feat(pygal): implement subplot-grid

[github-actions]

Implementation: subplot-grid - pygal

Implements the pygal version of subplot-grid.

File: plots/subplot-grid/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 2x2 grid of four distinct charts with a main title "subplot-grid · pygal · pyplots.ai" centered at the top in dark gray text.

Top-left: Line chart titled "Monthly Revenue ($K)" showing an upward trend from January (~120K) to December (~248K). Blue line with visible data point markers, gray gridlines, x-axis labeled "Month" and y-axis labeled "Revenue ($K)".

Top-right: Bar chart titled "Sales by Category ($K)" with five golden/yellow vertical bars representing Electronics (~45K), Apparel (~33K), Home (~28K), Sports (~20K), and Books (~15K). X-axis labeled "Category", y-axis labeled "Sales ($K)".

Bottom-left: Scatter plot titled "Ad Spend vs Return on Investment" showing ~30 blue dots distributed across the chart. X-axis labeled "Ad Spend ($K)" ranging 0-50, y-axis labeled "ROI (%)" ranging 0-6. Shows positive correlation trend.

Bottom-right: Histogram titled "Daily Order Volume Distribution" with golden/yellow bars showing a roughly normal distribution of daily orders. X-axis labeled "Orders per Day" (60-280 range), y-axis labeled "Frequency" (0-70 range). Peak frequency around 150-160 orders.

Overall: Clean white background, light gray plot backgrounds (#fafafa), consistent styling across all charts, well-balanced layout with equal cell sizes.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text readable; titles, axis labels, and tick marks are clear at full size. Slightly smaller than ideal for publication quality.
• VQ-02: No Overlap (8/8) - No overlapping text elements; x-axis labels are spaced well, all tick labels readable.
• VQ-03: Element Visibility (7/8) - Markers and bars are well-sized; scatter plot dots are visible but could be slightly larger for 30 points.
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) are colorblind-safe and provide good contrast.
• VQ-05: Layout Balance (4/5) - Good 2x2 grid layout with balanced margins; slight excess whitespace at edges.
• VQ-06: Axis Labels (2/2) - All axes have descriptive labels with units (e.g., "Revenue ($K)", "ROI (%)", "Orders per Day").
• VQ-07: Grid & Legend (1/2) - Grid is subtle and appropriate; no legends needed (single series per chart), but some charts could benefit from guide lines.

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correct subplot grid with four distinct chart types (line, bar, scatter, histogram).
• SC-02: Data Mapping (5/5) - X/Y correctly assigned in all four subplots.
• SC-03: Required Features (5/5) - Configurable 2x2 grid, different plot types per cell, clear titles for each subplot, consistent spacing.
• SC-04: Data Range (3/3) - All axes show complete data ranges without clipping.
• SC-05: Legend Accuracy (2/2) - No legends needed for single-series charts; appropriate decision.
• SC-06: Title Format (1/2) - Uses correct format "subplot-grid · pygal · pyplots.ai" but rendered via PIL rather than native pygal (acceptable workaround).

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows four different chart types demonstrating grid versatility; histogram shows distribution, scatter shows correlation, line shows trend, bar shows comparison. Missing: shared axes example.
• DQ-02: Realistic Context (7/7) - Excellent business dashboard scenario with realistic metrics: revenue trends, category sales, ad ROI, and order distribution.
• DQ-03: Appropriate Scale (4/5) - Values are realistic for business context; revenue in hundreds of thousands, ROI percentages reasonable.

Code Quality (8/10 pts)

• CQ-01: KISS Structure (2/3) - Linear flow but uses helper functions/loops for chart creation; slightly more complex than ideal KISS structure.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for deterministic data.
• CQ-03: Clean Imports (2/2) - All imports are used (pygal, numpy, cairosvg, PIL).
• CQ-04: No Deprecated API (1/1) - Uses current pygal API.
• CQ-05: Output Correct (0/1) - Saves as "plot.png" but also creates "plot.html"; should use explicit path.

Library Features (5/5 pts)

• LF-01: Distinctive Features (5/5) - Excellent use of pygal's distinctive features: SVG rendering with cairosvg conversion, Style customization, multiple chart types (Line, Bar, XY, Histogram), interactive HTML output with embedded SVG.

Strengths

• Excellent demonstration of pygal's versatility with four distinct chart types (Line, Bar, XY scatter, Histogram) in a cohesive grid
• Creative solution for subplot grid using PIL composition since pygal lacks native subplots
• Realistic business dashboard scenario with interconnected metrics (revenue, sales, advertising ROI, order volume)
• Proper use of Python brand colors (#306998 blue, #FFD43B yellow) maintaining visual consistency
• Both PNG and interactive HTML outputs generated, showcasing pygal's web-native SVG capabilities
• Clean, readable axis labels with appropriate units throughout all subplots

Weaknesses

• Chart titles repeat y-axis information (e.g., "Monthly Revenue ($K)" title + "Revenue ($K)" y-label) - could be more concise
• Scatter plot dots could be slightly larger for improved visibility at the 30-point density level
• Code structure uses loops for chart rendering which adds slight complexity beyond pure KISS style

Verdict: APPROVED