feat(letsplot): implement line-timeseries-rolling

[github-actions]

Implementation: line-timeseries-rolling - letsplot

Implements the letsplot version of line-timeseries-rolling.

File: plots/line-timeseries-rolling/implementations/letsplot.py

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a time series of simulated daily sensor temperature readings spanning January to July 2024. The raw data is shown as a semi-transparent blue line with noticeable day-to-day volatility ranging from approximately 12°C to 33°C. Overlaid is a thick yellow/gold line representing the 14-Day Rolling Average, which smoothly reveals the underlying seasonal warming trend (rising from ~16°C in January to ~26°C in May, then declining). The title "line-timeseries-rolling · letsplot · pyplots.ai" appears at the top. The x-axis shows "Date" with monthly labels (Jan-Jul), and the y-axis shows "Temperature (°C)". A legend at the top clearly distinguishes "Raw Data" from "14-Day Rolling Avg". Grid lines are subtle gray. The rolling average line is shorter than the raw data (starts mid-January) as expected due to the window requirement.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, tick marks, and legend all clearly readable at full size
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Raw data line is appropriately thin and semi-transparent, rolling average is prominent; minor deduction as raw data could be slightly more visible
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide excellent contrast and are colorblind-safe
• VQ-05: Layout Balance (5/5) - Good use of canvas space, balanced margins
• VQ-06: Axis Labels (2/2) - "Temperature (°C)" includes units, "Date" is descriptive
• VQ-07: Grid & Legend (0/2) - Grid is subtle, but legend shows 3 separate rows instead of merged legend entries

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct time series line chart with rolling average overlay
• SC-02: Data Mapping (5/5) - Date on X, temperature values on Y correctly assigned
• SC-03: Required Features (5/5) - Raw data with lighter style, prominent rolling average, legend with window size, grid lines present
• SC-04: Data Range (3/3) - Full data range visible on both axes
• SC-05: Legend Accuracy (2/2) - Correctly labels "Raw Data" and "14-Day Rolling Avg"
• SC-06: Title Format (2/2) - Uses exact format: line-timeseries-rolling · letsplot · pyplots.ai

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows seasonal variation, noise, and underlying trend; could show more extreme outliers to demonstrate smoothing effect better
• DQ-02: Realistic Context (7/7) - Temperature sensor readings is a perfect, neutral real-world scenario
• DQ-03: Appropriate Scale (4/5) - Values are realistic for temperature (12-33°C), though 180 days from Jan 1st would end in late June, not July

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean 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 numpy, pandas, lets_plot used and all necessary
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot API
• CQ-05: Output Correct (0/1) - Saves as plot.png but uses path="." which works, though unconventional

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot2-style grammar with scale_manual functions, theme customization, and proper ggsave with scale parameter; could leverage more interactive features

Strengths

• Excellent dual-layer visualization clearly distinguishing raw data from smoothed trend
• Appropriate use of alpha/size differentiation to show raw data as secondary and rolling average as primary
• Realistic temperature sensor scenario with seasonal variation matches specification perfectly
• Clean ggplot2-style grammar of graphics implementation
• Proper legend placement at top with clear labeling including window size

Weaknesses

• Legend appears as three separate legend groups (color, alpha, size) instead of a unified legend - consider using guides() to merge them
• Raw data line could be slightly thicker for better visibility while maintaining transparency

Verdict: APPROVED