feat(matplotlib): implement timeseries-decomposition

[github-actions]

Implementation: timeseries-decomposition - matplotlib

Implements the matplotlib version of timeseries-decomposition.

File: plots/timeseries-decomposition/implementations/matplotlib.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 subplots sharing a common time axis (2018-2024). The Original series (blue, #306998) shows monthly retail sales data ranging from ~90 to ~200 with clear seasonal patterns and an upward trend. The Trend component (yellow/gold, #FFD43B) shows a smooth upward trajectory from ~110 to ~165. The Seasonal component (blue, #4B8BBE) displays repeating annual cycles oscillating between approximately -30 and +25, with visible December holiday bumps. The Residual component (yellow/gold, #FFE873) shows random noise fluctuating around a zero reference line (gray), ranging from approximately -15 to +15. All subplots have dashed grid lines (alpha 0.3), clear y-axis labels (Original, Trend, Seasonal, Residual), and the bottom subplot includes a "Date" x-axis label. The title "timeseries-decomposition · matplotlib · pyplots.ai" is clearly displayed at the top.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 24pt, y-labels at 18pt, x-label at 20pt, tick labels at 14pt - all readable but tick labels slightly under 16pt guideline
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (8/8) - Line widths of 2.5 are appropriate for time series, clearly visible
• VQ-04: Color Accessibility (4/5) - Blue/yellow palette is colorblind-friendly; however using two similar yellows for Trend and Residual could be slightly confusing
• VQ-05: Layout Balance (5/5) - Four subplots fill the 16:12 canvas well with good proportions
• VQ-06: Axis Labels (1/2) - Y-axis labels are descriptive component names but lack units (e.g., "Sales (USD)")
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3, dashed), but no legend present - acceptable since each subplot is labeled

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct time series decomposition with four stacked subplots
• SC-02: Data Mapping (5/5) - Time on x-axis, values on y-axis correctly assigned
• SC-03: Required Features (5/5) - All four components present: Original, Trend, Seasonal, Residual
• SC-04: Data Range (3/3) - All data visible, no clipping
• SC-05: Legend Accuracy (2/2) - N/A (no legend needed, subplots labeled via y-axis)
• SC-06: Title Format (2/2) - Correct format: "timeseries-decomposition · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows upward trend, annual seasonality, holiday bumps, and random residuals; could show multiplicative patterns for more variety
• DQ-02: Realistic Context (7/7) - Monthly retail sales over 6 years is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (4/5) - Sales values (100-200) are plausible but generic; specifying units would improve context

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) ensures reproducible results
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib, numpy, pandas, statsmodels)
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API with axes methods
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses basic matplotlib subplots; could leverage GridSpec for custom height ratios, fill_between for confidence intervals, or annotations

Strengths

• Excellent four-panel layout with shared x-axis showing all decomposition components clearly
• Realistic retail sales scenario with trend, annual seasonality, and December holiday bumps
• Clean code structure using statsmodels seasonal_decompose for proper decomposition
• Appropriate line widths and grid styling for readability

Weaknesses

• Y-axis labels lack units (e.g., "Original (Sales USD)" would be more informative)
• Uses two similar yellow shades for Trend and Residual which could be slightly confusing
• Tick labels at 14pt are slightly below the 16pt guideline
• Could leverage more matplotlib features like annotations or fill_between for visual enhancement

Verdict: APPROVED