feat(letsplot): implement timeseries-decomposition

[github-actions]

Implementation: timeseries-decomposition - letsplot

Implements the letsplot version of timeseries-decomposition.

File: plots/timeseries-decomposition/implementations/letsplot.py

Parent Issue: #2992

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a time series decomposition with four vertically stacked panels showing monthly temperature data over 5 years (2019-2024). The Original Series (blue line) shows temperature fluctuations ranging from ~7°C to ~30°C with clear seasonal patterns. The Trend component (red line) shows a gradual warming from ~15°C to ~18°C. The Seasonal component (green line) displays a repeating annual cycle oscillating between approximately -11°C and +11°C. The Residual (purple line) shows random noise centered around 0, ranging from about -2°C to +3°C. All panels share a common x-axis showing dates (Jul, Oct, Jan, Apr pattern), with the main title "timeseries-decomposition · letsplot · pyplots.ai" at the top. Each panel has "Temperature (°C)" on the y-axis and clear component labels. Light grid lines aid readability.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text is readable; title ~24pt, subplot titles ~20pt, axis labels and ticks clearly visible, though axis tick text could be slightly larger
• VQ-02: No Overlap (8/8) - No overlapping text elements; x-axis labels are well-spaced
• VQ-03: Element Visibility (7/8) - Lines are clearly visible with good thickness (size=1.2), though could be slightly thicker for the scaled output
• VQ-04: Color Accessibility (5/5) - Excellent colorblind-safe palette: blue (#306998), red (#DC2626), green (#059669), purple (#7C3AED) - all distinguishable
• VQ-05: Layout Balance (5/5) - Good use of canvas; four panels fill the space well with balanced margins
• VQ-06: Axis Labels (2/2) - All y-axes labeled "Temperature (°C)" with units; x-axis labeled "Date"
• VQ-07: Grid & Legend (0/2) - Grid is present but very subtle (almost invisible); no legend needed for this plot type

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct time series decomposition with all four components
• SC-02: Data Mapping (5/5) - Time on x-axis, values on y-axis for all panels
• SC-03: Required Features (5/5) - All four components present: Original, Trend, Seasonal, Residual
• SC-04: Data Range (3/3) - All data visible within axes ranges
• SC-05: Legend Accuracy (2/2) - N/A for this plot type (components labeled via subplot titles)
• SC-06: Title Format (2/2) - Correct format: "timeseries-decomposition · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows clear trend, seasonal pattern, and residual noise; seasonal cycle is well-defined; could show more residual variation
• DQ-02: Realistic Context (7/7) - Temperature data over 5 years is a classic, neutral, and comprehensible scenario for time series decomposition
• DQ-03: Appropriate Scale (4/5) - Temperature values are realistic (7-30°C annual range), though the 3°C warming trend over 5 years is slightly aggressive

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Follows imports → data → plot → save pattern; uses gggrid for composition
• 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 lets-plot API
• CQ-05: Output Correct (0/1) - Saves to plot.png but requires file moving from lets-plot-images subdirectory

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses gggrid for multi-panel layout and theme_minimal(); could leverage more lets-plot specific features like tooltips or interactive elements

Strengths

• Excellent use of gggrid() to create vertically stacked decomposition panels
• Clean, colorblind-safe color palette with distinct colors for each component
• Proper use of statsmodels seasonal_decompose for actual decomposition
• Good realistic temperature scenario with clear seasonal patterns
• Correct title format following pyplots.ai conventions
• Properly handles NaN values from decomposition by using dropna()

Weaknesses

• Grid lines are too subtle and barely visible - could increase alpha for better readability
• File handling requires moving files from lets-plot-images subdirectory (workaround needed)

Verdict: APPROVED