feat(plotly): implement bar-error

[github-actions]

Implementation: bar-error - plotly

Implements the plotly version of bar-error.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a bar chart with 5 blue bars (#306998) representing treatment groups: Control, Treatment A, Treatment B, Treatment C, and Treatment D. Each bar has dark vertical error bars with horizontal caps at the ends indicating asymmetric standard deviations. The y-axis shows "Response Value (%)" ranging from 0-90, and the x-axis shows "Treatment Group". The title reads "Lab Treatment Results · bar-error · plotly · pyplots.ai" centered at the top. An annotation in the upper right corner states "Error bars: ±1 SD (asymmetric)". The plot uses a clean white background with subtle horizontal grid lines. Treatment C has the highest response (~72%) with the largest error range, while Control has the lowest (~45%).

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 32pt, axis labels at 24pt, tick labels at 20pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text anywhere, category labels well-spaced
• VQ-03: Element Visibility (8/8) - Bars clearly visible, error bars with thick lines (3px) and wide caps (12px)
• VQ-04: Color Accessibility (5/5) - Single blue color scheme, no colorblind issues
• VQ-05: Layout Balance (4/5) - Good proportions, slight excess whitespace at bottom
• VQ-06: Axis Labels (2/2) - Y-axis has units "Response Value (%)", X-axis descriptive "Treatment Group"
• VQ-07: Grid & Legend (0/2) - Grid is subtle (alpha 0.1), but no legend shown (showlegend=False) - annotation explains error bars but a legend entry would be cleaner

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, errors correctly applied
• SC-03: Required Features (5/5) - Error bars have caps, annotation explains error representation, asymmetric errors demonstrated
• SC-04: Data Range (3/3) - Y-axis range [0, 90] shows all data with headroom for error bars
• SC-05: Legend Accuracy (2/2) - Annotation accurately describes "±1 SD (asymmetric)"
• SC-06: Title Format (2/2) - Follows exact format: "Lab Treatment Results · bar-error · plotly · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows asymmetric errors and variation across groups, but could show more dramatic differences in error magnitudes
• DQ-02: Realistic Context (7/7) - Lab treatment experiment is a perfect real-world scenario for error bars
• DQ-03: Appropriate Scale (5/5) - Response values 45-72% with errors 4-8% are realistic for treatment studies

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → figure → layout → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects imported
• CQ-04: No Deprecated API (1/1) - Uses current Plotly API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses go.Bar with error_y correctly, but doesn't leverage Plotly's interactive features (hover templates, animations) or Express API for simpler code

Strengths

• Excellent visual clarity with well-sized text and properly styled error bars with visible caps
• Perfect spec compliance including the required annotation explaining error bar meaning
• Realistic lab experiment context that demonstrates the practical use case
• Clean KISS code structure following all conventions
• Asymmetric error bars correctly implemented showing realistic experimental variation

Weaknesses

• Legend is disabled (showlegend=False) - while the annotation works, a legend entry would be more standard
• Could leverage Plotly's hover templates to show exact values and error ranges on interaction
• Error magnitude variation across groups is relatively uniform - more dramatic differences would better showcase the feature

Verdict: APPROVED