feat(letsplot): implement line-timeseries-rolling (#2827)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602454238)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

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

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+""" pyplots.ai
+line-timeseries-rolling: Time Series with Rolling Average Overlay
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data: Simulated daily sensor temperature readings
+np.random.seed(42)
+n_days = 180
+dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
+
+# Generate realistic temperature data with seasonal trend and noise
+base_temp = 15  # Base temperature in Celsius
+seasonal = 10 * np.sin(2 * np.pi * np.arange(n_days) / 365)  # Seasonal variation
+noise = np.random.normal(0, 3, n_days)  # Daily fluctuations
+trend = np.linspace(0, 2, n_days)  # Slight warming trend
+values = base_temp + seasonal + noise + trend
+
+# Calculate 14-day rolling average
+rolling_window = 14
+rolling_avg = pd.Series(values).rolling(window=rolling_window, center=False).mean()
+
+# Create DataFrame for plotting
+df = pd.DataFrame({"date": dates, "value": values, "rolling_avg": rolling_avg})
+
+# Reshape data for lets-plot (long format for legend)
+df_raw = df[["date", "value"]].copy()
+df_raw["series"] = "Raw Data"
+df_raw = df_raw.rename(columns={"value": "temp"})
+
+df_rolling = df[["date", "rolling_avg"]].dropna().copy()
+df_rolling["series"] = f"{rolling_window}-Day Rolling Avg"
+df_rolling = df_rolling.rename(columns={"rolling_avg": "temp"})
+
+df_long = pd.concat([df_raw, df_rolling], ignore_index=True)
+
+# Plot
+plot = (
+    ggplot(df_long, aes(x="date", y="temp", color="series"))
+    + geom_line(aes(alpha="series", size="series"))
+    + labs(x="Date", y="Temperature (°C)", title="line-timeseries-rolling · letsplot · pyplots.ai")
+    + scale_color_manual(name="", values=["#306998", "#FFD43B"])
+    + scale_alpha_manual(name="", values=[0.5, 1.0])
+    + scale_size_manual(name="", values=[0.8, 2.5])
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_text=element_text(size=16),
+        legend_position="top",
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save PNG (scale 3x for 4800 × 2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save HTML for interactive version
+ggsave(plot, "plot.html", path=".")

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

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+library: letsplot
+specification_id: line-timeseries-rolling
+created: '2025-12-30T17:50:31Z'
+updated: '2025-12-30T18:03:52Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602454238
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent dual-layer visualization clearly distinguishing raw data from smoothed
+    trend
+  - Appropriate use of alpha/size differentiation to show raw data as secondary and
+    rolling average as primary
+  - Realistic temperature sensor scenario with seasonal variation matches specification
+    perfectly
+  - Clean ggplot2-style grammar of graphics implementation
+  - Proper legend placement at top with clear labeling including window size
+  weaknesses:
+  - Legend appears as three separate legend groups (color, alpha, size) instead of
+    a unified legend - consider using guides() to merge them
+  - Raw data line could be slightly thicker for better visibility while maintaining
+    transparency