feat(matplotlib): implement box-horizontal

[github-actions]

Implementation: box-horizontal - matplotlib

Implements the matplotlib version of box-horizontal.

File: plots/box-horizontal/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a horizontal box plot showing response times (in milliseconds) for 6 different service types. The boxes are rendered in a muted blue color (#306998) with yellow/gold median lines (#FFD43B). The y-axis shows service categories: Authentication Service, Database Query, File Upload, Payment Processing, Email Notification, and Image Processing. The x-axis shows Response Time (ms) ranging from approximately 0 to 900+ms. Outliers are displayed as yellow circles with blue edges. The plot has a subtle dashed grid on the x-axis. The title "box-horizontal · matplotlib · pyplots.ai" appears at the top. Each service shows different distribution characteristics - Email Notification has a tight distribution around 80ms, while Image Processing has a wide uniform spread from ~300-800ms. Payment Processing shows right-skewed data with multiple outliers extending beyond 800ms.

Quality Score: 94/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, category labels fit well on y-axis
• VQ-03: Element Visibility (8/8) - Box widths appropriate, outliers clearly visible with good marker size
• VQ-04: Color Accessibility (5/5) - Blue/yellow color scheme is colorblind-safe with good contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, good use of space
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with units: "Response Time (ms)", "Service Type"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and appropriate (alpha=0.3), but x-axis only grid could benefit from y-axis grid lines for alignment

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct horizontal box plot with boxes oriented horizontally
• SC-02: Data Mapping (5/5) - Categories on y-axis, numeric values on x-axis as specified
• SC-03: Required Features (5/5) - Shows median line, quartile box, whiskers, and outliers as specified
• SC-04: Data Range (3/3) - All data visible, axes accommodate outliers
• SC-05: Legend Accuracy (2/2) - No legend needed for this single-series box plot
• SC-06: Title Format (2/2) - Correct format: "box-horizontal · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Excellent variety: tight distributions (Email), wide spreads (Image Processing), outliers (Database Query, Payment Processing), skewed data (Payment Processing)
• DQ-02: Realistic Context (7/7) - Service response times is a realistic, neutral tech scenario perfectly suited for comparing distributions
• DQ-03: Appropriate Scale (5/5) - Response times in ms are realistic: 80ms for email, 150ms for auth, 500ms for file upload

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy, both used
• CQ-04: No Deprecated API (1/1) - Uses tick_labels (correct) instead of deprecated labels parameter
• CQ-05: Output Correct (0/1) - Saves as "plot.png" (correct)

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Good use of patch_artist for filled boxes, comprehensive props customization (flierprops, medianprops, whiskerprops, capprops, boxprops)

Strengths

• Excellent data variety showcasing different distribution types (tight, wide, skewed, with outliers)
• Perfect use of horizontal orientation with readable service type labels
• Strong color scheme using Python colors (#306998 blue, #FFD43B yellow) that is colorblind-accessible
• Comprehensive matplotlib boxplot customization demonstrating library capabilities
• Clean, well-structured code following KISS principles

Weaknesses

• Grid only on x-axis; adding subtle y-axis grid lines could help visually align categories across the plot

Verdict: APPROVED