feat(plotnine): implement boxen-basic

[github-actions]

Implementation: boxen-basic - plotnine

Implements the plotnine version of boxen-basic.

File: plots/boxen-basic/implementations/plotnine.py

Parent Issue: #3414

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a boxen plot (letter-value plot) showing server response times across four endpoints: API, Database, Cache, and Auth. Each category has nested rectangular boxes representing different quantile levels, colored in a gradient from dark blue (innermost, representing 25%-75%) to light blue (outermost, representing 0.4%-99.6%). The boxes decrease in width for deeper quantiles as expected. A white horizontal line marks the median in each box. Outliers are shown as semi-transparent blue points above the main boxes, particularly visible for the API endpoint (ranging up to ~500ms) and to a lesser extent for Database and Auth. The Cache endpoint shows a tight, low distribution with few outliers. The x-axis is labeled "Endpoint" with category names (API, Database, Cache, Auth), and the y-axis is labeled "Response Time (ms)" ranging from 0 to about 500. A legend on the right explains the quantile levels. The title follows the required format: "boxen-basic · plotnine · pyplots.ai". Grid lines are subtle and the overall layout is well-balanced.

Quality Score: 91/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt bold, axis labels at 20pt, tick labels at 16-18pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, category labels well-spaced
• VQ-03: Element Visibility (7/8) - Nested boxes clearly visible with distinct widths; outlier points appropriately sized with alpha=0.5
• VQ-04: Color Accessibility (5/5) - Blue gradient is colorblind-safe, good contrast between levels
• VQ-05: Layout Balance (5/5) - Plot fills good portion of canvas, balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Response Time (ms)" and "Endpoint"
• VQ-07: Grid & Legend (1/2) - Grid subtle (alpha 0.3), legend placed well but has many entries that could be simplified

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct boxen/letter-value plot with nested boxes showing multiple quantile levels
• SC-02: Data Mapping (5/5) - Categories on X-axis, values on Y-axis correctly assigned
• SC-03: Required Features (4/5) - Shows nested boxes with decreasing widths, outliers displayed; however the color/shading distinction between quantile levels is subtle (same blue gradient)
• SC-04: Data Range (3/3) - All data visible including outliers up to ~500ms
• SC-05: Legend Accuracy (2/2) - Legend correctly explains quantile levels with percentage ranges
• SC-06: Title Format (1/2) - Correct format "boxen-basic · plotnine · pyplots.ai" but uses wrong separator (standard dot · vs middle dot ·)

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (8/8) - Excellent: shows different distribution shapes (API skewed with outliers, Database bimodal-ish spread, Cache tight/low, Auth gamma-shaped with tail)
• DQ-02: Realistic Context (7/7) - Server response times by endpoint is a perfect, realistic scenario with plausible variation
• DQ-03: Appropriate Scale (4/5) - Response times 0-500ms are realistic; Cache at ~15ms is plausible, API outliers reaching 500ms are reasonable

Code Quality (8/10 pts)

• CQ-01: KISS Structure (2/3) - Code follows imports → data → plot → save pattern but has significant complexity with manual letter-value calculation (forgivable since plotnine lacks native geom_boxen)
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set at beginning
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current plotnine API
• CQ-05: Output Correct (0/1) - Saves to "plot.png" correctly

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses ggplot grammar (ggplot + geom_rect + geom_segment + scale_fill_manual + theme_minimal), but since plotnine lacks native geom_boxen, implementation required manual quantile calculation and geom_rect workaround

Strengths

• Excellent implementation of a boxen plot using plotnine's grammar of graphics despite lacking native geom_boxen support
• Data demonstrates all key aspects of boxen plots: different distribution shapes, visible quantile nesting, and outlier detection
• Clean blue gradient color scheme that is colorblind-safe
• Realistic server response time scenario with 2000 points per category
• Proper use of nested boxes with decreasing widths for deeper quantiles
• Clear legend explaining quantile level percentages

Weaknesses

• Color gradient between quantile levels could have more contrast to better distinguish nested boxes
• Manual implementation adds code complexity (though unavoidable for plotnine)

Verdict: APPROVED