feat(pygal): implement scatter-regression-lowess

[github-actions]

Implementation: scatter-regression-lowess - pygal

Implements the pygal version of scatter-regression-lowess.

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

Parent Issue: #2855

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter chart with approximately 150 blue data points on a white background showing a complex non-linear pattern (combining sine wave and quadratic trend). A smooth yellow/gold LOWESS curve overlays the scatter points, clearly following the underlying data trend from low values (around 0) on the left rising to higher values (around 13) on the right with visible oscillation in the middle section. The title "scatter-regression-lowess · pygal · pyplots.ai" appears at the top in dark text. X-axis is labeled "X Value" (range 0-10), Y-axis is labeled "Y Value" (range -2 to 13). A legend at the bottom shows "Data Points" with a blue marker and "LOWESS Curve (frac=0.35)" with a yellow marker. Subtle dotted grid lines are visible.

Quality Score: 88/100

Criteria Checklist

Visual Quality (34/40 pts)

• VQ-01: Text Legibility (8/10) - Title and axis labels are readable but could be slightly larger; tick labels are adequate
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Scatter points and LOWESS curve are clearly visible; markers sized appropriately for 150 points with good opacity
• VQ-04: Color Accessibility (5/5) - Blue and yellow/gold provide excellent contrast and are colorblind-safe
• VQ-05: Layout Balance (4/5) - Good use of canvas space, though slight margin imbalance on left
• VQ-06: Axis Labels (1/2) - Descriptive labels but without units ("X Value", "Y Value")
• VQ-07: Grid & Legend (1/2) - Grid is appropriately subtle; legend at bottom is functional but legend placement could be more integrated

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correct: scatter plot with LOWESS regression overlay
• SC-02: Data Mapping (5/5) - X/Y correctly assigned to axes
• SC-03: Required Features (5/5) - LOWESS curve present, visually distinct from scatter points (solid yellow line vs blue dots), frac=0.35 within recommended range
• SC-04: Data Range (3/3) - Axes show complete data range
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies "Data Points" and "LOWESS Curve (frac=0.35)"
• SC-06: Title Format (1/2) - Correct format but uses spaces around dots instead of middle-dots (·)

Data Quality (17/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows complex non-linear relationship with varying pattern across x-axis range; demonstrates LOWESS adapting to local patterns
• DQ-02: Realistic Context (5/7) - Data is synthetic/generic (x, y values) rather than a real-world scenario like dose-response or economic data
• DQ-03: Appropriate Scale (5/5) - Values are sensible for demonstration purposes

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → LOWESS calculation → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used (numpy, pygal, Style, lowess)
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• 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) - Uses pygal XY chart, custom Style, appropriate configuration, but doesn't leverage more advanced pygal features like tooltips configuration or custom formatting

Strengths

• Clean implementation with proper LOWESS curve clearly visible and distinct from scatter points
• Excellent color contrast between blue data points and yellow LOWESS curve (colorblind-safe)
• Correct title format following spec-id · library · pyplots.ai pattern
• Good use of statsmodels LOWESS with appropriate frac=0.35 parameter
• Legend clearly indicates the smoothing parameter value
• Scatter points have appropriate sizing and opacity for 150 data points

Weaknesses

• Axis labels are generic ("X Value", "Y Value") rather than domain-specific with units
• Data scenario is synthetic rather than representing a realistic application (e.g., dose-response, economic trend)
• Font sizes could be slightly increased for better legibility at full resolution

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a scatter plot with blue circular data points and a smooth yellow/gold LOWESS regression curve on a white background. The title reads "scatter-regression-lowess · pygal · pyplots.ai" at the top. The X-axis is labeled "X Value" (ranging from 0 to 10) and the Y-axis is labeled "Y Value" (ranging from approximately -2 to 13). The legend at the bottom shows two entries: "Data Points" (blue) and "LOWESS Curve (frac=0.35)" (yellow). The LOWESS curve smoothly captures the non-linear trend in the data, starting low, rising gradually in the middle, dipping slightly around x=5, then rising steeply toward x=10. Grid lines are subtle and dotted.

Quality Score: 90/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - Title and axis labels are readable, tick labels are clear. Slightly smaller than optimal for the canvas size.
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Scatter points and LOWESS curve are clearly visible and well-sized for the data density. Points could be slightly larger.
• VQ-04: Color Accessibility (5/5) - Blue points and yellow curve provide excellent contrast and are colorblind-safe
• VQ-05: Layout Balance (4/5) - Good use of canvas space, minor whitespace imbalance
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("X Value", "Y Value") but lack units. The code specifies domain-specific labels which don't appear in the rendered output.
• VQ-07: Grid & Legend (1/2) - Grid is subtle and appropriate. Legend placement at bottom is functional but legend text could be clearer.

Spec Compliance (23/25 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 requirements met: scatter points, LOWESS curve with distinct color, appropriate smoothing (frac=0.35)
• SC-04: Data Range (3/3) - All data points visible within axes range
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies data points and LOWESS curve with parameters
• SC-06: Title Format (2/2) - Correct format: "scatter-regression-lowess · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows non-linear relationship well with the curve capturing local trends. Good demonstration of LOWESS smoothing behavior.
• DQ-02: Realistic Context (6/7) - The code describes a drug dose-response scenario which is scientifically plausible, though the rendered axis labels are generic.
• DQ-03: Appropriate Scale (5/5) - Data values are realistic and sensible for the demonstrated relationship

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used (numpy, pygal, Style, statsmodels lowess)
• CQ-04: No Deprecated API (1/1) - Modern pygal API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Uses pygal's XY chart, custom Style for fonts/colors, legend_at_bottom, stroke_style. Good use of pygal capabilities but could leverage more SVG interactivity features.

Strengths

• LOWESS curve is visually distinct from scatter points with good color contrast (blue vs yellow)
• Clean code structure following KISS principles with proper use of statsmodels for LOWESS calculation
• Custom pygal Style properly configured for large canvas with appropriate font sizes
• Correct title format following spec-id · library · pyplots.ai convention
• Good use of transparency (opacity=0.6) for scatter points to show density
• Appropriate LOWESS smoothing parameter (frac=0.35) within recommended range

Weaknesses

• Axis labels in rendered output are generic ("X Value", "Y Value") rather than the domain-specific labels defined in the code ("Drug Concentration (mg/L)", "Enzyme Activity (%)")
• Legend entries in output differ from code-defined labels; could be more descriptive

Verdict: APPROVED