feat(bokeh): implement subplot-grid-custom

[github-actions]

Implementation: subplot-grid-custom - bokeh

Implements the bokeh version of subplot-grid-custom.

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

Parent Issue: #2856

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a dashboard-style custom subplot grid with 5 panels. The main title "subplot-grid-custom · bokeh · pyplots.ai" is centered at the top. The top row contains two panels: (1) "Price Trend Over Time" - a large blue line chart with scatter points showing stock price declining from ~108 to ~80 over 100 days, and (2) "Daily Trading Volume" - yellow vertical bars showing volume fluctuating between 1-5 million. The bottom row has three smaller panels: (1) "Returns Distribution" - a green histogram showing approximately normal distribution of daily returns from -6% to +4%, (2) "Sales by Product Category" - blue vertical bars for Products A-D with varying heights (45, 72, 38, 65 units), and (3) "Variable Correlation Analysis" - coral/red scatter points showing positive correlation. All panels have readable axis labels with units, subtle gray grid lines, and gray outlines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - all text readable, font sizes appropriate for large canvas, slight reduction as some labels could be slightly larger
• VQ-02: No Overlap (8/8) - no overlapping text elements anywhere
• VQ-03: Element Visibility (7/8) - markers and bars well-sized, scatter points visible with good alpha; minor: histogram bins could have slightly more contrast
• VQ-04: Color Accessibility (5/5) - uses blue, yellow, green, coral - distinct and colorblind-safe palette
• VQ-05: Layout Balance (4/5) - dashboard fills canvas well with varied panel sizes demonstrating custom grid; minor asymmetry in bottom row widths
• VQ-06: Axis Labels (2/2) - descriptive labels with units (Price ($), Volume (millions), Daily Return (%), Units Sold)
• VQ-07: Grid & Legend (1/2) - grids subtle at alpha 0.3; no legends needed for these single-series plots but could benefit from subplot outlines being more subtle

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - correctly implements custom subplot grid with non-uniform cell sizes (larger main chart, smaller supporting views)
• SC-02: Data Mapping (5/5) - X/Y correctly assigned for all five subplot types
• SC-03: Required Features (5/5) - demonstrates colspan concept (main time series wider), multiple plot types (line, bar, histogram, scatter), dashboard-style layout
• SC-04: Data Range (3/3) - all axes show complete data ranges
• SC-05: Legend Accuracy (1/2) - no legends shown (not strictly needed but could add series labels)
• SC-06: Title Format (2/2) - uses correct "subplot-grid-custom · bokeh · pyplots.ai" format

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - shows 5 different plot types demonstrating grid flexibility; could add a plot spanning multiple rows for full rowspan demonstration
• DQ-02: Realistic Context (7/7) - excellent financial dashboard scenario with price, volume, returns, sales - cohesive and realistic
• DQ-03: Appropriate Scale (4/5) - values realistic (prices ~80-110, volume 1-5M, returns ±6%), product sales could have more realistic scale

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - clean imports → data → plots → layout → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - all imports are used
• CQ-04: No Deprecated API (1/1) - uses current Bokeh API
• CQ-05: Output Correct (1/1) - saves as plot.png

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - uses Bokeh's row/column layouts and ColumnDataSource; could leverage more Bokeh-specific features like hover tools or linked axes for a dashboard

Strengths

• Excellent dashboard-style layout demonstrating non-uniform subplot sizes with larger main chart and smaller detail panels
• Consistent visual styling across all subplots with matching font sizes, grid alpha, and outline styling
• Realistic financial dashboard scenario with coherent data story (price, volume, returns analysis)
• Clean KISS code structure with proper use of ColumnDataSource for all data
• Good color palette using Python blue/yellow theme with accent colors for variety

Weaknesses

• Bottom row panels have slight width inconsistency (1500+1500+1600 vs top row) creating minor visual imbalance
• Does not demonstrate rowspan (tall sidebar plot) which is mentioned in the spec notes
• Could leverage more Bokeh-specific interactive features like linked brushing or hover tooltips

Verdict: APPROVED