feat(matplotlib): implement histogram-stacked

[github-actions]

Implementation: histogram-stacked - matplotlib

Implements the matplotlib version of histogram-stacked.

File: plots/histogram-stacked/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a stacked histogram showing server response times (in milliseconds) across three geographic regions. The x-axis shows "Response Time (ms)" ranging from approximately 10 to 140ms. The y-axis shows "Number of Requests" ranging from 0 to about 75. Three colored segments are stacked: US-East (blue/#306998), Europe (yellow/#FFD43B), and Asia-Pacific (green/#5BA85B). The US-East distribution peaks around 45ms, Europe around 65ms, and Asia-Pacific extends to higher values around 80-100ms. The title reads "histogram-stacked · matplotlib · pyplots.ai" and a legend is positioned in the upper right corner. White edge lines separate the bars, and a subtle y-axis grid is visible.

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 anywhere
• VQ-03: Element Visibility (8/8) - Histogram bars are well-sized with good alpha (0.9) and white edge lines for distinction
• VQ-04: Color Accessibility (4/5) - Blue, yellow, and green are distinguishable and avoid red-green issues, though yellow-green adjacent could be slightly better
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Response Time (ms)" and "Number of Requests"
• VQ-07: Grid & Legend (0/2) - Grid only on y-axis is good, but legend could use slightly better visual separation

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct stacked histogram
• SC-02: Data Mapping (5/5) - Continuous variable (response time) binned, category (region) for stacking
• SC-03: Required Features (5/5) - Same bin boundaries for all groups, distinct colors, legend with labels, total height shows combined frequency
• SC-04: Data Range (3/3) - All data visible within axes
• SC-05: Legend Accuracy (2/2) - Legend labels correctly identify all three regions
• SC-06: Title Format (2/2) - Correct format: "histogram-stacked · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows overlapping distributions, different group sizes (200, 180, 150), varying means and spreads
• DQ-02: Realistic Context (7/7) - Server response times by region is a realistic, neutral technical scenario
• DQ-03: Appropriate Scale (5/5) - Response times 10-160ms are realistic for web services

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) ensures reproducibility
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot 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 (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Basic ax.hist() with stacked=True is standard usage, no distinctive matplotlib features like custom styling, annotations, or advanced formatting

Strengths

• Excellent realistic data scenario with server response times across geographic regions
• Clean stacking visualization with good color separation using white edge lines
• All three distributions have distinct characteristics (different means, scales, and sample sizes)
• Perfect title format and axis labeling with units
• Good use of alpha for slight transparency while maintaining visibility

Weaknesses

• No distinctive matplotlib features beyond basic histogram; could use annotations, custom spine styling, or statistical overlays
• Legend visual separation could be enhanced with a border or better background

Verdict: APPROVED