feat(pygal): implement parallel-categories-basic

[github-actions]

Implementation: parallel-categories-basic - pygal

Implements the pygal version of parallel-categories-basic.

File: plots/parallel-categories-basic/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a parallel categories diagram showing customer purchase journey flows across four dimensions: Category, Channel, Payment, and Outcome. The leftmost axis shows product categories (Electronics in blue, Clothing in yellow, Home & Garden in teal, Sports in coral) with colored bars proportional to their total counts. Curved ribbons flow from left to right connecting categories across dimensions, with ribbon widths representing observation counts. The middle two axes (Channel and Payment) use gray bars, while the rightmost axis (Outcome) shows Completed, Returned, and Cancelled outcomes. A legend at the bottom identifies the four product categories by color. The title "parallel-categories-basic · pygal · pyplots.ai" appears at the top, and a subtitle "Customer Purchase Journey Flows by Product Category" is at the bottom.

Quality Score: 85/100

Criteria Checklist

Visual Quality (32/40 pts)

• VQ-01: Text Legibility (8/10) - Title and most labels readable, but "Cancelled" label is too small
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Ribbons well-sized but middle dimension labels are missing
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette with good contrast
• VQ-05: Layout Balance (4/5) - Good proportions but some wasted space at margins
• VQ-06: Axis Labels (0/2) - Middle dimensions (Channel, Payment) have no category labels visible
• VQ-07: Grid & Legend (0/2) - Legend present but no grid (N/A for this chart type)

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct parallel categories visualization
• SC-02: Data Mapping (5/5) - Four dimensions correctly mapped with proper flow
• SC-03: Required Features (4/5) - Width-proportional ribbons present, but missing some labels
• SC-04: Data Range (3/3) - All data visible within the chart
• SC-05: Legend Accuracy (2/2) - Legend correctly shows category colors
• SC-06: Title Format (2/2) - Uses correct {spec-id} · {library} · pyplots.ai format

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple paths, different flow proportions, but doesn't emphasize all edge cases
• DQ-02: Realistic Context (7/7) - Excellent e-commerce purchase journey scenario
• DQ-03: Appropriate Scale (5/5) - Realistic order counts for different product categories

Code Quality (7/10 pts)

• CQ-01: KISS Structure (1/3) - Has helper function xml_escape() which violates no-functions rule
• CQ-02: Reproducibility (3/3) - Fixed seed np.random.seed(42) used
• CQ-03: Clean Imports (2/2) - Only necessary imports present
• CQ-04: No Deprecated API (1/1) - No deprecated functions used
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.svg (extra files)

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Creative use of pygal's SVG rendering with custom path elements for parallel categories, demonstrates advanced SVG manipulation

Strengths

• Excellent parallel categories visualization with clear flow ribbons showing customer journey paths across four dimensions
• Colorblind-safe palette with good distinction between product categories (blue, yellow, teal, coral)
• Proper title format following spec-id · library · pyplots.ai convention
• Realistic e-commerce scenario with meaningful data showing different purchase patterns
• Width-proportional ribbons correctly represent observation counts as specified

Weaknesses

• Middle dimension labels (Channel, Payment) are missing - only first and last dimensions show category labels
• The "Cancelled" label on the rightmost axis is very small and barely readable compared to other labels
• Code uses helper function xml_escape() which violates KISS principle (no functions/classes rule)
• Gray bars for middle dimensions reduce visual clarity - consider using color coding throughout

Verdict: REJECTED

Score of 85 is below the 90 threshold for immediate approval. The implementation successfully creates a parallel categories diagram with proper flow visualization, but needs improvements to category labeling on middle axes and code structure to meet quality standards.

[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 parallel categories diagram with four vertical dimensions: Category, Channel, Payment, and Outcome. The Category dimension on the left shows four stacked colored bars (Electronics in blue, Clothing in yellow, Home & Garden in teal, Sports in coral). Curved ribbons flow from each category through the middle dimensions (Channel showing Online/Store/Mobile App; Payment showing Credit Card/Debit Card/Digital Wallet) to the Outcome dimension on the right (Completed/Returned/Cancelled). The ribbons are semi-transparent and colored by their source category, allowing visual tracking of customer purchase journeys. The title "parallel-categories-basic · pygal · pyplots.ai" appears at the top center. A legend at the bottom shows the four category colors, with an italic subtitle describing the visualization.

Quality Score: 90/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - Title is large and clear, dimension headers bold, category labels readable. Minor: "Cancelled" label at bottom right is smaller than other outcome labels
• VQ-02: No Overlap (8/8) - No overlapping text, category labels positioned outside bars, ribbons flow cleanly
• VQ-03: Element Visibility (7/8) - Ribbons clearly visible with good opacity (0.4), bars well-sized. Ribbons appropriately show flow proportions
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette: blue, yellow, teal, coral - all distinguishable
• VQ-05: Layout Balance (4/5) - Good use of canvas, plot fills most of the space. Small margin imbalance at bottom
• VQ-06: Axis Labels (2/2) - N/A for parallel categories but dimension headers are descriptive (Category, Channel, Payment, Outcome)
• VQ-07: Grid & Legend (0/2) - Legend is present and well-placed, no grid needed for this chart type

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct parallel categories visualization with ribbons connecting dimensions
• SC-02: Data Mapping (5/5) - Four categorical dimensions correctly mapped: Category → Channel → Payment → Outcome
• SC-03: Required Features (5/5) - Ribbons proportional to counts, colored by first dimension, multiple dimensions shown
• SC-04: Data Range (3/3) - All categories visible in each dimension
• SC-05: Legend Accuracy (0/2) - Legend shows category colors but middle dimensions use different colors not explained
• SC-06: Title Format (2/2) - Correct format: "parallel-categories-basic · pygal · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows flows across 4 dimensions with varying proportions, includes completed/returned/cancelled outcomes. Could show more variation in return rates
• DQ-02: Realistic Context (7/7) - Real e-commerce customer journey scenario with plausible product categories and channels
• DQ-03: Appropriate Scale (5/5) - Transaction counts are realistic (hundreds per path), proportions make sense

Code Quality (9/10 pts)

• CQ-01: KISS Structure (2/3) - Code builds SVG manually which is complex but necessary for pygal parallel categories. Uses procedural structure
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set at start
• CQ-03: Clean Imports (2/2) - All imports used: cairosvg, numpy, pygal, Style
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (1/1) - Saves as plot.png (and plot.svg, plot.html)

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Creatively uses pygal's XY chart as base and builds custom SVG elements. Shows deep understanding of pygal's SVG rendering but relies heavily on manual SVG construction

Strengths

• Excellent parallel categories visualization with clear flow ribbons connecting four dimensions
• Colorblind-safe palette with distinct colors for each product category
• Well-proportioned ribbons showing customer journey flows with appropriate opacity
• Clean layout with dimension labels at top and category labels positioned outside bars
• Realistic e-commerce scenario with plausible purchase journey data

Weaknesses

• Middle dimension category labels (Channel, Payment) are positioned below bars but could overlap with ribbons in denser visualizations
• Cancelled outcome label at bottom right is quite small and harder to read than other labels

Verdict: APPROVED