feat(seaborn): implement line-annotated-events (#3011)

## Implementation: `line-annotated-events` - seaborn

Implements the **seaborn** version of `line-annotated-events`.

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

**Parent Issue:** #2997

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617488740)*

---------

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

Author: github-actions[bot]

plots/line-annotated-events/implementations/seaborn.py

@@ -0,0 +1,86 @@
+""" pyplots.ai
+line-annotated-events: Annotated Line Plot with Event Markers
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - Simulating monthly product sales with marketing events
+np.random.seed(42)
+
+# Create 12 months of sales data
+dates = pd.date_range("2024-01-01", periods=365, freq="D")
+# Base trend with seasonality and noise
+trend = np.linspace(100, 180, 365)
+seasonality = 15 * np.sin(np.linspace(0, 4 * np.pi, 365))
+noise = np.random.normal(0, 8, 365)
+sales = trend + seasonality + noise
+
+df = pd.DataFrame({"date": dates, "sales": sales})
+
+# Events - Key marketing milestones
+events = pd.DataFrame(
+    {
+        "event_date": pd.to_datetime(["2024-02-14", "2024-05-01", "2024-07-15", "2024-09-20", "2024-11-25"]),
+        "event_label": ["Valentine's Campaign", "Spring Sale", "Summer Launch", "Fall Promotion", "Black Friday"],
+    }
+)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Main line plot using seaborn
+sns.lineplot(data=df, x="date", y="sales", ax=ax, linewidth=2.5, color="#306998")
+
+# Add event markers with alternating heights for readability
+y_positions = [0.85, 0.75, 0.85, 0.75, 0.85]  # Alternating label positions
+colors_events = ["#FFD43B"] * len(events)  # Use Python Yellow for all events
+
+for i, (_, event) in enumerate(events.iterrows()):
+    # Vertical line at event date
+    ax.axvline(x=event["event_date"], color=colors_events[i], linestyle="--", linewidth=2, alpha=0.8)
+
+    # Event label with background
+    y_pos = ax.get_ylim()[0] + (ax.get_ylim()[1] - ax.get_ylim()[0]) * y_positions[i]
+    ax.annotate(
+        event["event_label"],
+        xy=(event["event_date"], y_pos),
+        fontsize=14,
+        fontweight="bold",
+        color="#333333",
+        ha="center",
+        va="bottom",
+        bbox={"boxstyle": "round,pad=0.3", "facecolor": "#FFD43B", "edgecolor": "none", "alpha": 0.9},
+        rotation=0,
+    )
+
+    # Small marker on the line at event date
+    event_sales = df.loc[df["date"] == event["event_date"], "sales"]
+    if not event_sales.empty:
+        ax.scatter(
+            event["event_date"],
+            event_sales.values[0],
+            color="#FFD43B",
+            s=150,
+            zorder=5,
+            edgecolor="#333333",
+            linewidth=2,
+        )
+
+# Styling
+ax.set_xlabel("Date", fontsize=20)
+ax.set_ylabel("Daily Sales (Units)", fontsize=20)
+ax.set_title("line-annotated-events · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+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-annotated-events/metadata/seaborn.yaml

@@ -0,0 +1,25 @@
+library: seaborn
+specification_id: line-annotated-events
+created: '2025-12-31T10:54:41Z'
+updated: '2025-12-31T10:59:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617488740
+issue: 2997
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-annotated-events/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-annotated-events/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of alternating label heights to prevent overlap with dense events
+  - 'Clear visual hierarchy: blue data line, yellow event markers create strong contrast'
+  - Comprehensive data generation with realistic trend, seasonality, and noise
+  - Proper handling of event markers on the actual data line with scatter points
+  - Well-sized text elements for readability at high resolution
+  weaknesses:
+  - No legend or key explaining the event marker style (spec mentions including a
+    subtle legend or key)
+  - Realistic context score could be higher with more domain-specific context (e.g.,
+    actual product category mentioned in labels)