feat(plotly): implement line-timeseries-rolling (#2795)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602448540)*

---------

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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/plotly.py

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+""" pyplots.ai
+line-timeseries-rolling: Time Series with Rolling Average Overlay
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+
+# Data - Daily temperature readings with noise
+np.random.seed(42)
+
+# Generate 200 days of data
+dates = pd.date_range("2024-01-01", periods=200, freq="D")
+
+# Create realistic temperature pattern with seasonal trend + noise
+days = np.arange(200)
+seasonal = 15 * np.sin(2 * np.pi * days / 365 - np.pi / 2)  # Seasonal component
+trend = 0.02 * days  # Slight warming trend
+noise = np.random.randn(200) * 3  # Daily fluctuations
+base_temp = 12  # Base temperature (Celsius)
+
+raw_values = base_temp + seasonal + trend + noise
+
+# Create DataFrame
+df = pd.DataFrame({"date": dates, "value": raw_values})
+
+# Calculate 14-day rolling average
+window_size = 14
+df["rolling_avg"] = df["value"].rolling(window=window_size, center=False).mean()
+
+# Create figure
+fig = go.Figure()
+
+# Raw data - lighter, semi-transparent line
+fig.add_trace(
+    go.Scatter(
+        x=df["date"], y=df["value"], mode="lines", name="Raw Data", line=dict(color="#306998", width=1.5), opacity=0.4
+    )
+)
+
+# Rolling average - prominent smooth line
+fig.add_trace(
+    go.Scatter(
+        x=df["date"],
+        y=df["rolling_avg"],
+        mode="lines",
+        name=f"{window_size}-Day Rolling Average",
+        line=dict(color="#FFD43B", width=4),
+    )
+)
+
+# Layout for 4800x2700 canvas
+fig.update_layout(
+    title=dict(text="line-timeseries-rolling · plotly · pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Date", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(0,0,0,0.1)",
+    ),
+    yaxis=dict(
+        title=dict(text="Temperature (°C)", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridwidth=1,
+        gridcolor="rgba(0,0,0,0.1)",
+    ),
+    template="plotly_white",
+    legend=dict(
+        font=dict(size=20),
+        x=0.02,
+        y=0.98,
+        xanchor="left",
+        yanchor="top",
+        bgcolor="rgba(255,255,255,0.8)",
+        bordercolor="rgba(0,0,0,0.2)",
+        borderwidth=1,
+    ),
+    margin=dict(l=100, r=50, t=100, b=80),
+    plot_bgcolor="white",
+)
+
+# Save as PNG (4800x2700)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save interactive HTML
+fig.write_html("plot.html", include_plotlyjs="cdn")

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

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+library: plotly
+specification_id: line-timeseries-rolling
+created: '2025-12-30T17:47:33Z'
+updated: '2025-12-30T17:51:40Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602448540
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/plotly/plot.html
+quality_score: 93
+review:
+  strengths:
+  - Excellent visual clarity with well-chosen colors (blue raw data, yellow rolling
+    average) that provide strong contrast
+  - Rolling average line appropriately prominent (width=4) while raw data is appropriately
+    subdued (opacity=0.4)
+  - Legend includes the window size (14-Day Rolling Average) as specified in the spec
+  - Clean code structure following KISS principles with proper seed for reproducibility
+  - Realistic temperature scenario with believable seasonal patterns
+  - Produces both PNG and interactive HTML outputs leveraging Plotly strengths
+  weaknesses:
+  - Grid opacity at 0.1 is almost too subtle; could be increased to 0.2-0.3 for better
+    readability
+  - Could leverage more Plotly-specific features like customized hover templates showing
+    both raw and rolling values, or a range selector for interactivity