feat(altair): implement subplot-grid-custom

[github-actions]

Implementation: subplot-grid-custom - altair

Implements the altair version of subplot-grid-custom.

File: plots/subplot-grid-custom/implementations/altair.py

Parent Issue: #2856

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a custom subplot grid layout with 5 different visualizations arranged in 3 rows. The top row shows a large "Daily Price Trend (Main View)" line chart spanning the full width with a blue (#306998) line showing price movements from January to late April 2024. The middle row contains two charts side by side: a yellow bar chart showing "Trading Volume" and a blue histogram showing "Return Distribution" with daily return percentages. The bottom row has a scatter plot titled "Feature Correlation" showing blue points demonstrating positive correlation between Feature A and Feature B, and a donut chart "Category Breakdown" with four colored segments (blue, yellow, light blue, golden). A legend for the category breakdown appears at the top right. The main title "subplot-grid-custom · altair · pyplots.ai" is displayed at the top center. All text is readable, axes are properly labeled with units where appropriate (Price $, Volume, Daily Return %, Feature A/B).

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text is clearly readable; title is appropriately sized at 30pt, subplot titles at 18-22pt, axis labels at 14-18pt. Slightly smaller than ideal for some tick labels but still legible.
• VQ-02: No Overlap (8/8) - No overlapping text elements; all labels and titles are well-positioned
• VQ-03: Element Visibility (7/8) - Line chart is well-sized (strokeWidth=4), scatter points are appropriately sized (size=120), histogram and bar charts are clear. Donut chart could be slightly larger.
• VQ-04: Color Accessibility (5/5) - Uses blue (#306998), yellow (#FFD43B), light blue (#4B8BBE) - colorblind-safe palette with good contrast
• VQ-05: Layout Balance (4/5) - Good overall layout with clear visual hierarchy. Main chart appropriately larger. Slight imbalance with legend placement far right.
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Price ($)", "Volume", "Daily Return (%)", "Feature A", "Feature B"
• VQ-07: Grid & Legend (1/2) - No grid lines (clean look), but legend for category breakdown is placed far from the donut chart

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements custom subplot grid with non-uniform cell sizes (main chart wider)
• SC-02: Data Mapping (5/5) - X/Y correctly assigned for all subplots (time series, histogram, scatter, categorical)
• SC-03: Required Features (4/5) - Implements colspan-style layout with main chart spanning full width, multiple plot types, dashboard-style layout. Minor: could show rowspan as well.
• SC-04: Data Range (3/3) - All axes show complete data ranges
• SC-05: Legend Accuracy (2/2) - Legend labels correctly match category names
• SC-06: Title Format (2/2) - Uses exact format "subplot-grid-custom · altair · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows diverse plot types (line, bar, histogram, scatter, donut) demonstrating grid flexibility. Could include more variation in grid patterns.
• DQ-02: Realistic Context (7/7) - Financial dashboard scenario with price, volume, returns, correlations, and product breakdown - highly plausible real-world use case
• DQ-03: Appropriate Scale (4/5) - Price around $75-110 (realistic stock), volume 500-2000 (reasonable), returns -6% to +6% (realistic daily). Correlation features slightly generic.

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → charts → layout → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports: altair, numpy, pandas
• CQ-04: No Deprecated API (0/1) - Uses .properties(title=...) which works but could use more modern patterns
• CQ-05: Output Correct (0/1) - Saves as plot.png and plot.html correctly

Library Features (5/5 pts)

• LF-01: Distinctive Features (5/5) - Excellent use of Altair's declarative syntax: vconcat/hconcat for layout composition, resolve_scale for independent axes, mark_arc for donut, comprehensive encoding with explicit type annotations (:Q, :T, :N)

Strengths

• Excellent implementation of dashboard-style grid using Altair's native vconcat/hconcat composition
• Strong visual hierarchy with main chart appropriately larger than detail panels
• Good use of consistent color scheme (Python blue/gold theme)
• Comprehensive financial dashboard scenario with meaningful interconnected data
• Clean declarative code structure showcasing Altair's strengths

Weaknesses

• Legend for category breakdown appears isolated from the donut chart (top-right instead of near the chart)
• Could demonstrate rowspan capability (e.g., a tall sidebar chart) in addition to colspan
• Tick labels on x-axis of main chart are dense and could benefit from rotation or fewer ticks

Verdict: APPROVED