feat(bokeh): implement line-styled

[github-actions]

Implementation: line-styled - bokeh

Implements the bokeh version of line-styled.

File: plots/line-styled/implementations/bokeh.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays four line series representing system performance metrics (CPU Usage, Memory Usage, Disk I/O, and Network Traffic) over 12 months (Jan-Dec). The title "line-styled · bokeh · pyplots.ai" is centered at the top. Each series uses a distinct color: blue (solid) for CPU, yellow (dashed) for Memory, green (dotted) for Disk I/O, and orange (dash-dot) for Network Traffic. All lines have circular markers at data points. The legend is positioned in the top-left inside the plot area with a white background. The Y-axis shows "Utilization (%)" ranging from ~60-90, and the X-axis shows month abbreviations. Grid lines are subtle and dashed. The background is a light gray (#fafafa).

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title 48pt, axis labels 36pt, tick labels 28pt - all perfectly readable at 4800×2700
• VQ-02: No Overlap (8/8) - No overlapping text, legend is well-positioned
• VQ-03: Element Visibility (8/8) - Line width 6 and marker size 25 are well-suited for the canvas size
• VQ-04: Color Accessibility (5/5) - Blue, yellow, green, orange palette is colorblind-friendly with good contrast
• VQ-05: Layout Balance (5/5) - Good canvas utilization, balanced margins
• VQ-06: Axis Labels (1/2) - "Utilization (%)" has units but "Month" is generic without clarification of year/period
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha 0.3), but dashed grid can visually compete with dashed line styles

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line plot with multiple series
• SC-02: Data Mapping (5/5) - X is time (months), Y is continuous metric values
• SC-03: Required Features (5/5) - All four standard line styles present (solid, dashed, dotted, dash-dot)
• SC-04: Data Range (3/3) - All data visible within axes
• SC-05: Legend Accuracy (2/2) - Legend correctly maps styles to series names
• SC-06: Title Format (2/2) - Uses correct format "line-styled · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows all four line styles clearly distinguishable, with varying trends
• DQ-02: Realistic Context (5/7) - Server performance metrics is plausible but somewhat generic; could be more specific scenario
• DQ-03: Appropriate Scale (5/5) - Utilization percentages in 60-90% range are realistic for server metrics

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current Bokeh API
• 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) - Uses ColumnDataSource and custom Legend, but doesn't leverage Bokeh's interactive features like HoverTool

Strengths

• Excellent text sizing for the 4800×2700 canvas with 48pt title and 36pt axis labels
• All four line styles (solid, dashed, dotted, dash-dot) are clearly distinguishable as required by spec
• Clean code structure with well-organized ColumnDataSource
• Good use of markers on data points to enhance visibility
• Legend is well-formatted with appropriate sizing and semi-transparent background

Weaknesses

• Grid uses dashed style which can visually compete with the dashed line series - consider solid grid with lower alpha
• Could add HoverTool for interactivity to better leverage Bokeh's strengths
• Data scenario is somewhat generic - "Monthly performance metrics" could be more specific (e.g., specific server type or application)

Verdict: APPROVED