feat(seaborn): implement line-realtime (#3125)

## Implementation: `line-realtime` - seaborn

Implements the **seaborn** version of `line-realtime`.

**File:** `plots/line-realtime/implementations/seaborn.py`

**Parent Issue:** #3073

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620498178)*

---------

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

Author: github-actions[bot]

plots/line-realtime/implementations/seaborn.py

@@ -0,0 +1,144 @@
+""" pyplots.ai
+line-realtime: Real-Time Updating Line Chart
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from matplotlib.dates import DateFormatter
+
+
+# Data - Simulated CPU usage with realistic patterns
+np.random.seed(42)
+n_points = 100
+visible_points = 60  # Sliding window shows last 60 points
+
+# Create timestamps (100ms intervals)
+base_time = pd.Timestamp("2025-12-31 14:30:00")
+timestamps = pd.date_range(start=base_time, periods=n_points, freq="100ms")
+
+# Generate realistic CPU usage (with spikes and trends)
+base_usage = 45 + np.cumsum(np.random.randn(n_points) * 0.5)
+spikes = np.random.choice([0, 15, 25], size=n_points, p=[0.85, 0.10, 0.05])
+cpu_usage = np.clip(base_usage + spikes + np.random.randn(n_points) * 2, 5, 95)
+
+# Create DataFrame
+df = pd.DataFrame({"timestamp": timestamps, "cpu_usage": cpu_usage})
+
+# Select visible window (sliding window effect)
+df_visible = df.iloc[-visible_points:].copy()
+
+# Create alpha gradient for fade effect (older points more transparent)
+alpha_values = np.linspace(0.3, 1.0, visible_points)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot the main line with gradient effect (segment by segment for fade)
+for i in range(len(df_visible) - 1):
+    sns.lineplot(
+        data=df_visible.iloc[i : i + 2],
+        x="timestamp",
+        y="cpu_usage",
+        ax=ax,
+        color="#306998",
+        linewidth=3,
+        alpha=alpha_values[i],
+        legend=False,
+    )
+
+# Add scatter points at key locations (latest points more visible)
+scatter_indices = [0, len(df_visible) // 4, len(df_visible) // 2, -10, -5, -1]
+for idx in scatter_indices:
+    point = df_visible.iloc[idx]
+    alpha = alpha_values[idx] if idx >= 0 else 1.0
+    size = 150 if idx == -1 else 80
+    ax.scatter(
+        point["timestamp"],
+        point["cpu_usage"],
+        s=size,
+        color="#306998",
+        alpha=alpha,
+        zorder=5,
+        edgecolors="white",
+        linewidth=1.5,
+    )
+
+# Highlight the latest value with annotation
+latest_value = df_visible.iloc[-1]["cpu_usage"]
+latest_time = df_visible.iloc[-1]["timestamp"]
+ax.annotate(
+    f"Latest: {latest_value:.1f}%",
+    xy=(latest_time, latest_value),
+    xytext=(15, 25),
+    textcoords="offset points",
+    fontsize=18,
+    fontweight="bold",
+    color="#306998",
+    bbox={"boxstyle": "round,pad=0.4", "facecolor": "#FFD43B", "edgecolor": "#306998", "linewidth": 2},
+    arrowprops={"arrowstyle": "->", "color": "#306998", "linewidth": 2},
+)
+
+# Add arrow indicating scroll direction (old data scrolls off left)
+ax.annotate(
+    "",
+    xy=(df_visible.iloc[0]["timestamp"], 92),
+    xytext=(df_visible.iloc[8]["timestamp"], 92),
+    arrowprops={"arrowstyle": "<-", "color": "#666666", "linewidth": 2.5, "mutation_scale": 15},
+)
+ax.text(
+    df_visible.iloc[4]["timestamp"],
+    96,
+    "← Old data scrolls off",
+    fontsize=13,
+    color="#666666",
+    ha="center",
+    style="italic",
+)
+
+# Styling
+ax.set_xlabel("Time (HH:MM:SS)", fontsize=20)
+ax.set_ylabel("CPU Usage (%)", fontsize=20)
+ax.set_title("line-realtime · seaborn · pyplots.ai", fontsize=24, fontweight="bold")
+ax.tick_params(axis="both", labelsize=16)
+
+# Format x-axis time labels (cleaner format)
+ax.xaxis.set_major_formatter(DateFormatter("%H:%M:%S"))
+plt.xticks(rotation=25, ha="right")
+
+# Set y-axis limits with padding
+ax.set_ylim(0, 100)
+
+# Grid styling
+ax.grid(True, alpha=0.3, linestyle="--")
+ax.set_facecolor("#fafafa")
+
+# Add live indicator with pulsing effect simulation
+ax.text(
+    0.02,
+    0.96,
+    "● LIVE",
+    transform=ax.transAxes,
+    fontsize=18,
+    fontweight="bold",
+    color="#e74c3c",
+    verticalalignment="top",
+)
+
+# Add window info
+ax.text(
+    0.98,
+    0.02,
+    f"Sliding window: {visible_points} samples (6 sec)",
+    transform=ax.transAxes,
+    fontsize=13,
+    color="#666666",
+    ha="right",
+    va="bottom",
+)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/line-realtime/metadata/seaborn.yaml

@@ -0,0 +1,27 @@
+library: seaborn
+specification_id: line-realtime
+created: '2025-12-31T14:06:35Z'
+updated: '2025-12-31T14:18:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620498178
+issue: 3073
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-realtime/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-realtime/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual communication of real-time concept with LIVE indicator and scroll
+    direction arrow
+  - Latest value annotation with contrasting yellow background draws attention effectively
+  - Alpha gradient fade effect successfully conveys older data visually
+  - Proper sliding window implementation showing 60 of 100 generated points
+  - Clean title format and axis labels with units
+  - Realistic CPU monitoring scenario with appropriate value ranges
+  weaknesses:
+  - Calling sns.lineplot 59 times in a loop is inefficient; consider using matplotlib
+    LineCollection for gradient or a single seaborn call with post-processing
+  - Data lacks dramatic variation - mostly hovers around 35-50% with only one visible
+    spike