feat(matplotlib): implement network-directed

[github-actions]

Implementation: network-directed - matplotlib

Implements the matplotlib version of network-directed.

File: plots/network-directed/implementations/matplotlib.py

Parent Issue: #2858

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a directed network graph representing software module dependencies. It shows 13 nodes (modules) connected by curved arrows indicating import direction. The nodes are colored by group: Core modules (main, config, database) in Python blue (#306998), Api modules (auth, routes, handlers) in Python yellow (#FFD43B), Utils modules (validators, helpers, logger, cache) in green (#4DAF4A), and Tests modules (test_auth, test_routes, test_db) in purple (#984EA3). The main module sits at the top with arrows flowing downward to its dependencies. Arrows are gray with appropriate arrowheads showing direction clearly. A legend in the upper-left explains the color coding by Module Type. The title "network-directed · matplotlib · pyplots.ai" appears at the top in bold.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 24pt is excellent, node labels at 13pt are readable but could be slightly larger
• VQ-02: No Overlap (8/8) - No overlapping text or elements, nodes well-spaced
• VQ-03: Element Visibility (7/8) - Nodes and arrows clearly visible, arrows well-sized with appropriate alpha
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette (blue, yellow, green, purple)
• VQ-05: Layout Balance (5/5) - Good use of canvas space, nodes distributed across the plot
• VQ-06: Axis Labels (N/A) - Network graphs do not use axis labels (axis off)
• VQ-07: Grid & Legend (2/2) - Legend well-placed upper-left, no grid needed for network graph

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct directed network graph with arrows
• SC-02: Data Mapping (5/5) - Nodes represent modules, edges show import direction
• SC-03: Required Features (5/5) - Arrows clearly visible, curved edges for visual clarity, node groups colored
• SC-04: Data Range (3/3) - All 13 nodes and 22 edges fully visible
• SC-05: Legend Accuracy (2/2) - Legend correctly maps colors to module types
• SC-06: Title Format (2/2) - Correct format: network-directed · matplotlib · pyplots.ai

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows directed dependencies, multiple node groups, varying connectivity levels
• DQ-02: Realistic Context (7/7) - Software package dependencies is an excellent real-world scenario
• DQ-03: Appropriate Scale (5/5) - 13 nodes is ideal for static visualization per spec (10-50 range)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple flat structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set (though not strictly needed here)
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib.pyplot, numpy, patches)
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib APIs
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (4/5 pts)

• LF-01: Uses distinctive features (4/5) - Good use of FancyArrowPatch with connectionstyle for curved arrows, Circle patches for nodes

Strengths

• Excellent directed graph visualization with clear arrow directions showing import dependencies
• Colorblind-safe palette with good distinction between Core (blue), Api (yellow), Utils (green), and Tests (purple) groups
• Well-positioned nodes using manual layout that minimizes edge crossings
• Curved arrows effectively handle overlapping paths and clearly show direction
• Proper title format following spec-id · library · pyplots.ai convention
• Clean KISS code structure with no functions or classes

Weaknesses

• Some node labels are slightly cramped within circles (e.g., validators, test_routes)
• Node radius could be slightly larger to better accommodate longer labels

Verdict: APPROVED