feat(plotnine): implement errorbar-asymmetric

[github-actions]

Implementation: errorbar-asymmetric - plotnine

Implements the plotnine version of errorbar-asymmetric.

File: plots/errorbar-asymmetric/implementations/plotnine.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays 6 quarterly data points (Q1 2024 through Q2 2025) on a clean white background with a subtle gray grid. Each point is marked with a solid blue circle (#306998) with asymmetric error bars extending above and below. The error bars have horizontal caps at both ends. The downside error bars are visibly larger than the upside bars, correctly representing conservative financial projections with greater downside risk. The title "errorbar-asymmetric · plotnine · pyplots.ai" is centered at the top. The y-axis is labeled "Revenue (Million USD)" and the x-axis is labeled "Quarter". A caption at the bottom right explains: "Error bars show 10th-90th percentile forecast range".

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title ~24pt, axis labels ~20pt, tick labels ~16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Points and error bars are well-sized, caps are visible
• VQ-04: Color Accessibility (5/5) - Single blue color, no colorblind issues
• VQ-05: Layout Balance (3/5) - Good proportions but some unused space on right side
• VQ-06: Axis Labels (2/2) - Y-axis includes units "(Million USD)", X-axis is descriptive
• VQ-07: Grid & Legend (0/2) - Grid is subtle (good), but no legend present (caption substitutes but not ideal)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct asymmetric error bar plot
• SC-02: Data Mapping (5/5) - X=quarters, Y=revenue, error bounds correctly computed
• SC-03: Required Features (5/5) - Asymmetric errors, caps visible, annotation explaining bounds
• SC-04: Data Range (3/3) - All data points visible with appropriate range
• SC-05: Legend Accuracy (2/2) - Caption explains error bar meaning as spec recommends
• SC-06: Title Format (2/2) - Correct format: "errorbar-asymmetric · plotnine · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows asymmetric errors clearly, but all bars follow same pattern (downside > upside). More variation would demonstrate the concept better
• DQ-02: Realistic Context (7/7) - Financial forecasting with conservative projections is a real, neutral scenario
• DQ-03: Appropriate Scale (5/5) - Revenue values (105-175M USD) are realistic for quarterly projections

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Using current plotnine API
• CQ-05: Output Correct (1/1) - Saves as "plot.png"

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of ggplot grammar (aes, geom_errorbar, geom_point, theme_minimal, theme customization), but no advanced plotnine-specific features like faceting or scales

Strengths

• Excellent visual clarity with well-sized text and data elements
• Clear demonstration of asymmetric error bars with larger downside risk
• Good use of caption to explain what the error bars represent (as spec recommends)
• Clean, readable code following KISS principles
• Realistic financial forecasting scenario

Weaknesses

• Error bar asymmetry pattern is uniform (all bars have larger downside) - mixing in some with larger upside would better demonstrate the concept
• Grid/legend scoring: caption is good but a proper legend entry could enhance clarity

Verdict: APPROVED