feat(matplotlib): implement scatter-regression-lowess

[github-actions]

Implementation: scatter-regression-lowess - matplotlib

Implements the matplotlib version of scatter-regression-lowess.

File: plots/scatter-regression-lowess/implementations/matplotlib.py

Parent Issue: #2855

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter plot with 200 blue data points (#306998) with white edges and moderate transparency (alpha 0.6). A thick yellow/gold LOWESS curve (#FFD43B, linewidth 4) smoothly traces through the data, capturing the non-linear pattern. The data shows a relatively flat region from x=0-5, followed by an accelerating upward trend from x=5-10. The title "scatter-regression-lowess · matplotlib · pyplots.ai" appears at the top. X-axis is labeled "Measurement Index" (0-10), Y-axis is labeled "Response Value" (approximately -1 to 17.5). A legend in the upper left identifies "Data points" and "LOWESS fit". A subtle dashed grid (alpha 0.3) aids readability.

Quality Score: 95/100

Criteria Checklist

Visual Quality (39/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, labels 20pt, ticks 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Marker size s=100 and alpha=0.6 appropriate for 200 points
• VQ-04: Color Accessibility (5/5) - Blue/yellow contrast is colorblind-safe
• VQ-05: Layout Balance (5/5) - Good proportions, plot fills canvas appropriately
• VQ-06: Axis Labels (1/2) - Descriptive labels but missing units
• VQ-07: Grid & Legend (2/2) - Subtle grid (alpha 0.3), well-placed legend

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot with LOWESS regression curve
• SC-02: Data Mapping (5/5) - X/Y correctly assigned
• SC-03: Required Features (5/5) - All spec features present: distinct LOWESS curve, transparency, moderate smoothing (frac=0.3)
• SC-04: Data Range (3/3) - All data visible
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies data points and LOWESS fit
• SC-06: Title Format (2/2) - Uses {spec-id} · {library} · pyplots.ai

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Demonstrates LOWESS capability with complex non-linear pattern (flat + accelerating regions)
• DQ-02: Realistic Context (5/7) - Generic measurement scenario, plausible but could be more concrete
• DQ-03: Appropriate Scale (5/5) - Sensible values for measurement data

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - No functions/classes, simple flow
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: No Deprecated API (1/1) - Current APIs only
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses axes methods correctly, but no special matplotlib features beyond basic plotting

Strengths

• Excellent visualization of LOWESS smoothing on complex non-linear data (sine + quadratic + noise)
• LOWESS curve is visually distinct with contrasting yellow color and thick line (linewidth=4)
• Appropriate smoothing bandwidth (frac=0.3) as recommended in spec
• Marker transparency (alpha=0.6) shows data density while keeping LOWESS curve visible
• Clean, readable code following KISS principles
• All text elements properly sized for high-resolution output

Weaknesses

• Axis labels lack units (e.g., could be "Measurement Index (samples)" or similar)
• Data context is generic "Measurement Index" / "Response Value" - could be more domain-specific

Verdict: APPROVED