormap (correct choice for continuous data), but relies on standard altair defaults without custom design touches. Professional but not sophisticated. No custom color harmonies or typographic hierarchy beyond standard sizing.
• DE-02: Visual Refinement (2/6) — Minimal customization. Standard altair colorbar styling, default legend box fill (ELEVATED_BG). No custom spines adjustment (not applicable to heatmaps), standard grid/axis treatment (gridOpacity=0.0 is appropriate but not refined).
• DE-03: Data Storytelling (3/6) — The heatmap effectively reveals the bivariate correlation pattern (0.7 correlation visible from dense diagonal), but no visual hierarchy or emphasis beyond the standard heatmap presentation. Data is displayed functionally without focal points or interpretive guidance.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D histogram heatmap with rectangular bins.
• SC-02: Required Features (4/4) — Includes colorbar, viridis colormap, binning (40×40), tooltip interaction, proper title/legend.
• SC-03: Data Mapping (3/3) — X/Y axes correctly mapped. Data range visible from -4 to +3.6 on both axes, capturing full distribution.
• SC-04: Title & Legend (3/3) — Title format correct. Colorbar labeled "Count" with appropriate legend styling.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Demonstrates full 2D histogram feature set: binning, density variation through color, clear correlation pattern visible.
• DQ-02: Realistic Context (5/5) — Bivariate normal with 0.7 correlation is realistic and neutral (common in statistical examples, physics, finance).
• DQ-03: Appropriate Scale (4/4) — 2000 points with 40×40 bins creates good density variation (range 0-7 counts per bin). Values are sensible and proportionally accurate.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Simple, linear structure: imports → data generation → chart definition → save. No unnecessary functions or classes.
• CQ-02: Reproducibility (2/2) — Uses for deterministic output.
• CQ-03: Clean Imports (2/2) — Only imports used: os (ANYPLOT_THEME), altair (charting), numpy (random data), pandas (dataframe).
• CQ-04: Code Elegance (2/2) — Appropriate complexity for the task. No over-engineering, no fake functionality.
• CQ-05: Output & API (1/1) — Correctly saves as and with theme suffix.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (5/5) — Expertly uses altair patterns: mark_rect with binning, proper encoding structure, theme-adaptive configuration through configure_axis, configure_legend, configure_view, configure_title. This is canonical altair 2D histogram implementation.
• LM-02: Distinctive Features (2/5) — Uses colorbar legend (distinctive) and tooltips (interactive), but could showcase more altair features. For a 2D histogram, however, the current approach is solid and sufficient.

Score Caps Applied

• None applied — all critical criteria passed.

Strengths

• Perfect theme-adaptive implementation with correct token usage throughout (PAGE_BG, ELEVATED_BG, INK, INK_SOFT)
• Fully readable in both light and dark themes with no legibility issues or dark-on-dark text failures
• Correct colormap choice (viridis) for continuous density data, adhering to style guide
• Idiomatic altair code using proper binning, encoding, and configuration patterns
• Appropriate example data (bivariate normal with correlation) effectively demonstrates the 2D histogram use case
• Correct output file naming and format with theme suffixes
• Proper canvas utilization and layout balance

Weaknesses

• Design Excellence is moderate—uses standard altair defaults without custom styling touches
• Visual refinement minimal—colorbar uses default styling, no custom design choices
• No visual hierarchy or storytelling emphasis—data is presented functionally without focal points

Issues Found

1. DE-01 MODERATE (4/8): Generic styling with viridis defaults, no custom design sophistication
   • Fix: Consider custom colorbar styling, axis refinement, or typography emphasis to elevate design
2. DE-02 MINIMAL (2/6): Default altair styling without visual refinement touches
   • Fix: Add subtle visual touches—custom colorbar frame, axis label styling, or whitespace adjustment
3. DE-03 MODERATE (3/6): Data displayed functionally but no visual storytelling or hierarchy
   • Fix: Add visual emphasis through color contrast, size variation, or annotation to guide viewer attention to the correlation pattern

AI Feedback for Next Attempt

The implementation is technically solid and passes all spec requirements with perfect theme adaptation. To improve Design Excellence scores: (1) enhance colorbar styling with custom frames or gradients, (2) add subtle visual hierarchy through axis refinement or title emphasis, (3) consider annotating the correlation pattern or adding marginal distributions as suggested in spec notes to improve data storytelling.

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

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): Warm off-white background (#FAF8F1) — correct. Title "histogram-2d · altair · anyplot.ai" in dark ink is clearly readable. Axis labels "Variable X" and "Variable Y" are legible; tick labels (−4.0 to 3.6 range) are well-sized and readable against the light surface. The 2D heatmap shows a bivariate normal distribution with ρ=0.7 as a diagonal elongated cluster of rectangular bins. Bins are rendered in a nearly uniform mid-teal viridis color — very little density gradient is visible between the high-density center and sparse edges. Empty bins (count=0) within the cluster appear as white holes, giving a fragmented pixel texture. The viridis colorbar on the right is styled with appropriate font sizes; the "Count" title appears slightly clipped at the right canvas edge. All text is readable against the light background — legibility verdict: PASS.

