feat(plotnine): implement subplot-grid-custom

[github-actions]

Implementation: subplot-grid-custom - plotnine

Implements the plotnine version of subplot-grid-custom.

File: plots/subplot-grid-custom/implementations/plotnine.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 forming an investment portfolio dashboard. The main title "subplot-grid-custom · plotnine · pyplots.ai" appears at the top in bold black text. The layout consists of a large main plot on the left side spanning the full height, showing "Portfolio Value Trend" with a blue line chart displaying portfolio values from ~96,000 to ~103,000 over 60 trading days, with data points marked and a yellow linear regression trend line with confidence band. On the right side are three stacked smaller panels: (1) "Asset Allocation" - a vertical bar chart showing Stocks (~55%), Bonds (~25%), Real Estate (~12%), and Cash (~8%) in blue, yellow, light blue, and coral colors; (2) "Daily Trading Volume" - a bar chart showing volume fluctuations over 60 trading days in blue; (3) "Returns Distribution" - a yellow histogram showing daily returns centered around 0% with a roughly normal distribution. All subplots have white backgrounds with subtle gray grid lines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text readable, main title is excellent, subplot titles and axis labels are clear. Minor: some axis tick labels are slightly small but still readable.
• VQ-02: No Overlap (8/8) - No overlapping text or elements anywhere
• VQ-03: Element Visibility (7/8) - Line, bars, and histogram bins are clearly visible. Points on main plot could be slightly larger.
• VQ-04: Color Accessibility (5/5) - Blue/yellow/coral color scheme is colorblind-safe, good contrast
• VQ-05: Layout Balance (4/5) - Excellent dashboard layout with main plot given prominence. Right column panels are well-proportioned. Minor: slight vertical compression on right panels.
• VQ-06: Axis Labels (2/2) - All axes have descriptive labels with units: "Portfolio Value ($)", "Allocation (%)", "Volume (Units)", "Daily Return (%)"
• VQ-07: Grid & Legend (1/2) - Grid is subtle and appropriate. No legend needed for most plots, but asset allocation chart hides legend (acceptable choice).

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correct custom grid layout with multiple different plot types (line, bar, histogram)
• SC-02: Data Mapping (5/5) - All data correctly mapped to appropriate axes
• SC-03: Required Features (4/5) - Implements custom grid with non-uniform sizing (main plot spans full height, 3 smaller panels stacked). Uses plotnine composition operators (| and /). Minor: could demonstrate rowspan more explicitly.
• SC-04: Data Range (3/3) - All data visible within axis ranges
• SC-05: Legend Accuracy (2/2) - No misleading legends
• SC-06: Title Format (2/2) - Correct format: "subplot-grid-custom · plotnine · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple plot types (line with trend, bar charts, histogram) demonstrating dashboard capability. The portfolio data shows both upward and downward movements.
• DQ-02: Realistic Context (7/7) - Investment portfolio dashboard is an excellent real-world scenario for dashboard-style layouts
• DQ-03: Appropriate Scale (5/5) - Realistic portfolio values (~$100k), reasonable allocation percentages, plausible trading volumes and returns

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plots → composition → save structure
• 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 plotnine API
• CQ-05: Output Correct (0/1) - Saves to "plot.png" correctly ✓

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of plotnine composition operators (| for horizontal, / for vertical stacking), ggplot grammar, stat_smooth for trend line. Could leverage more ggplot2-specific features like faceting alternatives.

Strengths

• Excellent dashboard layout demonstrating custom grid with non-uniform cell sizes using plotnine's composition operators
• Clear visual hierarchy with main plot given appropriate prominence
• Real-world investment portfolio scenario is highly relevant to the spec's executive dashboard application
• Clean code structure following KISS principles with shared theme definitions
• Good use of stat_smooth for trend line with confidence band in main plot
• Colorblind-safe color palette with good contrast

Weaknesses

• Text sizes on right-panel subplots are slightly smaller than optimal for the canvas size
• The grid layout uses | and / operators which create a 2-column layout, but doesn't demonstrate explicit colspan/rowspan parameters mentioned in spec notes
• Library features score could be higher with additional ggplot2 grammar features

Verdict: APPROVED