feat(plotnine): implement line-stepwise

[github-actions]

Implementation: line-stepwise - plotnine

Implements the plotnine version of line-stepwise.

File: plots/line-stepwise/implementations/plotnine.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a step line chart showing CPU utilization (%) over a 24-hour period. The line is rendered in a blue color (#306998) with clear horizontal-then-vertical step transitions. The x-axis shows "Hour of Day" ranging from 0 to approximately 23, and the y-axis shows "CPU Utilization (%)" ranging from about 10% to 90%. The title "line-stepwise · plotnine · pyplots.ai" appears at the top. The plot uses a minimal theme with subtle gray grid lines on a white background. The step pattern clearly shows low utilization during night hours (10-15%), a steep morning ramp-up (6-10 hours), sustained high utilization during business hours (75-90%), and gradual wind-down in the evening.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis titles 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) - Line width (size=2) is appropriate, step transitions clearly visible
• VQ-04: Color Accessibility (5/5) - Single blue color, excellent contrast against white background
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "CPU Utilization (%)" and "Hour of Day"
• VQ-07: Grid & Legend (1/2) - Grid is subtle, but element_text used incorrectly for panel_grid (should use element_line) - no visual impact but technically incorrect

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct step line plot using geom_step
• SC-02: Data Mapping (5/5) - X=time (hour), Y=value (CPU %) correctly mapped
• SC-03: Required Features (5/5) - Shows horizontal-then-vertical transitions (direction="hv"), discrete state changes
• SC-04: Data Range (3/3) - Full 24-hour range shown, y-axis accommodates all values
• SC-05: Legend Accuracy (2/2) - No legend needed for single series
• SC-06: Title Format (2/2) - Correct format: "line-stepwise · plotnine · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows value persistence (flat segments), instantaneous changes (vertical jumps), various step heights, both increasing and decreasing transitions
• DQ-02: Realistic Context (7/7) - Server CPU utilization is a perfect real-world example of discrete state monitoring
• DQ-03: Appropriate Scale (5/5) - CPU percentages are realistic (10-90%), hour range is logical (0-23)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) is set (though data is actually deterministic)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current plotnine API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (5/5 pts)

• LF-01: Distinctive Features (5/5) - Excellent use of plotnine's grammar of graphics: ggplot + aes + geom_step with direction parameter, theme_minimal, element_text customization

Strengths

• Excellent use of geom_step with direction="hv" for proper step function visualization
• Realistic and relatable CPU utilization scenario that perfectly demonstrates discrete state changes
• Clean grammar of graphics implementation following plotnine best practices
• Well-balanced layout with appropriate text sizing for 4800x2700 output
• Data shows clear daily pattern with morning ramp-up, business hours peak, and evening wind-down

Weaknesses

• Minor: panel_grid_major and panel_grid_minor use element_text() instead of element_line() - this doesn't cause visible issues but is technically incorrect for grid styling

Verdict: APPROVED