feat(plotnine): implement line-timeseries-rolling (#2840)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20603334572)*

---------

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

Author: github-actions[bot]

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

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+""" pyplots.ai
+line-timeseries-rolling: Time Series with Rolling Average Overlay
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from mizani.breaks import breaks_date
+from mizani.labels import label_date
+from plotnine import (
+    aes,
+    element_line,
+    element_text,
+    geom_line,
+    ggplot,
+    guides,
+    labs,
+    scale_alpha_manual,
+    scale_color_manual,
+    scale_size_manual,
+    scale_x_datetime,
+    theme,
+    theme_minimal,
+)
+
+
+# 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)
+noise = np.random.normal(0, 3, 180)
+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()
+
+# Reshape data for plotnine - need long format for multiple series
+df_raw = df[["date", "temperature"]].copy()
+df_raw["series"] = "Daily Temperature"
+df_raw = df_raw.rename(columns={"temperature": "value"})
+
+df_roll = df[["date", "rolling_avg"]].dropna().copy()
+df_roll["series"] = "7-Day Rolling Average"
+df_roll = df_roll.rename(columns={"rolling_avg": "value"})
+
+df_long = pd.concat([df_raw, df_roll], ignore_index=True)
+
+# Make series categorical for consistent ordering
+df_long["series"] = pd.Categorical(
+    df_long["series"], categories=["Daily Temperature", "7-Day Rolling Average"], ordered=True
+)
+
+# Plot
+plot = (
+    ggplot(df_long, aes(x="date", y="value", color="series", alpha="series", size="series"))
+    + geom_line()
+    + scale_color_manual(values={"Daily Temperature": "#306998", "7-Day Rolling Average": "#FFD43B"})
+    + scale_alpha_manual(values={"Daily Temperature": 0.5, "7-Day Rolling Average": 1.0})
+    + scale_size_manual(values={"Daily Temperature": 0.8, "7-Day Rolling Average": 2.0})
+    + guides(alpha="none", size="none")
+    + scale_x_datetime(breaks=breaks_date(7), labels=label_date("%b %Y"))
+    + labs(x="Date", y="Temperature (°C)", title="line-timeseries-rolling · plotnine · pyplots.ai", color="")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        axis_text_x=element_text(angle=30, hjust=1),
+        legend_text=element_text(size=16),
+        legend_title=element_text(size=0),
+        legend_position="right",
+        panel_grid_major=element_line(color="#cccccc", size=0.5, alpha=0.3),
+        panel_grid_minor=element_line(color="#dddddd", size=0.3, alpha=0.2),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

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

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+library: plotnine
+specification_id: line-timeseries-rolling
+created: '2025-12-30T18:40:03Z'
+updated: '2025-12-30T18:42:10Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20603334572
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of plotnine grammar of graphics with proper aesthetics mapping for
+    color, alpha, and size
+  - Clear visual distinction between raw data (lighter blue, thin line) and rolling
+    average (prominent yellow, thicker line)
+  - Proper title format following pyplots.ai conventions
+  - Good use of scale_alpha_manual and scale_size_manual for visual differentiation
+  - Well-structured data transformation to long format for ggplot-style plotting
+  weaknesses:
+  - X-axis date labels are slightly crowded with 7-day breaks over 6 months - could
+    use monthly breaks for cleaner appearance