feat(bokeh): implement histogram-stacked

[github-actions]

Implementation: histogram-stacked - bokeh

Implements the bokeh version of histogram-stacked.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a stacked histogram showing test score distributions for three study groups. The x-axis shows "Test Score (points)" ranging from approximately 40 to 120, and the y-axis shows "Frequency (count)" from 0 to about 83. Three colored segments are stacked: dark blue (Self-study Group) at bottom, yellow (Regular Study Group) in middle, and light blue (Intensive Study Group) on top. The distributions show realistic patterns with Self-study peaking around 65, Regular around 75, and Intensive around 82. A legend is positioned in the top-right corner. White line separators between bar segments provide clear visual distinction. The title "histogram-stacked · bokeh · pyplots.ai" appears at top-left.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 36pt, axis labels at 28pt, tick labels at 22pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Bars well-sized with 0.85 width factor, alpha 0.9 provides good visibility
• VQ-04: Color Accessibility (4/5) - Good contrast between groups, but two blues (dark and light) could be confused by some colorblind users
• VQ-05: Layout Balance (4/5) - Good canvas utilization, though legend is slightly far from data
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Test Score (points)", "Frequency (count)"
• VQ-07: Grid & Legend (0/2) - Legend colors don't match plot colors (Self-study shows as darker blue in legend but appears lighter in some contexts)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct stacked histogram
• SC-02: Data Mapping (5/5) - Continuous values binned correctly, categories stacked
• SC-03: Required Features (5/5) - Same bin boundaries, distinct colors, legend present, total height = combined frequency
• SC-04: Data Range (3/3) - All data visible, y-axis starts at 0
• SC-05: Legend Accuracy (2/2) - Legend labels match group names
• SC-06: Title Format (2/2) - Correct format: histogram-stacked · bokeh · pyplots.ai

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct distributions with different means and spreads, though more variation in spreads would better demonstrate the plot type
• DQ-02: Realistic Context (7/7) - Test scores from different study methods is a plausible, neutral educational scenario
• DQ-03: Appropriate Scale (4/5) - Scores in 40-120 range are reasonable for tests, though some values exceed 100 which is unusual for typical tests

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple script structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: No Deprecated API (1/1) - Uses current Bokeh API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Basic vbar usage, could leverage ColumnDataSource and HoverTool for better Bokeh idioms

Strengths

• Excellent text sizing scaled appropriately for 4800x2700 canvas
• Clean stacking implementation with proper bottom/top calculations
• White line separators between segments improve readability
• Realistic educational scenario with meaningful group differences
• Proper use of consistent bin edges across all groups

Weaknesses

• Two blue colors (dark #306998 and light #4B8BBE) may cause confusion for colorblind users; consider using more distinct hues
• Does not use ColumnDataSource which is idiomatic Bokeh practice
• Some test scores exceed 100 points which is atypical for standard tests

Verdict: APPROVED