feat(pygal): implement raincloud-basic

[github-actions]

Implementation: raincloud-basic - pygal

Implements the pygal version of raincloud-basic.

File: plots/raincloud-basic/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a raincloud visualization for three treatment groups (Control, Treatment A, Treatment B) showing reaction time data in milliseconds. Each group uses a distinct color: blue (#306998) for Control, yellow (#FFD43B) for Treatment A, and green (#4CAF50) for Treatment B. For each group, from left to right: jittered data points ("rain") appear on the left side, followed by a box plot (showing median, quartiles, and whiskers) in the center, and a half-violin density plot ("cloud") on the right side. The title "raincloud-basic · pygal · pyplots.ai" appears at the top. The Y-axis shows "Reaction Time (ms)" ranging from 100-700, and the X-axis shows "Treatment Group" with the three category labels. The layout has subtle grid lines on a white background.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text is readable at full size; title, axis labels, and tick marks are clearly visible with appropriate font sizes for the 4800x2700 canvas
• VQ-02: No Overlap (8/8) - No overlapping text elements; labels are well-spaced
• VQ-03: Element Visibility (7/8) - Data points are clearly visible with good sizing; the dots_size=32 works well for the data density. Minor: box plot elements could be slightly more prominent
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette using blue, yellow, and green which are distinguishable
• VQ-05: Layout Balance (4/5) - Good use of canvas space; plot fills appropriate area. Minor: some extra whitespace on right edge
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Reaction Time (ms)" and "Treatment Group"
• VQ-07: Grid & Legend (1/2) - Grid is subtle and appropriate; no legend shown (acceptable since colors map to x-axis categories, but could be cleaner)

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct raincloud plot with all three elements: half-violin (cloud), box plot, and jittered points (rain)
• SC-02: Data Mapping (5/5) - Categories on X-axis, values (reaction times) on Y-axis correctly assigned
• SC-03: Required Features (4/5) - Has half-violin, box plot with median/quartiles/whiskers, and jittered points. Minor: the spec notes "cloud on top, rain below" for horizontal or "cloud on right, rain on left" for vertical - implementation follows vertical correctly
• SC-04: Data Range (3/3) - Y-axis range (100-750) shows all data appropriately
• SC-05: Legend Accuracy (1/2) - No explicit legend, relies on x-axis labels (functional but not ideal)
• SC-06: Title Format (2/2) - Correct format: "raincloud-basic · pygal · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct distributions with different means and spreads; includes outliers. Data demonstrates the value of raincloud plots well
• DQ-02: Realistic Context (7/7) - Reaction times for control vs treatment groups is a classic, realistic psychology experiment scenario
• DQ-03: Appropriate Scale (5/5) - Reaction times in 180-700ms range are perfectly realistic for human response time studies

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save. While the code is more complex due to manual raincloud construction, no unnecessary functions or classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) and secondary seeds for jitter
• CQ-03: Clean Imports (2/2) - Only numpy, pygal, and pygal.style.Style used
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (1/1) - Saves as both plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal's XY chart with custom styling to build a complex visualization. While impressive manual construction, it doesn't leverage pygal-specific features like built-in interactivity or tooltips

Strengths

• Excellent manual implementation of raincloud plot using pygal's XY chart primitives
• Clean KDE computation using Silverman's rule for bandwidth estimation
• Correct vertical raincloud layout with cloud on right, rain on left as per spec
• Well-scaled font sizes for the 4800x2700 canvas
• Colorblind-safe color palette
• Realistic reaction time data with appropriate outliers showing distribution differences

Weaknesses

• Box plot elements could be slightly thicker/more visible relative to the half-violin
• No interactive tooltips leveraging pygal's SVG interactivity capabilities

Verdict: APPROVED