feat(bokeh): implement forest-basic

[github-actions]

Implementation: forest-basic - bokeh

Implements the bokeh version of forest-basic.

File: plots/forest-basic/implementations/bokeh.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The forest plot displays a meta-analysis of blood pressure reduction trials with 11 individual studies plus a pooled estimate. Each study (Smith et al. 2018 through Thomas et al. 2023) is represented by a blue circle (point estimate) with horizontal whiskers showing 95% confidence intervals and vertical end caps. Marker sizes vary proportionally to study weight. The pooled estimate at the bottom is shown as a distinctive yellow diamond. A vertical dashed gray line marks the null effect at 0. The x-axis shows "Mean Difference in Blood Pressure (mmHg)" ranging from -12 to 4. "← Favors Treatment" and "Favors Control →" labels appear at the bottom. All effect sizes are negative, indicating treatment benefit. The title "forest-basic · bokeh · pyplots.ai" appears at top left.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, study names all clearly readable at appropriate sizes
• VQ-02: No Overlap (8/8) - No overlapping text elements, studies well-spaced vertically
• VQ-03: Element Visibility (8/8) - Markers well-sized proportional to weights, CI lines clearly visible
• VQ-04: Color Accessibility (5/5) - Blue (#306998) for studies, yellow (#FFD43B) for pooled - colorblind-safe
• VQ-05: Layout Balance (3/5) - Good use of canvas but significant whitespace on right side
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Mean Difference in Blood Pressure (mmHg)"
• VQ-07: Grid & Legend (0/2) - No legend needed, but grid could be more subtle (alpha 0.3 is acceptable but visible)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct forest plot with point estimates and CIs
• SC-02: Data Mapping (5/5) - Effect sizes on X, studies on Y - correct mapping
• SC-03: Required Features (5/5) - Diamond for pooled, reference line at null, weighted markers, CI whiskers
• SC-04: Data Range (3/3) - All data visible within axis range
• SC-05: Legend Accuracy (2/2) - Study labels accurate and readable
• SC-06: Title Format (2/2) - Correct format: "forest-basic · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variety of effect sizes and CI widths, but all favor treatment (could show one borderline)
• DQ-02: Realistic Context (7/7) - Blood pressure reduction trials - excellent realistic medical meta-analysis scenario
• DQ-03: Appropriate Scale (4/5) - Effect sizes (-2.8 to -6.2 mmHg) realistic for BP trials, though slightly narrow range

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data → figure → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used (numpy, bokeh components)
• CQ-04: No Deprecated API (1/1) - Current Bokeh API used
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of ColumnDataSource, Span, Label, patch for diamond. Could leverage more Bokeh-specific features like HoverTool for interactivity.

Strengths

• Excellent implementation of all forest plot elements (diamond, whiskers, reference line, weighted markers)
• Realistic medical meta-analysis data with appropriate study naming conventions
• Clean KISS code structure with proper Bokeh idioms (ColumnDataSource, Span, Label)
• Good visual hierarchy distinguishing individual studies from pooled estimate
• Proper use of Bokeh patch() for diamond shape

Weaknesses

• Right side of canvas has excessive whitespace (x_range extends to 4 but data only goes to ~-0.2)
• All studies favor treatment - missing a study crossing the null line would better demonstrate forest plot capabilities
• Could add HoverTool for interactivity (a Bokeh strength)

Verdict: APPROVED