feat(matplotlib): implement line-timeseries-rolling

[github-actions]

Implementation: line-timeseries-rolling - matplotlib

Implements the matplotlib version of line-timeseries-rolling.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a time series of daily temperature data over 6 months (January 2024 to July 2024). The raw data is shown as a thin, semi-transparent blue line (#306998) with high-frequency fluctuations representing day-to-day temperature variations. Overlaid on top is a thick yellow/gold line (#FFD43B) representing the 7-day rolling average, which smoothly traces the underlying seasonal trend. The y-axis shows "Temperature (°C)" ranging from about -5°C to 25°C, and the x-axis shows dates with monthly labels rotated at 30 degrees. A legend in the upper left clearly identifies "Daily Temperature" and "7-Day Rolling Average". The title follows the required format "line-timeseries-rolling · matplotlib · pyplots.ai". A subtle dashed grid (alpha=0.3) aids in reading values.

Quality Score: 92/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, labels at 20pt, ticks at 16pt, legend at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, date labels properly rotated
• VQ-03: Element Visibility (7/8) - Lines well-sized, though raw data could be slightly thinner for more contrast
• VQ-04: Color Accessibility (5/5) - Blue and yellow provide excellent contrast, colorblind-safe
• VQ-05: Layout Balance (5/5) - Good use of canvas space, balanced margins with tight_layout
• VQ-06: Axis Labels (2/2) - "Temperature (°C)" with units, "Date" is descriptive
• VQ-07: Grid & Legend (1/2) - Grid subtle at alpha=0.3, legend well-placed but could be positioned better to not overlap potential data

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct time series with rolling average overlay
• SC-02: Data Mapping (5/5) - Date on x-axis, temperature values on y-axis, rolling average computed correctly
• SC-03: Required Features (5/5) - Raw data line (semi-transparent), rolling average (prominent), legend with window size, grid on both axes
• SC-04: Data Range (3/3) - All data visible, axes show full range
• SC-05: Legend Accuracy (2/2) - Correctly identifies "Daily Temperature" and "7-Day Rolling Average"
• SC-06: Title Format (2/2) - Uses exact format: "line-timeseries-rolling · matplotlib · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows seasonal trend, daily noise, and smoothing effect well; could show more dramatic trend reversal
• DQ-02: Realistic Context (7/7) - Daily temperature readings with seasonal pattern is a perfect, neutral real-world scenario
• DQ-03: Appropriate Scale (5/5) - Temperature values (-5°C to 25°C) realistic for temperate climate winter-to-summer transition

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) set
• CQ-03: Clean Imports (2/2) - Only matplotlib, numpy, pandas used and all needed
• CQ-04: No Deprecated API (1/1) - All APIs current
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Standard matplotlib usage, no distinctive features like date formatters, custom styles, or advanced annotations

Strengths

• Excellent dual-layer visualization with clear contrast between raw data and rolling average
• Realistic seasonal temperature data that effectively demonstrates the rolling average smoothing effect
• Proper use of alpha transparency for raw data line making the rolling average prominent
• Clean KISS code structure following matplotlib best practices
• Well-formatted date axis with appropriate rotation for readability

Weaknesses

• Raw data line could be slightly thinner (linewidth=1 instead of 1.5) for even better contrast with rolling average
• Consider adding a subtle shaded region between raw and rolling average to emphasize the smoothing effect

Verdict: APPROVED