feat(matplotlib): implement contour-density (#2558)

## Implementation: `contour-density` - matplotlib

Implements the **matplotlib** version of `contour-density`.

**File:** `plots/contour-density/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593350594)*

---------

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

Author: github-actions[bot]

plots/contour-density/implementations/matplotlib.py

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+""" pyplots.ai
+contour-density: Density Contour Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import stats
+
+
+# Data - bivariate distribution with two clusters
+np.random.seed(42)
+
+# Cluster 1: Main cluster centered around (5, 5)
+n1 = 300
+x1 = np.random.normal(5, 1.5, n1)
+y1 = np.random.normal(5, 1.2, n1)
+
+# Cluster 2: Secondary cluster centered around (9, 8)
+n2 = 150
+x2 = np.random.normal(9, 0.8, n2)
+y2 = np.random.normal(8, 1.0, n2)
+
+# Combine clusters
+x = np.concatenate([x1, x2])
+y = np.concatenate([y1, y2])
+
+# Compute 2D kernel density estimation
+xmin, xmax = x.min() - 1, x.max() + 1
+ymin, ymax = y.min() - 1, y.max() + 1
+xx, yy = np.mgrid[xmin:xmax:200j, ymin:ymax:200j]
+positions = np.vstack([xx.ravel(), yy.ravel()])
+values = np.vstack([x, y])
+kernel = stats.gaussian_kde(values)
+density = np.reshape(kernel(positions).T, xx.shape)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Filled contours for visual impact
+contourf = ax.contourf(xx, yy, density, levels=12, cmap="Blues", alpha=0.8)
+
+# Contour lines for clarity
+contour = ax.contour(xx, yy, density, levels=12, colors="#306998", linewidths=1.5, alpha=0.9)
+
+# Scatter plot overlay for context (smaller, semi-transparent points)
+ax.scatter(x, y, s=30, alpha=0.3, color="#FFD43B", edgecolors="#306998", linewidths=0.5, zorder=5)
+
+# Colorbar
+cbar = plt.colorbar(contourf, ax=ax, shrink=0.85, pad=0.02)
+cbar.set_label("Density", fontsize=18)
+cbar.ax.tick_params(labelsize=14)
+
+# Labels and styling
+ax.set_xlabel("X Variable", fontsize=20)
+ax.set_ylabel("Y Variable", fontsize=20)
+ax.set_title("contour-density · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/contour-density/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: contour-density
+created: '2025-12-30T09:30:38Z'
+updated: '2025-12-30T09:36:58Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593350594
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-density/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-density/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent combination of filled contours with contour lines for visual clarity
+  - Appropriate scatter point overlay with semi-transparency showing individual points
+  - Good colorbar implementation with proper labeling
+  - Two-cluster data effectively demonstrates density pattern detection
+  - Clean, well-structured code following KISS principles
+  - Proper use of KDE from scipy for density computation
+  weaknesses:
+  - Axis labels are generic (X Variable, Y Variable) - could use descriptive labels
+    with units
+  - Does not use matplotlib contour label feature (ax.clabel) which would add value
+  - Scatter point size (s=30) is on the smaller side for overlay context