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feat(highcharts): implement scatter-regression-lowess#2886

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github-actions[bot] merged 4 commits intomainfrom
implementation/scatter-regression-lowess/highcharts
Dec 31, 2025
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feat(highcharts): implement scatter-regression-lowess#2886
github-actions[bot] merged 4 commits intomainfrom
implementation/scatter-regression-lowess/highcharts

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Implementation: scatter-regression-lowess - highcharts

Implements the highcharts version of scatter-regression-lowess.

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

Parent Issue: #2855


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claude Bot commented Dec 31, 2025

AI Review - Attempt 1/3

Image Description

The plot displays a scatter plot with LOWESS regression on a white background. Blue circular markers (with transparency and blue outline) represent 200 data points spread across X values 0-10 and Y values approximately -6 to 25. A smooth yellow/gold LOWESS curve runs through the data, showing a non-linear pattern: starting around y=1, dipping slightly around x=3.5 (to ~-0.5), then rising continuously to about y=21 at x=10. The title "scatter-regression-lowess · highcharts · pyplots.ai" appears at the top in bold, with a subtitle "Non-linear Trend with LOWESS Smoothing (frac=0.3)". Axis labels show "X Value" and "Y Value". A legend in the upper right shows "Data Points" (blue circle) and "LOWESS Curve" (yellow line). Subtle grid lines are visible.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

  • VQ-01: Text Legibility (9/10) - Title, subtitle, axis labels, and tick marks are all clearly readable at full size. Text sizes are appropriately scaled for the large canvas.
  • VQ-02: No Overlap (8/8) - No overlapping text elements. All labels, tick marks, and legend are cleanly separated.
  • VQ-03: Element Visibility (7/8) - Markers are well-sized (radius 10) with good alpha transparency (0.6). The LOWESS curve stands out clearly at lineWidth 6. Minor: Some points in dense areas slightly overlap each other.
  • VQ-04: Color Accessibility (5/5) - Blue (#306998) for points and yellow (#FFD43B) for the curve provide excellent contrast and are colorblind-safe (blue-yellow is the safest combination).
  • VQ-05: Layout Balance (4/5) - Good canvas utilization with proper margins. Plot area is well-proportioned. Minor: Legend could be positioned slightly closer to the plot area.
  • VQ-06: Axis Labels (1/2) - "X Value" and "Y Value" are descriptive but lack units or context (e.g., could be more meaningful like "Measurement Index" or include units).
  • VQ-07: Grid & Legend (2/2) - Grid is subtle with alpha 0.1. Legend is well-placed in upper right with clear labels.

Spec Compliance (25/25 pts)

  • SC-01: Plot Type (8/8) - Correct scatter plot with LOWESS regression overlay.
  • SC-02: Data Mapping (5/5) - X/Y correctly assigned, LOWESS curve properly overlaid.
  • SC-03: Required Features (5/5) - All spec features present: scatter points with transparency, distinct LOWESS curve (solid yellow line), moderate smoothing bandwidth (frac=0.3), descriptive title.
  • SC-04: Data Range (3/3) - All data points visible, axes appropriately scaled.
  • SC-05: Legend Accuracy (2/2) - Legend correctly labels "Data Points" and "LOWESS Curve".
  • SC-06: Title Format (2/2) - Title correctly uses format "scatter-regression-lowess · highcharts · pyplots.ai".

Data Quality (17/20 pts)

  • DQ-01: Feature Coverage (7/8) - Data shows complex non-linear relationship (sine + quadratic) that varies across x-axis range. LOWESS effectively captures local trends. Minor: Could show more dramatic non-linearity.
  • DQ-02: Realistic Context (5/7) - Data is mathematically generated showing a plausible complex pattern. Generic "X Value"/"Y Value" labels reduce context. Could be more anchored to a real-world scenario.
  • DQ-03: Appropriate Scale (5/5) - Values are sensible and demonstrate the intended pattern well.

Code Quality (8/10 pts)

  • CQ-01: KISS Structure (1/3) - Code includes a custom lowess() function definition. While necessary (Highcharts lacks built-in LOWESS), this deviates from the KISS principle of no functions/classes.
  • CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for reproducibility.
  • CQ-03: Clean Imports (2/2) - All imports are used.
  • CQ-04: No Deprecated API (1/1) - Uses current highcharts-core API.
  • CQ-05: Output Correct (1/1) - Saves as plot.png (and plot.html for interactive version).

Library Features (5/5 pts)

  • LF-01: Uses distinctive library features (5/5) - Good use of Highcharts features: SplineSeries for smooth curve rendering, interactive legend, Chart/HighchartsOptions pattern, Selenium export pattern as documented.

Strengths

  • Excellent color contrast with colorblind-safe blue/yellow combination
  • LOWESS curve is smooth and clearly distinguishable from scatter points
  • Proper title format and informative subtitle mentioning the smoothing parameter
  • Good marker sizing and transparency for data density
  • Generates both PNG and HTML outputs for static and interactive viewing
  • Custom LOWESS implementation works correctly with tricube weighting

Weaknesses

  • Axis labels are generic ("X Value", "Y Value") rather than context-specific
  • Custom function definition in code deviates from KISS principle (though necessary)
  • Data could be tied to a more realistic scenario with meaningful labels

Verdict: APPROVED

@github-actions github-actions Bot added the quality:91 Quality score 91/100 label Dec 31, 2025
@github-actions github-actions Bot added the ai-approved Quality OK, ready for merge label Dec 31, 2025
@github-actions github-actions Bot merged commit 59b274e into main Dec 31, 2025
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@github-actions github-actions Bot deleted the implementation/scatter-regression-lowess/highcharts branch December 31, 2025 00:06
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