Dark render (plot-dark.png): Near-black background (#1A1A17) — correct. Title and all axis/tick labels are rendered in light ink (≈#F0EFE8) and are clearly readable against the dark surface. No dark-on-dark failures detected. Data bin colors are identical to the light render (same mid-teal viridis) — data colors are theme-invariant as required. Empty bins now appear as near-black holes. The same poor density gradient applies — the correlated elliptical shape is clear but internal density variation remains invisible. Legibility verdict: PASS.

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

Score: 82/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	11	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	82	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set (28/22/18/16px), readable in both themes; minor deduction for "Count" colorbar title slightly clipped at right edge
• VQ-02: No Overlap (6/6) — No collisions; tick labels well spaced
• VQ-03: Element Visibility (3/6) — Bins are rendered, but nearly uniform mid-teal across the entire distribution; no visible density gradient between high-density center and low-density edges — the primary purpose of a 2D histogram is compromised
• VQ-04: Color Accessibility (2/2) — Viridis is perceptually uniform and CVD-safe
• VQ-05: Layout & Canvas (3/4) — Good proportions; "Count" colorbar title slightly clipped at right canvas edge
• VQ-06: Axis Labels & Title (2/2) — Title format correct; axis labels clear
• VQ-07: Palette Compliance (2/2) — Viridis correct for continuous density data; both backgrounds theme-correct (#FAF8F1 / #1A1A17)

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Genuine theme-adaptation work, styled colorbar with custom font sizes, fill, and border. Raised from default 4; ceiling limited by the uniform-color visual failure.
• DE-02: Visual Refinement (4/6) — No grid (gridOpacity=0.0), no tick marks (tickSize=0), clean view border — above default 2.
• DE-03: Data Storytelling (2/6) — The correlated ellipse shape is visible, but density variation within the distribution is not; the main storytelling goal of a 2D histogram is unmet.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — 2D histogram as rectangular heatmap ✓
• SC-02: Required Features (4/4) — Colorbar, viridis colormap, bins parameter all present ✓
• SC-03: Data Mapping (3/3) — Bivariate normal mapped to X/Y with count aggregation ✓
• SC-04: Title & Legend (3/3) — Title "histogram-2d · altair · anyplot.ai"; colorbar labeled "Count" ✓

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — 2D density concept shown; slight deduction for poor density discrimination with 2000 pts / 40 bins
• DQ-02: Realistic Context (5/5) — Bivariate normal with ρ=0.7 is classic, realistic, and neutral ✓
• DQ-03: Appropriate Scale (4/4) — 2000 points, standard normal range ✓

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) — os, altair, numpy, pandas — all used ✓
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity ✓
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html with scale_factor=3.0 ✓

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — mark_rect + alt.Bin + count() aggregation is the canonical Altair 2D histogram; configure_* API used correctly
• LM-02: Distinctive Features (3/5) — Interactive tooltips (bin range + count), HTML export, theme-adaptive configure_axis/legend/title

Score Caps Applied

• None

Strengths

• Full theme-adaptive chrome: background, axis text, legend, and border all switch correctly between light (#FAF8F1) and dark (#1A1A17) renders
• Correct viridis colormap for continuous density data with well-styled colorbar (sized fonts, filled/bordered legend box)
• Font hierarchy properly set: title 28px, axis labels 22px, tick labels 18px, legend 16px — readable at full resolution in both themes
• Clean Altair API: mark_rect with alt.Bin(maxbins=40) and count() aggregation is the idiomatic approach
• Excellent code quality: flat script, seed fixed, all imports used, dual PNG+HTML output with theme suffix

Weaknesses

• Poor density gradient: with n=2000 and maxbins=40 most bins hold 1–10 counts, collapsing viridis to nearly uniform mid-teal; the primary storytelling goal (revealing density variation) is largely invisible
• White/black "holes" inside the distribution cluster: empty bins (count=0) within the blob create a fragmented pixel pattern — reduce maxbins to ~20 or increase n to 5000+ to fill bins more uniformly
• Colorbar title "Count" slightly clipped at the right canvas edge — reduce chart width by ~50px or add explicit right padding

Issues Found

1. VQ-03 / DE-03 LOW: Nearly uniform teal across all bins — no visible density gradient
   • Fix: Use fewer bins (maxbins=20) or increase data to n=5000 or apply log color scale via alt.Scale(scheme='viridis', type='log') to spread the viridis range over the actual count distribution
2. VQ-05 MINOR: Colorbar "Count" title clipped at right canvas edge
   • Fix: Set width=1550 instead of 1600 to give the colorbar title room, or apply .configure_legend(padding=10)

AI Feedback for Next Attempt

The density gradient is the critical fix: switch to maxbins=20 (or n=5000) to ensure bins hold 5–50+ counts and the viridis colormap shows a clear center-to-edge gradient. Alternatively, add alt.Scale(scheme='viridis', type='log') to the color encoding which naturally spreads low-count variance. Also reduce chart width slightly (1550) to avoid the "Count" colorbar-title clip.

Verdict: APPROVED