feat(seaborn): implement contour-density

[github-actions]

Implementation: contour-density - seaborn

Implements the seaborn version of contour-density.

File: plots/contour-density/implementations/seaborn.py

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 2D density contour visualization with filled contours using the viridis colormap (purple for low density transitioning through blue/green to yellow for high density). Two distinct cluster centers are clearly visible: a larger main cluster centered around (5, 4-5) and a smaller secondary cluster around (9-10, 7-8). White semi-transparent scatter points are overlaid on the contours to show the underlying data distribution. The title "contour-density · seaborn · pyplots.ai" appears at the top. Both axes have descriptive labels with units: "X Variable (units)" and "Y Variable (units)". A subtle dashed grid is present in the background. The plot fills the canvas well with good proportions.

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, and tick labels are all clearly readable at appropriate sizes (24pt title, 20pt labels, 16pt ticks)
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Contour levels are well-defined, scatter overlay uses appropriate size (s=15) and alpha (0.3) for 450 points
• VQ-04: Color Accessibility (5/5) - Viridis colormap is colorblind-safe with excellent contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "X Variable (units)", "Y Variable (units)"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and appropriate, but no legend/colorbar to indicate density scale

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct density contour plot using KDE
• SC-02: Data Mapping (5/5) - X and Y variables correctly mapped
• SC-03: Required Features (5/5) - Has filled contours, contour lines, and scatter overlay as mentioned in spec notes
• SC-04: Data Range (3/3) - All data visible within axes
• SC-05: Legend Accuracy (2/2) - No legend needed for this plot type (density is shown via color gradient)
• SC-06: Title Format (2/2) - Correct format: "contour-density · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows two clusters of different sizes/densities, demonstrating the power of density visualization
• DQ-02: Realistic Context (5/7) - Generic "X Variable" and "Y Variable" labels are plausible but not a real-world scenario
• DQ-03: Appropriate Scale (5/5) - Values in reasonable ranges (0-14 for X, 0-12 for Y)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports: matplotlib, numpy, seaborn
• CQ-04: No Deprecated API (1/1) - Uses current seaborn API
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Excellent use of seaborn's kdeplot with both filled contours and line contours, demonstrating library's statistical visualization strengths

Strengths

• Excellent use of seaborn's kdeplot for 2D kernel density estimation with both filled and line contours
• Two-cluster data effectively demonstrates density visualization capabilities
• Viridis colormap provides excellent colorblind accessibility and visual appeal
• Scatter overlay adds context without cluttering the visualization
• Clean, well-structured code following KISS principles

Weaknesses

• No colorbar/legend to show the density scale mapping (minor issue for this plot type)
• Generic axis labels ("X Variable", "Y Variable") rather than a real-world scenario

Verdict: APPROVED