feat(pygal): implement andrews-curves

[github-actions]

Implementation: andrews-curves - pygal

Implements the pygal version of andrews-curves.

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

Parent Issue: #2859

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays Andrews curves for multivariate Iris-like data with three species (Setosa, Versicolor, Virginica). The visualization shows 45 smooth Fourier series curves (15 per species) plotted across t values from -π to π on the x-axis, with f(t) values ranging from approximately -3 to 3.5 on the y-axis. Each species is color-coded: blue curves for Setosa (consistently lower amplitude, staying mostly between -1 and 1), yellow/gold curves for Versicolor (medium amplitude, often extending to -2 to 2), and red curves for Virginica (higher amplitude, reaching up to 3 and down to -2). The curves demonstrate clear cluster separation, with each species producing distinctly different wave patterns. The title "andrews-curves · pygal · pyplots.ai" appears at the top. The legend is positioned at the bottom with the three species names. Grid lines are visible but subtle in both directions. The background is white with the plot filling a reasonable portion of the canvas.

Quality Score: 76/100

Criteria Checklist

Visual Quality (28/40 pts)

• VQ-01: Text Legibility (7/10) - Title and axis labels are readable but relatively small given the 4800x2700 canvas; tick labels are visible but could be larger
• VQ-02: No Overlap (8/8) - No overlapping text elements; legend is well-separated from data
• VQ-03: Element Visibility (6/8) - Curves are visible with good opacity (0.4), but with 45 curves some overlap makes individual tracing difficult
• VQ-04: Color Accessibility (3/5) - Blue/yellow/red palette is distinguishable but yellow on white is less visible than other colors
• VQ-05: Layout Balance (3/5) - Plot area is reasonable but there's excess whitespace; legend labels at bottom are quite small
• VQ-06: Axis Labels (1/2) - "t (radians)" includes units, "f(t)" is descriptive but minimal
• VQ-07: Grid & Legend (0/2) - Grid is good (subtle), but legend labels are very small and difficult to read at full scale

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct Andrews curves implementation using Fourier transformation
• SC-02: Data Mapping (5/5) - Variables correctly mapped to Fourier coefficients, t range from -π to π
• SC-03: Required Features (5/5) - Normalization applied, transparency used, color coding by category present
• SC-04: Data Range (3/3) - All curves and data visible within axes
• SC-05: Legend Accuracy (0/2) - Legend labels (Setosa, Versicolor, Virginica) are present but extremely small/hard to read
• SC-06: Title Format (2/2) - Correctly uses "andrews-curves · pygal · pyplots.ai" format

Data Quality (17/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows clear cluster separation between species; demonstrates how similar observations produce similar curves
• DQ-02: Realistic Context (7/7) - Uses classic Iris dataset scenario with realistic sepal/petal measurements
• DQ-03: Appropriate Scale (3/5) - Values are realistic for Iris data, but normalized z-scores create abstract scale on y-axis

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: No Deprecated API (1/1) - No deprecated functions
• 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's XY chart, custom Style, legend_at_bottom, SVG rendering to PNG, but doesn't leverage more distinctive features like tooltips or interactivity in the HTML output

Strengths

• Correct mathematical implementation of Andrews curves Fourier transformation
• Clear visual separation of the three species clusters
• Good use of transparency (opacity=0.4) to reveal density patterns
• Proper data normalization before transformation
• Clean, well-organized code structure following KISS principles
• Appropriate use of pygal's XY chart and custom styling
• Realistic Iris-like dataset with plausible measurements

Weaknesses

• Legend labels at bottom are too small to read clearly; font sizes need significant increase for 4800x2700 canvas
• Yellow curves on white background lack sufficient contrast compared to blue and red
• The stroke parameter on line 115 may not work as intended (should use stroke style attribute)

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 Andrews curves for multivariate Iris flower data with three species (Setosa, Versicolor, Virginica). Each species is represented by 15 semi-transparent curves in distinct colors: blue (#306998) for Setosa, orange (#E67E22) for Versicolor, and purple (#9B59B6) for Virginica. The x-axis shows "t (radians)" ranging from approximately -2.8 to 2.8 (i.e., -π to π), and the y-axis shows "f(t)" ranging from about -3 to 3. The title "andrews-curves · pygal · pyplots.ai" appears at the top. A legend showing the three species names is displayed at the bottom. The curves show clear separation between species, with Setosa (blue) generally showing different wave patterns from Versicolor (orange) and Virginica (purple). Grid lines are visible for both axes.

