feat(matplotlib): implement line-timeseries-rolling (#2805)

## Implementation: `line-timeseries-rolling` - matplotlib

Implements the **matplotlib** version of `line-timeseries-rolling`.

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602448619)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

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

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+""" pyplots.ai
+line-timeseries-rolling: Time Series with Rolling Average Overlay
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data - Daily temperature readings with 7-day rolling average
+np.random.seed(42)
+
+# Generate 180 days of temperature data (6 months)
+dates = pd.date_range("2024-01-01", periods=180, freq="D")
+
+# Create seasonal temperature pattern with noise
+# Base seasonal pattern: winter -> spring -> summer
+day_of_year = np.arange(180)
+seasonal = 5 + 15 * np.sin(2 * np.pi * (day_of_year - 30) / 365)  # Seasonal trend
+noise = np.random.normal(0, 3, 180)  # Daily variation
+temperature = seasonal + noise
+
+# Create DataFrame and calculate rolling average
+df = pd.DataFrame({"date": dates, "temperature": temperature})
+df["rolling_avg"] = df["temperature"].rolling(window=7, center=True).mean()
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Raw data - thin, semi-transparent line
+ax.plot(df["date"], df["temperature"], linewidth=1.5, alpha=0.5, color="#306998", label="Daily Temperature")
+
+# Rolling average - prominent smooth line
+ax.plot(df["date"], df["rolling_avg"], linewidth=3.5, color="#FFD43B", label="7-Day Rolling Average")
+
+# Labels and styling
+ax.set_xlabel("Date", fontsize=20)
+ax.set_ylabel("Temperature (°C)", fontsize=20)
+ax.set_title("line-timeseries-rolling · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=16, loc="upper left")
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Format x-axis dates
+fig.autofmt_xdate(rotation=30)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-timeseries-rolling/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: line-timeseries-rolling
+created: '2025-12-30T17:48:15Z'
+updated: '2025-12-30T17:55:37Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602448619
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  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