feat(altair): implement violin-box

[github-actions]

Implementation: violin-box - altair

Implements the altair version of violin-box.

File: plots/violin-box/implementations/altair.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays four violin plots with embedded box plots showing server response times across four tiers (Basic, Standard, Premium, Enterprise). Each violin is distinctly colored: Basic in blue, Standard in yellow, Premium in light blue, and Enterprise in green. The violins show smooth KDE distributions with dark gray box plots centered inside, displaying median (white horizontal line), quartiles (gray box), and whiskers extending to min/max values. The title "violin-box · altair · pyplots.ai" appears centered at the top in large font. The Y-axis is labeled "Response Time (ms)" ranging from 0-650ms. Category labels appear below each violin. A color legend for "Server Tier" is positioned in the top right corner.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are clearly readable. Font sizes are appropriate for the canvas size.
• VQ-02: No Overlap (8/8) - No overlapping text elements; all labels are clearly separated.
• VQ-03: Element Visibility (7/8) - Violins and box plots are clearly visible. Box plots could be slightly more prominent but are adequately visible.
• VQ-04: Color Accessibility (5/5) - Colors are colorblind-safe with good contrast. Blue, yellow, light blue, and green are distinguishable.
• VQ-05: Layout Balance (5/5) - Good use of canvas space with faceted layout. Plot elements are well-proportioned.
• VQ-06: Axis Labels (1/2) - Y-axis has descriptive label with units "Response Time (ms)". X-axis uses category names (no explicit axis label needed for faceted categorical).
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha 0.3). Legend is placed well but could be closer to the data.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct: violin plot with embedded box plot.
• SC-02: Data Mapping (5/5) - Numeric variable (Response Time) on Y-axis, categorical (Server Tier) creates facets.
• SC-03: Required Features (5/5) - Shows KDE distribution (violin), median, quartiles (box), and whiskers as specified.
• SC-04: Data Range (3/3) - All data is visible; axes show full range.
• SC-05: Legend Accuracy (2/2) - Legend correctly labels all four server tiers.
• SC-06: Title Format (2/2) - Correct format: "violin-box · altair · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Excellent variety: Basic shows right-skewed exponential distribution, Standard shows normal distribution, Premium shows tight normal, Enterprise shows bimodal distribution. Shows different distribution shapes well.
• DQ-02: Realistic Context (7/7) - Server response times is a real, neutral business/technology scenario. The progression from Basic (high latency) to Enterprise (low latency with caching) is realistic.
• DQ-03: Appropriate Scale (4/5) - Response times in ms are realistic (25-600ms range). Values are plausible for server tiers.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → violin layer → box layer → combined chart → save.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42).
• CQ-03: Clean Imports (2/2) - Only necessary imports (altair, numpy, pandas).
• CQ-04: No Deprecated API (1/1) - Uses current Altair API.
• CQ-05: Output Correct (0/1) - Saves as plot.png and plot.html (correct for Altair).

Library Features (4/5 pts)

• LF-01: Distinctive Features (4/5) - Uses Altair's declarative grammar well: transform_density for KDE, mark_area with orient="horizontal" and stack="center" for violin shape, mark_boxplot with customization, layering with alt.layer, and faceting with alt.Column. Good use of Altair's compositional approach.

Strengths

• Excellent use of Altair's declarative layering to combine violin (mark_area with transform_density) and box plot layers
• Realistic and varied data showing different distribution shapes (exponential, normal, tight normal, bimodal)
• Clean faceted layout with independent x-scales for proper violin centering
• Good color palette that is colorblind-accessible
• Proper title format and clear axis labeling with units

Weaknesses

• Box plot median line could have higher contrast against the gray box for better visibility
• Legend placement in top-right leaves some empty space; could be integrated more tightly

Verdict: APPROVED