feat(pygal): implement cat-strip

[github-actions]

Implementation: cat-strip - pygal

Implements the pygal version of cat-strip.

File: plots/cat-strip/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a categorical strip plot showing performance scores (y-axis, ranging from 40-100) across four departments (x-axis: Sales, Engineering, Marketing, Support). Each department's data points are shown as colored dots with horizontal jitter to prevent overlap: Sales in blue (#306998), Engineering in yellow (#FFD43B), Marketing in green (#4CAF50), and Support in pink (#E91E63). The title "cat-strip · pygal · pyplots.ai" appears at the top. The plot has a clean white background with subtle horizontal grid lines. A legend at the bottom identifies each department by color. The y-axis is labeled "Performance Score" and x-axis is labeled "Department". Each category has approximately 25 data points showing varied distributions - Marketing shows the widest spread including an outlier around 40, while Engineering shows a tighter cluster around 80-90.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title and labels are clearly readable at full size, good font sizing for the canvas
• VQ-02: No Overlap (8/8) - No overlapping text elements, jitter effectively separates points
• VQ-03: Element Visibility (7/8) - Dots are well-sized (dots_size=12), visible but could be slightly larger for 100 points
• VQ-04: Color Accessibility (5/5) - Four distinct colors that work for colorblind users (blue, yellow, green, pink)
• VQ-05: Layout Balance (5/5) - Good canvas utilization, plot fills appropriate space with balanced margins
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("Performance Score", "Department") but no units
• VQ-07: Grid & Legend (1/2) - Grid is subtle, legend at bottom is well-placed but slightly far from data

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct categorical strip plot with jittered points
• SC-02: Data Mapping (5/5) - Categories on x-axis, numeric values on y-axis as specified
• SC-03: Required Features (5/5) - Jitter applied, individual points visible, distributions shown per category
• SC-04: Data Range (3/3) - Y-axis range (35-105) shows all data points including outlier at 40
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies all four departments
• SC-06: Title Format (2/2) - Uses correct format "cat-strip · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows different distributions per category, includes outlier in Marketing, varied spreads
• DQ-02: Realistic Context (7/7) - Performance scores across departments is a realistic business scenario
• DQ-03: Appropriate Scale (4/5) - Scores 40-100 are realistic, though clipping to 40 minimum slightly artificial

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure without functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy, pygal, and Style imported, all used
• CQ-04: No Deprecated API (1/1) - Modern pygal API usage
• CQ-05: Output Correct (0/1) - Saves as plot.png but path should be verified

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses XY chart, custom Style, legend_at_bottom, x_value_formatter for category labels. Good use of pygal but no advanced SVG interactivity or animations.

Strengths

• Excellent jitter implementation using XY chart with calculated x-positions
• Clean, professional color scheme with four distinct, accessible colors
• Proper title format following pyplots.ai convention
• Realistic business context (department performance scores)
• Good distribution variation showing different spreads and an outlier
• Well-configured custom Style with appropriate font sizes for large canvas

Weaknesses

• Axis labels lack units (could be "Performance Score (%)" if percentages)
• Grid could be slightly more subtle (current alpha via style is acceptable but could improve)

Verdict: APPROVED