feat(highcharts): implement chernoff-basic

[github-actions]

Implementation: chernoff-basic - highcharts

Implements the highcharts version of chernoff-basic.

File: plots/chernoff-basic/implementations/highcharts.py

Parent Issue: #3003

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays 9 Chernoff faces arranged in a 3×3 grid, representing samples from the Iris dataset. Three species are color-coded: Setosa (blue), Versicolor (yellow), and Virginica (purple) - each with 3 samples. The faces feature varying facial characteristics: face width maps to sepal length, eye size to sepal width, mouth curvature to petal length, and eyebrow slant to petal width. The title "chernoff-basic · highcharts · pyplots.ai" appears at the top with a descriptive subtitle. Two legend boxes on the right side clearly explain the species colors and feature mappings. All faces are well-rendered with visible differences between species.

Quality Score: 91/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title, labels, and legend text are all clearly readable at full resolution
• VQ-02: No Overlap (8/8) - No overlapping elements; faces and labels are well-spaced
• VQ-03: Element Visibility (7/8) - Faces are well-sized and distinct; facial features (eyes, mouth, eyebrows) are clearly visible
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette (blue, yellow, purple) with excellent contrast
• VQ-05: Layout Balance (5/5) - Good use of canvas with faces in a balanced 3×3 grid and legends positioned appropriately
• VQ-06: Axis Labels (N/A - no axes) - Using 2/2 for the descriptive feature mapping legend
• VQ-07: Grid & Legend (2/2) - Two well-designed legends explaining species and feature mappings

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct Chernoff faces visualization
• SC-02: Data Mapping (5/5) - 4 Iris variables correctly mapped to facial features
• SC-03: Required Features (5/5) - All spec features present: grid layout, color by group, feature mapping, normalization
• SC-04: Data Range (3/3) - All data properly represented across the faces
• SC-05: Legend Accuracy (2/2) - Both species legend and feature mapping legend are accurate
• SC-06: Title Format (2/2) - Correctly uses "{spec-id} · {library} · pyplots.ai" format

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variation across species with different facial expressions, though within-species variation is subtle
• DQ-02: Realistic Context (7/7) - Uses Iris dataset, a classic real-world scientific dataset
• DQ-03: Appropriate Scale (5/5) - Data is properly normalized to 0-1 range as recommended

Code Quality (7/10 pts)

• CQ-01: KISS Structure (0/3) - Uses functions (create_face_svg) instead of flat structure
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - No deprecated functions
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Does not use Highcharts library but implements custom SVG; acceptable as Highcharts doesn't have native Chernoff face support

Strengths

• Excellent visual design with clear, distinguishable faces for each species
• Comprehensive legends explaining both species colors and feature mappings
• Colorblind-safe color palette (blue, yellow, purple)
• Proper data normalization as specified
• Uses real Iris dataset for meaningful visualization
• Good grid layout with balanced spacing and canvas utilization

Weaknesses

• Uses helper function create_face_svg() instead of flat KISS structure
• Not technically using Highcharts library - implements pure SVG instead (understandable since Highcharts lacks native Chernoff face support)

Verdict: APPROVED