feat(matplotlib): implement subplot-mosaic (#3028)

## Implementation: `subplot-mosaic` - matplotlib

Implements the **matplotlib** version of `subplot-mosaic`.

**File:** `plots/subplot-mosaic/implementations/matplotlib.py`

**Parent Issue:** #3002

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617508043)*

---------

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

Author: github-actions[bot]

plots/subplot-mosaic/implementations/matplotlib.py

@@ -0,0 +1,99 @@
+""" pyplots.ai
+subplot-mosaic: Mosaic Subplot Layout with Varying Sizes
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data
+np.random.seed(42)
+
+# Time series data for main overview chart
+dates = np.arange(100)
+sales = np.cumsum(np.random.randn(100) * 5 + 2) + 500
+
+# Category data for bar chart
+categories = ["Product A", "Product B", "Product C", "Product D"]
+values = [85, 120, 95, 110]
+
+# Scatter data for correlation plot
+x_scatter = np.random.normal(50, 15, 80)
+y_scatter = x_scatter * 0.8 + np.random.normal(0, 10, 80)
+
+# Histogram data
+measurements = np.random.normal(100, 20, 200)
+
+# Metric values for small panels
+metrics = {"Growth": 12.5, "Conversion": 3.2, "Retention": 87.4}
+
+# Create mosaic layout: "AAB;AAB;CDE" pattern
+# A = large overview (spans 2 rows, 2 cols), B = bar chart (right side, 2 rows)
+# C, D, E = three small panels at bottom
+mosaic = """
+AAB
+AAB
+CDE
+"""
+
+fig, axes = plt.subplot_mosaic(mosaic, figsize=(16, 9))
+
+# A: Main time series overview
+axes["A"].plot(dates, sales, linewidth=3, color="#306998")
+axes["A"].fill_between(dates, sales.min(), sales, alpha=0.3, color="#306998")
+axes["A"].set_xlabel("Day", fontsize=18)
+axes["A"].set_ylabel("Cumulative Sales ($)", fontsize=18)
+axes["A"].set_title("Sales Overview", fontsize=22)
+axes["A"].tick_params(axis="both", labelsize=14)
+axes["A"].grid(True, alpha=0.3, linestyle="--")
+
+# B: Bar chart for categories
+bars = axes["B"].barh(categories, values, color="#FFD43B", edgecolor="#306998", linewidth=2)
+axes["B"].set_xlabel("Units Sold", fontsize=18)
+axes["B"].set_title("Product Performance", fontsize=22)
+axes["B"].tick_params(axis="both", labelsize=14)
+axes["B"].grid(True, alpha=0.3, linestyle="--", axis="x")
+# Add value labels
+for bar, val in zip(bars, values, strict=True):
+    axes["B"].text(val + 2, bar.get_y() + bar.get_height() / 2, str(val), va="center", fontsize=14)
+
+# C: Scatter plot
+axes["C"].scatter(x_scatter, y_scatter, s=100, alpha=0.7, color="#306998", edgecolor="white", linewidth=1)
+axes["C"].set_xlabel("Feature X", fontsize=16)
+axes["C"].set_ylabel("Feature Y", fontsize=16)
+axes["C"].set_title("Correlation", fontsize=20)
+axes["C"].tick_params(axis="both", labelsize=12)
+axes["C"].grid(True, alpha=0.3, linestyle="--")
+
+# D: Histogram
+axes["D"].hist(measurements, bins=20, color="#306998", edgecolor="white", linewidth=1, alpha=0.8)
+axes["D"].set_xlabel("Value", fontsize=16)
+axes["D"].set_ylabel("Frequency", fontsize=16)
+axes["D"].set_title("Distribution", fontsize=20)
+axes["D"].tick_params(axis="both", labelsize=12)
+axes["D"].grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# E: Metrics display
+axes["E"].set_xlim(0, 1)
+axes["E"].set_ylim(0, 1)
+axes["E"].axis("off")
+axes["E"].set_title("Key Metrics", fontsize=20)
+y_positions = [0.75, 0.45, 0.15]
+for (name, value), y_pos in zip(metrics.items(), y_positions, strict=True):
+    axes["E"].text(0.5, y_pos, name, ha="center", va="center", fontsize=16, fontweight="bold")
+    if name == "Growth":
+        axes["E"].text(0.5, y_pos - 0.12, f"+{value}%", ha="center", va="center", fontsize=24, color="#306998")
+    elif name == "Conversion":
+        axes["E"].text(0.5, y_pos - 0.12, f"{value}%", ha="center", va="center", fontsize=24, color="#306998")
+    else:
+        axes["E"].text(0.5, y_pos - 0.12, f"{value}%", ha="center", va="center", fontsize=24, color="#306998")
+# Add box around metrics
+axes["E"].add_patch(plt.Rectangle((0.05, 0.02), 0.9, 0.96, fill=False, edgecolor="#306998", linewidth=2))
+
+# Main title
+fig.suptitle("subplot-mosaic · matplotlib · pyplots.ai", fontsize=26, fontweight="bold", y=0.98)
+
+plt.tight_layout(rect=[0, 0, 1, 0.95])
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/subplot-mosaic/metadata/matplotlib.yaml

@@ -0,0 +1,28 @@
+library: matplotlib
+specification_id: subplot-mosaic
+created: '2025-12-31T10:56:15Z'
+updated: '2025-12-31T11:07:18Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617508043
+issue: 3002
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-mosaic/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-mosaic/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent demonstration of plt.subplot_mosaic() with the AAB;AAB;CDE pattern -
+    this is exactly what the spec asked for
+  - Great variety of plot types (line with fill_between, horizontal bar, scatter,
+    histogram, text metrics)
+  - Clean, professional color scheme with accessible blue/yellow palette
+  - Well-structured dashboard layout with clear visual hierarchy (large overview,
+    medium bar chart, small detail panels)
+  - Realistic sales dashboard scenario with appropriate data scales
+  weaknesses:
+  - Scatter plot markers could be larger (s=100 is adequate but s=150 would be more
+    visible for 80 points)
+  - Missing demonstration of empty cells with . placeholder mentioned in spec notes
+  - Metrics panel (E) layout could be tighter - the box has significant internal whitespace