feat(bokeh): implement slider-control-basic

[github-actions]

Implementation: slider-control-basic - bokeh

Implements the bokeh version of slider-control-basic.

File: plots/slider-control-basic/implementations/bokeh.py

Parent Issue: #3071

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a bar chart showing monthly sales data for 2024. Blue vertical bars (#306998) represent sales values for each of the 12 months (January through December). A yellow/gold connecting line (#FFD43B) runs across the top of the bars with circular markers at each data point. The y-axis shows "Sales (thousands USD)" ranging from 0 to ~150, and the x-axis shows "Month" with abbreviated month names. Sales show seasonal variation: lower in winter months (Jan-Mar ~75-85), rising through spring/summer (Apr-Jul ~100-120), a slight dip in Aug-Sep (~100-110), then climbing to peak in Nov-Dec (~130-140). The background is light gray (#fafafa) with subtle dashed horizontal grid lines. A small toolbar appears in the top-right corner. Note: The slider widget is not visible in the static PNG export - it only appears in the HTML version.

Quality Score: 88/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - Title and axis labels are readable but could be slightly larger for optimal viewing at full resolution
• VQ-02: No Overlap (8/8) - No overlapping text, all month labels clearly visible
• VQ-03: Element Visibility (7/8) - Bars and line are visible, markers slightly small at full resolution
• VQ-04: Color Accessibility (5/5) - Blue/yellow contrast is colorblind-safe
• VQ-05: Layout Balance (4/5) - Good canvas utilization, slight excess whitespace at top
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Sales (thousands USD)"
• VQ-07: Grid & Legend (0/2) - No legend present (not strictly needed but would help explain the line)

Spec Compliance (21/25 pts)

• SC-01: Plot Type (8/8) - Correct interactive chart with slider control
• SC-02: Data Mapping (5/5) - X/Y correctly assigned (months vs sales)
• SC-03: Required Features (3/5) - Slider implemented via CustomJS callback, but slider not visible in PNG; current value shown in title is good
• SC-04: Data Range (3/3) - Y-axis range (0-180) properly shows all data
• SC-05: Legend Accuracy (0/2) - No legend explaining bar vs line
• SC-06: Title Format (2/2) - Correct format: "Monthly Sales (2024) · slider-control-basic · bokeh · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows seasonal patterns and year-over-year growth trend across 7 years of data
• DQ-02: Realistic Context (7/7) - Sales data is a realistic business scenario, neutral topic
• DQ-03: Appropriate Scale (5/5) - Values in thousands USD are plausible for monthly sales

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) is set
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current Bokeh APIs
• CQ-05: Output Correct (1/1) - Saves both plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ColumnDataSource and CustomJS for interactivity, but the slider interaction is only visible in HTML

Strengths

• Excellent use of Bokeh's interactive features with CustomJS callback for real-time slider updates
• Well-structured data generation with realistic seasonal patterns and growth trends across 7 years
• Good color scheme using Python brand colors (blue #306998 and yellow #FFD43B)
• Proper title format that dynamically updates with the selected year
• Clean code structure following KISS principles
• Both HTML (interactive) and PNG (static) outputs generated correctly

Weaknesses

• The static PNG cannot demonstrate the slider functionality - the core feature of this spec is not visible in the review image
• No legend explaining what the yellow line/markers represent vs the blue bars
• Slider width (800px) may appear small relative to the 4800px plot width in HTML view

Verdict: REJECTED

The implementation is technically sound and would score higher if the slider were visible. However, since the core feature (slider control) cannot be evaluated from the static PNG output, and the lack of a legend reduces clarity, the score falls below the 90 threshold. The implementation needs minor adjustments to improve the static representation or documentation of the interactive features.

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a bar chart showing monthly sales data for 2024. Blue vertical bars (#306998) represent sales values for each month (Jan-Dec) on the x-axis, with values ranging from approximately 70 to 140 thousand USD on the y-axis. A yellow/gold trend line (#FFD43B) with circular markers connects the tops of the bars, showing the overall trend. The title reads "Monthly Sales (2024) · slider-control-basic · bokeh · pyplots.ai" at the top left. The y-axis is labeled "Sales (thousands USD)" and the x-axis is labeled "Month". A legend appears on the right side showing "Monthly Sales" and "Trend Line". There's also a subtle note in the upper right area indicating "[Interactive] Use slider to change year (2018-2024)". The background is light gray (#fafafa) with subtle horizontal grid lines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 42pt, axis labels at 32pt, tick labels at 24pt - all clearly readable, slightly smaller title would be more balanced
• VQ-02: No Overlap (8/8) - No overlapping text anywhere
• VQ-03: Element Visibility (8/8) - Bars well-sized at 0.7 width, trend points at size 20, appropriate for 12 data points
• VQ-04: Color Accessibility (5/5) - Blue and yellow color scheme is colorblind-safe with good contrast
• VQ-05: Layout Balance (4/5) - Good canvas utilization, plot fills majority of space, though right margin has some extra space due to legend placement
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Sales (thousands USD)" and "Month"
• VQ-07: Grid & Legend (0/2) - Legend is well-placed but there appears to be rendering artifacts/glitches near the legend area on the right side

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Interactive plot with slider control - correct implementation
• SC-02: Data Mapping (5/5) - X=months (categorical), Y=sales values correctly mapped
• SC-03: Required Features (5/5) - Slider widget with year selection (2018-2024), real-time updates via CustomJS callback, clear min/max labels
• SC-04: Data Range (3/3) - Y-axis range (0-180) accommodates all data properly
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies "Monthly Sales" (bars) and "Trend Line"
• SC-06: Title Format (2/2) - Uses correct format: "Monthly Sales (2024) · slider-control-basic · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows seasonal patterns, growth trend across years, monthly variation - could show more dramatic year-over-year differences
• DQ-02: Realistic Context (7/7) - Sales data is a neutral, realistic business scenario with plausible seasonal patterns (holiday bump in Dec)
• DQ-03: Appropriate Scale (4/5) - Values 50-140k USD for monthly sales are realistic, though the growth trend could be slightly more pronounced

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: 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 3.x API
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html (correct for interactive)

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of ColumnDataSource, CustomJS callbacks, Slider widget, and export_png. Could leverage more Bokeh features like HoverTool for data inspection.

Strengths

• Excellent implementation of interactive slider control with CustomJS callback for client-side updates
• Clean visual design with professional color scheme (Python blue/yellow)
• Proper title format including dynamic year update in callback
• Good use of ColumnDataSource pattern for data management
• Generates both static PNG and interactive HTML outputs appropriately
• Seasonal sales pattern adds realistic data variation

Weaknesses

• Legend area shows some visual artifacts/rendering issues on the right side
• Missing HoverTool which would enhance the interactive experience by showing exact values on hover
• The interactive note annotation could be more visually integrated

Verdict: APPROVED