feat(pygal): implement scatter-categorical

[github-actions]

Implementation: scatter-categorical - pygal

Implements the pygal version of scatter-categorical.

File: plots/scatter-categorical/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a categorical scatter plot visualizing Iris-like flower measurements. Three distinct species clusters are shown: Setosa (blue, #306998) in the lower-left with small petal dimensions (length ~1-2cm, width ~0.1-0.5cm), Versicolor (yellow, #FFD43B) in the middle region (length ~3-5cm, width ~0.9-1.9cm), and Virginica (green, #2ca02c) in the upper-right (length ~4.5-7cm, width ~1.4-3cm). The title "scatter-categorical · pygal · pyplots.ai" appears at the top center. Axis labels include units: "Petal Length (cm)" on X-axis and "Petal Width (cm)" on Y-axis. A legend with colored squares is positioned in the top-left corner. Subtle grid lines aid readability. The markers are well-sized dots with transparency (opacity 0.7) allowing overlap visibility.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and legend are clearly readable; tick labels slightly small but acceptable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Markers well-sized with good alpha; dots_size=12 is appropriate for 120 points
• VQ-04: Color Accessibility (5/5) - Blue/yellow/green palette is colorblind-safe
• VQ-05: Layout Balance (5/5) - Good use of canvas, balanced margins, legend near plot
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Petal Length (cm)", "Petal Width (cm)"
• VQ-07: Grid & Legend (0/2) - Legend overlaps with plot area and first data point region

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct XY scatter chart with categorical coloring
• SC-02: Data Mapping (5/5) - X/Y correctly assigned to petal length/width
• SC-03: Required Features (5/5) - Categorical colors, legend, transparency all present
• SC-04: Data Range (3/3) - All data points visible within axes
• SC-05: Legend Accuracy (2/2) - Legend correctly labels all three species
• SC-06: Title Format (2/2) - Uses correct format: "scatter-categorical · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct clusters with different distributions; could show more overlap between categories
• DQ-02: Realistic Context (7/7) - Iris flower measurements - classic, neutral scientific dataset
• DQ-03: Appropriate Scale (4/5) - Values realistic for iris petals; Setosa width slightly low but plausible

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only numpy, pygal, and Style used
• CQ-04: No Deprecated API (1/1) - Current pygal API used
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses pygal's XY chart and custom Style; could leverage more pygal-specific features like tooltips

Strengths

• Excellent use of Iris-like data providing a realistic, scientifically meaningful context
• Clean three-cluster separation clearly demonstrates categorical grouping
• Good color palette (Python colors) that is colorblind-safe
• Proper use of pygal's custom Style with appropriately scaled font sizes for 4800x2700 canvas
• Correct title format and descriptive axis labels with units
• Alpha transparency (0.7) allows visibility of any overlapping points

Weaknesses

• Legend positioned in top-left overlaps with the plot area grid region; consider legend_at_bottom=True or repositioning
• Marker stroke_width=0 removes outlines which could help distinguish overlapping points

Verdict: APPROVED