feat(seaborn): implement andrews-curves

[github-actions]

Implementation: andrews-curves - seaborn

Implements the seaborn version of andrews-curves.

File: plots/andrews-curves/implementations/seaborn.py

Parent Issue: #2859

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays Andrews curves for the Iris dataset with three species (setosa, versicolor, virginica) represented by different colors. The x-axis shows t values from -π to π with appropriate π notation labels. The y-axis shows f(t) values ranging from approximately -4 to 5. Blue curves represent setosa, yellow/gold curves represent versicolor, and red/coral curves represent virginica. The curves show clear separation between species groups - setosa curves tend toward negative f(t) values in the middle region while virginica curves trend higher. The legend is positioned in the upper right corner with "Species" as the title. The plot has a white grid background with subtle gridlines, and the title follows the required format. All 150 observations from the Iris dataset are visualized as individual curves with appropriate transparency to show density patterns.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Lines visible with good alpha=0.4 transparency for 150 curves, linewidth=1.5 is appropriate
• VQ-04: Color Accessibility (5/5) - Blue (#306998), Yellow (#FFD43B), Red (#E74C3C) palette is colorblind-safe (distinguishable by luminance)
• VQ-05: Layout Balance (5/5) - Good 16:9 aspect ratio, plot fills canvas well, balanced margins
• VQ-06: Axis Labels (1/2) - "t" and "f(t)" are mathematical notation appropriate for Andrews curves but lack descriptive context
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), legend well placed but overlaps slightly with some curve endpoints

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct Andrews curves Fourier transformation visualization
• SC-02: Data Mapping (5/5) - Variables correctly transformed using Fourier series coefficients
• SC-03: Required Features (5/5) - Normalization, transparency, color by category, t from -π to π all implemented
• SC-04: Data Range (3/3) - Axes show all data appropriately
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies species
• SC-06: Title Format (2/2) - Uses exact format "andrews-curves · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows cluster separation and overlap patterns well, could show outliers more prominently
• DQ-02: Realistic Context (7/7) - Iris dataset is classic multivariate data, perfect for demonstrating Andrews curves
• DQ-03: Appropriate Scale (4/5) - Normalized values are appropriate; scale is sensible

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → transform → plot → save
• CQ-02: Reproducibility (2/3) - Uses fixed dataset (iris) but no random seed explicitly set (not strictly needed here since data is deterministic)
• CQ-03: Clean Imports (2/2) - All imports used appropriately
• CQ-04: No Deprecated API (1/1) - Uses current seaborn API
• CQ-05: Output Correct (1/1) - Saves as plot.png with dpi=300

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of sns.lineplot with units parameter for individual curves and sns.load_dataset for data loading, but Andrews curves transformation is manual (not a seaborn built-in)

Strengths

• Excellent implementation of Andrews curves Fourier transformation algorithm
• Perfect use of seaborn lineplot with units parameter to plot individual curves without aggregation
• Good choice of colorblind-safe color palette with appropriate transparency
• Clean separation of species clusters visible in the visualization
• Correct mathematical notation for x-axis labels using π symbols
• Well-structured code following KISS principles

Weaknesses

• Axis labels use mathematical notation only (t, f(t)) without descriptive context for newcomers
• Legend could be positioned to avoid any potential overlap with curve endpoints

Verdict: APPROVED