feat(letsplot): implement parallel-categories-basic

[github-actions]

Implementation: parallel-categories-basic - letsplot

Implements the letsplot version of parallel-categories-basic.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a parallel categories diagram visualizing customer journey data across four categorical dimensions: Channel (Mobile, Store, Online), Product (Home, Clothing, Electronics), Size (Small, Large), and Outcome (Abandoned, Completed). The ribbons flowing between categories are colored by the acquisition channel using blue (#306998) for Online, green (#27AE60) for Store, and yellow (#FFD43B) for Mobile. Each vertical dimension has dark navy rectangular nodes with category labels positioned on alternating sides showing counts in parentheses. The title "Customer Journey · parallel-categories-basic · letsplot · pyplots.ai" appears at the top. A legend at the bottom shows the "Acquisition Channel" color mapping. The ribbons have smooth curved transitions with 50% opacity and white borders.

Quality Score: 88/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title and dimension headers are clear and bold; category labels are readable but could be slightly larger
• VQ-02: No Overlap (7/8) - Minor label truncation on some categories ("Mobil" appears cut, some count labels partially overlap with nodes)
• VQ-03: Element Visibility (8/8) - Ribbons are well-sized and distinguishable, nodes are appropriately proportioned
• VQ-04: Color Accessibility (4/5) - Blue/green/yellow palette is reasonably colorblind-friendly, though blue-green distinction could be improved
• VQ-05: Layout Balance (5/5) - Good use of canvas, balanced margins, well-spaced dimensions
• VQ-06: Axis Labels (2/2) - N/A for parallel categories (no traditional axes), dimension headers are descriptive
• VQ-07: Grid & Legend (1/2) - Legend well-placed at bottom, but no grid (appropriate for this plot type)

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correct parallel categories visualization
• SC-02: Data Mapping (5/5) - Categories correctly mapped across dimensions
• SC-03: Required Features (4/5) - Width-proportional ribbons present; colors by first dimension; missing interactive highlighting (though HTML export is provided)
• SC-04: Data Range (3/3) - All categories visible with appropriate range
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies channel colors
• SC-06: Title Format (2/2) - Follows "{description} · {spec-id} · {library} · pyplots.ai" format

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows flow patterns across 4 dimensions with varied ribbon widths; good demonstration of cross-category relationships
• DQ-02: Realistic Context (7/7) - Customer journey through e-commerce is realistic and neutral
• DQ-03: Appropriate Scale (4/5) - Count values (2-65) are plausible; total ~762 observations is reasonable

Code Quality (7/10 pts)

• CQ-01: KISS Structure (0/3) - Uses helper functions (calculate_node_positions, add_flows_between_dimensions) instead of flat script structure
• CQ-02: Reproducibility (3/3) - Deterministic data (no random elements)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot 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 ggplot2-style grammar with geom_polygon and geom_rect for custom visualization; however, lets-plot doesn't have a native parallel categories geom, so manual construction was necessary

Strengths

• Excellent custom implementation of parallel categories using geom_polygon for ribbons and geom_rect for nodes
• Smooth curved ribbon transitions using easing function create professional appearance
• Good color scheme with channel-based coloring following spec recommendation
• Proper flow proportionality showing observation counts
• Dimension headers clearly labeled with counts
• Both PNG and HTML outputs provided for static and interactive viewing
• Realistic e-commerce customer journey scenario

Weaknesses

• Code uses helper functions instead of KISS flat structure (though complexity of manual implementation may justify this)
• Some category labels appear slightly truncated or crowded near nodes
• Category label text size (11) could be larger for better readability 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 parallel categories diagram with 4 vertical dimensions (Channel, Product, Size, Outcome) connected by smooth curved ribbons. The ribbons are colored by acquisition channel: blue (#306998) for Online, green (#27AE60) for Store, and yellow (#FFD43B) for Mobile. Each category node is rendered as a dark blue-gray rectangle with labels showing the category name and count in parentheses (e.g., "Online (270)", "Completed (579)"). Dimension headers appear in bold at the top of each vertical axis. The legend at the bottom shows "Acquisition Channel" with the three color mappings. The ribbons use 50% alpha transparency allowing overlapping flows to be distinguished.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title and dimension headers are clearly readable. Category labels are readable but could be slightly larger for optimal viewing at full resolution (size 14 vs recommended 16+)
• VQ-02: No Overlap (8/8) - No text overlaps; labels positioned on alternating sides of nodes to prevent collision
• VQ-03: Element Visibility (8/8) - Ribbons are well-sized with appropriate alpha (0.5), nodes are distinct dark rectangles
• VQ-04: Color Accessibility (5/5) - Blue, green, and yellow are colorblind-safe (not red-green dependent)
• VQ-05: Layout Balance (4/5) - Good use of canvas with plot occupying ~70% of space; bottom area has some unused whitespace
• VQ-06: Axis Labels (0/2) - N/A for this plot type (no traditional axes); dimension headers serve this purpose
• VQ-07: Grid & Legend (2/2) - No distracting grid (appropriate for this plot), legend well-placed at bottom

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct parallel categories with width-proportional ribbons
• SC-02: Data Mapping (5/5) - Four categorical dimensions correctly mapped with flow counts
• SC-03: Required Features (5/5) - Ribbons proportional to count, colored by first dimension (Channel), proper category ordering
• SC-04: Data Range (3/3) - All categories and flows visible
• SC-05: Legend Accuracy (2/2) - Legend correctly shows "Acquisition Channel" with accurate color mapping
• SC-06: Title Format (2/2) - Correct format: "parallel-categories-basic · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple channels, products, sizes, and outcomes with varied flow sizes; could show more extreme contrasts
• DQ-02: Realistic Context (7/7) - E-commerce customer journey is a perfect, neutral, realistic scenario
• DQ-03: Appropriate Scale (4/5) - Values are plausible (45, 32, 18, etc.) though some flows are quite similar in size

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Now uses flat structure without functions/classes (improved from previous attempt)
• CQ-02: Reproducibility (3/3) - Deterministic data (hardcoded list), no random seed needed
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Current lets-plot API
• CQ-05: Output Correct (0/1) - Saves to "plot.png" but path="." may cause issues

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot grammar (geom_polygon, geom_rect, geom_text, theme_minimal) appropriately, but this is a custom implementation rather than a native lets-plot parallel categories geom

Strengths

• Clean KISS flat structure without functions or classes (addressed previous feedback)
• Excellent custom implementation using geom_polygon for smooth curved ribbons with easing
• Professional color scheme that is colorblind-accessible
• Realistic e-commerce customer journey scenario with meaningful category names
• Labels include counts for quantitative context
• Both PNG and HTML outputs for static and interactive viewing
• No text overlap with alternating label positions

Weaknesses

• Category label text could be slightly larger (size 14 → 16) for optimal readability at 4800×2700

Verdict: APPROVED