Quality Score: 84/100

Criteria Checklist

Visual Quality (31/40 pts)

• VQ-01: Text Legibility (8/10) - Title and axis labels are readable, but appear somewhat small relative to the 4800×2700 canvas. Tick labels are readable but could be larger.
• VQ-02: No Overlap (8/8) - No text overlap issues. Legend is cleanly placed at bottom.
• VQ-03: Element Visibility (6/8) - Curves are visible with appropriate transparency (alpha=0.4), allowing density patterns to emerge. Line width is adequate, though curves could be slightly thicker for better visibility.
• VQ-04: Color Accessibility (5/5) - Excellent colorblind-safe palette: blue, orange, and purple provide strong contrast and are distinguishable by colorblind users.
• VQ-05: Layout Balance (2/5) - The plot area is well-proportioned but the legend labels at the bottom left (•Setosa, •Versicolor, •Virginica) are very small and positioned awkwardly. There's significant unused white space.
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels: "t (radians)" for x-axis and "f(t)" for y-axis.
• VQ-07: Grid & Legend (0/2) - Grid lines are visible but the legend presentation is problematic - the colored dots next to species names are barely visible, and the legend text is too small.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct implementation of Andrews curves using Fourier transformation
• SC-02: Data Mapping (5/5) - Variables correctly mapped as coefficients in Fourier expansion
• SC-03: Required Features (5/5) - All spec features present: multivariate transformation, color-coded categories, transparency for overlapping curves
• SC-04: Data Range (3/3) - t ranges from -π to π as specified, all data visible
• SC-05: Legend Accuracy (2/2) - Legend correctly labels the three species
• SC-06: Title Format (2/2) - Title follows exact format: "andrews-curves · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows clustering patterns and some overlap between Versicolor and Virginica, but could show more distinct outlier curves
• DQ-02: Realistic Context (7/7) - Uses Iris dataset simulation, a classic and appropriate example for Andrews curves
• DQ-03: Appropriate Scale (5/5) - Values are realistic for standardized Iris measurements

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear script structure with no functions or classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for reproducibility
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pygal, Style)
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (1/1) - Outputs both plot.png and plot.html

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - While pygal's XY chart is used correctly, no distinctive pygal features are leveraged (e.g., custom tooltips, interactive hover effects in HTML output, value formatters)

Strengths

• Correct Andrews curve Fourier transformation formula implementation
• Excellent colorblind-safe color palette with high contrast between species
• Proper data normalization (z-score standardization) as specified in notes
• Good use of transparency (opacity=0.4) to reveal density patterns
• Clean, well-structured code following KISS principles

Weaknesses

• Legend text and markers at bottom are too small and poorly positioned, reducing readability
• Font sizes could be increased further for better legibility at full resolution
• No distinctive pygal features utilized (interactive tooltips, custom hover effects)

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays Andrews curves for the Iris dataset with three species (Setosa, Versicolor, Virginica) represented by distinct colors: blue for Setosa, orange for Versicolor, and purple for Virginica. The title correctly shows "andrews-curves · pygal · pyplots.ai" at the top. The x-axis shows "t (radians)" ranging from approximately -3 to 3 (i.e., -π to π), and the y-axis shows "f(t)" ranging from -3 to 3. Each species has 15 smooth Fourier curves with transparency (alpha ~0.4) that creates density overlaps. The curves show clear separation between species - Setosa (blue) curves cluster in a lower amplitude range, while Versicolor (orange) and Virginica (purple) show higher amplitude patterns. The legend at the bottom shows the three species names with their corresponding color boxes, though there are many empty legend entries due to the implementation adding 15 curves per species. The overall layout has a white background with subtle grid guides.

Quality Score: 84/100

Criteria Checklist

Visual Quality (32/40 pts)

• VQ-01: Text Legibility (8/10) - Title, axis labels, and tick marks are readable with large font sizes, but legend text appears smaller relative to canvas
• VQ-02: No Overlap (8/8) - No overlapping text elements; curves overlap intentionally with transparency
• VQ-03: Element Visibility (6/8) - Curves visible with transparency, but the many legend entries create visual clutter
• VQ-04: Color Accessibility (5/5) - Blue, orange, purple palette is colorblind-safe with good contrast
• VQ-05: Layout Balance (3/5) - Plot fills reasonable space but legend takes excessive vertical space at bottom with many empty entries
• VQ-06: Axis Labels (2/2) - "t (radians)" and "f(t)" are descriptive
• VQ-07: Grid & Legend (0/2) - Legend has major issues with 45 entries (only 3 with names, rest empty boxes)

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct Andrews curves using Fourier transformation
• SC-02: Data Mapping (5/5) - Variables correctly mapped as Fourier coefficients
• SC-03: Required Features (4/5) - Has normalization, color by category, and t range -π to π; transparency present but no explicit alpha parameter visible in tooltip
• SC-04: Data Range (3/3) - Full t range shown, y-axis auto-scaled appropriately
• SC-05: Legend Accuracy (1/2) - Species names correct but cluttered with empty legend entries
• SC-06: Title Format (2/2) - Correct format "andrews-curves · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows clear cluster separation between species, some overlap between Versicolor/Virginica (realistic)
• DQ-02: Realistic Context (7/7) - Uses Iris-like botanical data which is a classic multivariate dataset
• DQ-03: Appropriate Scale (4/5) - Realistic sepal/petal measurements, though scaled values range larger than typical

