feat(letsplot): implement line-stepwise

[github-actions]

Implementation: line-stepwise - letsplot

Implements the letsplot version of line-stepwise.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a step line chart showing server response time monitoring over a 24-hour period. A dark blue step line (#306998) creates clear horizontal-then-vertical transitions characteristic of step functions. Yellow circular points (#FFD43B) with visible outlines mark each data point at every hour. The title "line-stepwise · letsplot · pyplots.ai" appears at the top in bold. The X-axis is labeled "Hour of Day" ranging from 0 to 24 in increments of 3, and the Y-axis is labeled "Response Time (ms)" ranging from approximately 50 to 150. The plot uses a minimal theme with subtle gray grid lines. The response time starts at 50ms and shows discrete jumps at hours 6, 9, 12, 15, 18, and 21, eventually reaching peaks of 150ms before declining.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title is bold and large (~24pt), axis labels are clear (~20pt), tick labels readable (~16pt)
• VQ-02: No Overlap (8/8) - No overlapping text elements, all labels clearly separated
• VQ-03: Element Visibility (7/8) - Step line is thick and visible (size=2), points are appropriately sized (size=5), good contrast
• VQ-04: Color Accessibility (5/5) - Blue/yellow color scheme is colorblind-safe with excellent contrast
• VQ-05: Layout Balance (4/5) - Good use of canvas space, plot fills majority of area, minor whitespace at top
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Response Time (ms)", "Hour of Day"
• VQ-07: Grid & Legend (0/2) - Grid is appropriately subtle, but no legend needed for single series

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correctly uses step line plot with geom_step()
• SC-02: Data Mapping (5/5) - X=hours (time), Y=response time (values that change at specific points)
• SC-03: Required Features (5/5) - Shows horizontal segments (value persistence), vertical segments (instantaneous changes), uses direction="hv" for pre-step alignment
• SC-04: Data Range (3/3) - All data visible, axes show full range
• SC-05: Legend Accuracy (2/2) - N/A (single series, no legend needed)
• SC-06: Title Format (2/2) - Correct format: "line-stepwise · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows discrete state changes at specific times, value persistence between changes, both increases and decreases
• DQ-02: Realistic Context (7/7) - Server response time monitoring is a realistic and neutral scenario, plausible for system performance
• DQ-03: Appropriate Scale (4/5) - Response times 50-150ms are realistic, though the pattern is somewhat regular

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pandas, lets_plot)
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot 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 ggplot grammar with geom_step, theme_minimal, ggsize for sizing, scale_x_continuous for axis breaks, but no advanced lets-plot specific features

Strengths

• Excellent step line visualization with clear horizontal-then-vertical transitions using direction="hv"
• Points overlay on steps helps identify exact data points at each hour
• Good color scheme with Python-inspired blue (#306998) and yellow (#FFD43B) that is colorblind accessible
• Realistic server response time monitoring scenario that demonstrates discrete state changes
• Proper title format following spec-id · library · pyplots.ai convention
• Clean KISS code structure with reproducible random seed
• Both PNG and HTML outputs generated correctly

Weaknesses

• Grid line color (#CCCCCC) could be slightly more subtle (alpha not directly controllable in element_line)
• Points at every hour are slightly redundant - could highlight only the change points for cleaner visualization
• Data pattern is somewhat predictable with changes only at specific scheduled hours (6, 9, 12, 15, 18, 21)

Verdict: APPROVED