feat(matplotlib): implement bar-error

[github-actions]

Implementation: bar-error - matplotlib

Implements the matplotlib version of bar-error.

File: plots/bar-error/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a bar chart with 5 blue vertical bars representing A/B test groups (Control, Variant A, Variant B, Variant C, Variant D). Each bar has dark blue error bars with visible caps extending above and below the bar tops. The bars use a consistent blue color (#306998) with darker edge color (#1e4466). The y-axis shows "Conversion Rate (%)" ranging from 0 to approximately 24, and the x-axis shows "Test Group". The title follows the required format "bar-error · matplotlib · pyplots.ai". A small annotation box in the bottom-right corner states "Error bars: 95% CI". The grid is subtle with horizontal dashed lines at alpha 0.3.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 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 elements
• VQ-03: Element Visibility (8/8) - Bars and error bars are clearly visible with appropriate sizing
• VQ-04: Color Accessibility (5/5) - Single color scheme, no colorblind issues
• VQ-05: Layout Balance (4/5) - Good proportions, plot fills canvas well, minor: could use slightly more vertical space
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Conversion Rate (%)"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and appropriate, but the annotation box placement in bottom-right corner is not optimal (sits in empty space rather than near legend position)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct bar chart with error bars
• SC-02: Data Mapping (5/5) - Categories on X, values on Y correctly assigned
• SC-03: Required Features (5/5) - Error bars with visible caps, annotation explaining error bar meaning
• SC-04: Data Range (3/3) - Y-axis shows all data with appropriate headroom (0 to ~24)
• SC-05: Legend Accuracy (2/2) - Annotation accurately describes "95% CI"
• SC-06: Title Format (2/2) - Correct format: "bar-error · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows varying bar heights and different error magnitudes, but all errors are symmetric (spec mentions asymmetric errors as an option)
• DQ-02: Realistic Context (7/7) - A/B test conversion rates is a realistic, comprehensible scenario
• DQ-03: Appropriate Scale (5/5) - Conversion rates of 11-18% with CI widths of 1-3% are realistic

Code Quality (10/10 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 and numpy, both used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses ax.bar() and ax.errorbar() correctly, but these are basic matplotlib features. Could have used more advanced features like bar_label() for value annotations or customized error bar styling

Strengths

• Excellent text legibility with appropriate font sizes for all elements
• Clear, realistic A/B test scenario with plausible conversion rate data
• Error bar caps are clearly visible with good sizing (capsize=10, capthick=3)
• Clean annotation explaining what error bars represent (95% CI requirement from spec)
• Good color choice with consistent dark blue theme
• KISS code structure following matplotlib best practices

Weaknesses

• Error bars are all symmetric; demonstrating asymmetric errors would better showcase the plot type's capabilities
• Annotation box placement in bottom-right corner floats in empty space rather than being more integrated with the plot
• Basic library usage without leveraging advanced matplotlib features like bar_label() for showing values

Verdict: APPROVED