feat(matplotlib): implement scatter-annotated (#2822)

## Implementation: `scatter-annotated` - matplotlib

Implements the **matplotlib** version of `scatter-annotated`.

**File:** `plots/scatter-annotated/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602450818)*

---------

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

Author: github-actions[bot]

plots/scatter-annotated/implementations/matplotlib.py

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+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Technology companies with market cap and revenue
+# Points intentionally spread out to minimize annotation overlap
+np.random.seed(42)
+companies = [
+    "TechCorp",
+    "DataSys",
+    "CloudNet",
+    "ByteWorks",
+    "QuantumAI",
+    "NexGen",
+    "CyberFlow",
+    "InfoPrime",
+    "CodeLabs",
+    "DigiCore",
+    "NetSphere",
+    "AlgoTech",
+    "VisionX",
+    "PulseTech",
+    "ZetaLogic",
+]
+# Market cap in billions (x-axis)
+market_cap = np.array([55, 135, 105, 18, 225, 70, 120, 45, 165, 85, 30, 195, 150, 175, 60])
+# Annual revenue in billions (y-axis)
+revenue = np.array([12, 42, 30, 4, 65, 18, 38, 10, 52, 24, 6, 58, 45, 50, 15])
+
+# Create figure (16:9 landscape)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Scatter plot with Python Blue color
+ax.scatter(market_cap, revenue, s=250, alpha=0.7, color="#306998", edgecolors="white", linewidths=2, zorder=3)
+
+# Custom annotation offsets for each point to avoid overlap
+# Positive x_offset = right, negative = left
+# Positive y_offset = up, negative = down
+offsets = [
+    (10, -10),  # TechCorp - below right
+    (-10, 8),  # DataSys - above left
+    (10, 8),  # CloudNet - above right
+    (-10, -12),  # ByteWorks - below left
+    (10, 8),  # QuantumAI - above right
+    (-10, 8),  # NexGen - above left
+    (10, -10),  # CyberFlow - below right
+    (10, -10),  # InfoPrime - below right (adjusted)
+    (-12, 8),  # CodeLabs - above left (adjusted)
+    (10, 8),  # DigiCore - above right
+    (-10, -10),  # NetSphere - below left (adjusted)
+    (10, -12),  # AlgoTech - below right
+    (10, 8),  # VisionX - above right (adjusted)
+    (10, 10),  # PulseTech - above right (adjusted)
+    (-10, -10),  # ZetaLogic - below left (adjusted)
+]
+
+# Annotate each point with company name
+for i, company in enumerate(companies):
+    x_offset, y_offset = offsets[i]
+    ha = "left" if x_offset > 0 else "right"
+
+    ax.annotate(
+        company,
+        xy=(market_cap[i], revenue[i]),
+        xytext=(x_offset, y_offset),
+        textcoords="offset points",
+        fontsize=13,
+        color="#333333",
+        fontweight="medium",
+        ha=ha,
+        va="center",
+        arrowprops={"arrowstyle": "-", "color": "#888888", "lw": 1, "connectionstyle": "arc3,rad=0"},
+        zorder=4,
+    )
+
+# Styling
+ax.set_xlabel("Market Capitalization ($ Billions)", fontsize=20)
+ax.set_ylabel("Annual Revenue ($ Billions)", fontsize=20)
+ax.set_title("scatter-annotated · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--", zorder=1)
+
+# Set axis limits with padding
+ax.set_xlim(0, 240)
+ax.set_ylim(0, 70)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/scatter-annotated/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: scatter-annotated
+created: '2025-12-30T17:49:45Z'
+updated: '2025-12-30T18:01:09Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602450818
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent manual offset positioning for annotations prevents all label overlaps
+  - Connector lines from labels to points add clarity without visual clutter
+  - Realistic tech company data with plausible market cap and revenue figures
+  - Clean, readable code following KISS principles
+  - Proper font sizing for 4800x2700 output resolution
+  weaknesses:
+  - Does not use adjustText or similar library for automatic label placement as suggested
+    in spec (though manual approach works well for this dataset)