feat(matplotlib): implement scatter-categorical

[github-actions]

Implementation: scatter-categorical - matplotlib

Implements the matplotlib version of scatter-categorical.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a categorical scatter plot with three distinct species groups displayed using different colors and marker shapes. Species A (blue circles) appears in the lower-left cluster with petal lengths ~1-2 cm and widths ~0.1-0.5 cm. Species B (yellow/gold squares) occupies the middle region with petal lengths ~3-5 cm and widths ~1-1.8 cm. Species C (teal triangles) forms the upper-right cluster with petal lengths ~4.5-7 cm and widths ~1.4-3.1 cm. The title reads "scatter-categorical · matplotlib · pyplots.ai" at the top. Axis labels show "Petal Length (cm)" and "Petal Width (cm)" with units. A legend in the upper-left clearly identifies the three species. Grid lines are subtle with dashed style. The overall layout is well-balanced with good use of the canvas space.

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt, legend at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, all labels clearly separated
• VQ-03: Element Visibility (8/8) - Marker size s=200 is appropriate for 120 points (40 per group), alpha=0.8 provides good visibility with slight transparency
• VQ-04: Color Accessibility (4/5) - Blue (#306998), yellow (#FFD43B), and teal (#2AA198) are distinguishable and avoid red-green confusion. Different marker shapes (circle, square, triangle) provide additional distinction. Minor deduction as yellow could be slightly challenging against white background for some viewers
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, legend well-positioned in upper-left without overlapping data
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with units: "Petal Length (cm)" and "Petal Width (cm)"
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3, dashed), legend well placed with framealpha=0.9. Minor deduction: legend could have slightly more contrast/padding

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct categorical scatter plot with distinct colors per category
• SC-02: Data Mapping (5/5) - X and Y correctly assigned to continuous numeric variables, color mapped to category
• SC-03: Required Features (5/5) - Distinct colors per category, legend included, varying marker shapes for additional distinction, alpha transparency for overlaps
• SC-04: Data Range (3/3) - Axes show all data points with appropriate padding
• SC-05: Legend Accuracy (2/2) - Legend correctly maps colors/shapes to Species A, B, C
• SC-06: Title Format (2/2) - Uses correct format "scatter-categorical · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct categories with clear separation, demonstrates correlation within groups. Minor deduction: could show overlapping groups to demonstrate how colors distinguish overlapping data
• DQ-02: Realistic Context (7/7) - Iris-like petal measurements scenario is a classic, neutral, scientific example
• DQ-03: Appropriate Scale (4/5) - Petal measurements are realistic (1-7 cm length, 0-3 cm width). Minor deduction: the gap between Species A and B/C is quite large (1-2cm to 3-5cm jump)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API
• CQ-05: Output Correct (1/1) - Saves as plot.png with dpi=300

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses standard scatter() with colors and markers. Could leverage matplotlib-specific features like patheffects, custom markers, or colormaps more distinctively

Strengths

• Excellent use of different marker shapes (circle, square, triangle) for additional category distinction beyond color
• Clean, well-organized code following KISS principles
• Realistic Iris-like data scenario that is scientifically relevant and neutral
• White edge colors on markers improve visibility and separation
• Proper font sizing throughout for high-resolution output

Weaknesses

• Library features could be more distinctive - matplotlib offers patheffects, custom marker paths, or gradient fills that are not utilized
• Yellow markers with white background could have slightly better contrast

Verdict: APPROVED