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - Flat script structure with imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy and pygal imports, all used
• CQ-04: No Deprecated API (0/1) - Uses strict=True in zip which is valid but unusual
• CQ-05: Output Correct (0/1) - Outputs both plot.png and plot.html correctly

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal XY chart, custom Style, interactive tooltips, render_to_png/html, but legend handling creates clutter

Strengths

• Correct Andrews curve Fourier transformation formula implementation
• Colorblind-safe palette (blue, orange, purple) with good contrast
• Interactive tooltips showing original data values (sepal/petal measurements)
• Clean separation visible between Iris species clusters
• Proper z-score normalization of variables before transformation
• Appropriate canvas size (4800x2700) with scaled font sizes

Weaknesses

• Legend shows 45 entries (15 per species) instead of 3 clean entries - creates visual clutter and wastes vertical space
• Empty legend boxes displayed for curves added with empty string names

Verdict: APPROVED

This is attempt 3/3 with a score of 84/100 (≥50), which qualifies for merge per quality criteria. The Andrews curves visualization correctly implements the Fourier transformation and shows clear cluster separation between Iris species. The main visual issue is the cluttered legend with many empty entries, but this is a pygal limitation when adding multiple series of the same category.

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays Andrews curves visualization with 45 curves total (15 per species). Three iris species are shown: Setosa (blue), Versicolor (orange), and Virginica (purple). The title "andrews-curves · pygal · pyplots.ai" is centered at the top. The X-axis shows "t (radians)" ranging from approximately -3 to 3, and the Y-axis shows "f(t)" ranging from -3 to 3. The legend appears at the bottom with three colored boxes indicating each species. The curves demonstrate the Fourier transformation of multivariate iris data, with Setosa curves (blue) forming a distinctly flat pattern near y=0, while Versicolor (orange) and Virginica (purple) show more wave-like patterns with greater amplitude. Grid lines are subtle and visible. The colorblind-safe palette (blue/orange/purple) provides good differentiation between groups.

Quality Score: 88/100

Criteria Checklist

Visual Quality (34/40 pts)

• VQ-01: Text Legibility (9/10) - Title and axis labels are readable; tick labels slightly small but acceptable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (6/8) - Curves visible with appropriate transparency (0.4 alpha), some overlapping curves difficult to trace individually
• VQ-04: Color Accessibility (5/5) - Colorblind-safe blue/orange/purple palette
• VQ-05: Layout Balance (4/5) - Good proportions, slight whitespace imbalance
• VQ-06: Axis Labels (2/2) - Descriptive labels with units ("t (radians)", "f(t)")
• VQ-07: Grid & Legend (0/2) - Legend positioned well at bottom but grid lines could be more subtle

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct Andrews curves visualization using Fourier transform
• SC-02: Data Mapping (5/5) - Multivariate data correctly transformed to curves
• SC-03: Required Features (5/5) - All spec features present: normalization, color by category, t ranges from -π to π
• SC-04: Data Range (3/3) - All curves visible within axis range
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies three species
• SC-06: Title Format (0/2) - Title format is correct

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows cluster separation well; Setosa distinctly different from others
• DQ-02: Realistic Context (7/7) - Iris dataset is classic, neutral scientific example
• DQ-03: Appropriate Scale (4/5) - Values match realistic iris measurements

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy, pygal, and Style imported
• CQ-04: No Deprecated API (1/1) - Modern pygal API
• CQ-05: Output Correct (0/1) - Saves to both plot.html and plot.png (correct)

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Uses pygal's XY chart with custom Style, interactive tooltips with metadata, legend_at_bottom configuration, SVG-native output with HTML interactivity

Strengths

• Excellent colorblind-safe color palette (blue/orange/purple)
• Proper data normalization (z-score standardization) as recommended in spec
• Interactive tooltips showing original iris measurements for each curve
• Clean legend with only 3 entries (one per species) rather than per-curve
• Correct Andrews curve formula implementation
• Good use of pygal's custom Style for large canvas sizing

Weaknesses

• Grid lines could be slightly more subtle (alpha/opacity could be reduced)
• Some individual curves are hard to trace due to overlapping in the middle region

Verdict: APPROVED

This is attempt 3/3. The implementation meets quality standards with a score of 88/100, which exceeds the 50-point threshold for final attempt approval. The visualization correctly implements Andrews curves with proper Fourier transformation, good color accessibility, and effective use of pygal's interactive features.

[github-actions]

AI Review - Final Status

Score: 88/100 (Acceptable)

After 3 repair attempts, pygal reached 88/100.

Score ≥ 50 is acceptable for the repository. Merging.

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🤖 impl-review