feat(matplotlib): implement histogram-stacked (#2617)

## Implementation: `histogram-stacked` - matplotlib

Implements the **matplotlib** version of `histogram-stacked`.

**File:** `plots/histogram-stacked/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594557552)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/histogram-stacked/implementations/matplotlib.py

@@ -0,0 +1,54 @@
+""" pyplots.ai
+histogram-stacked: Stacked Histogram
+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 - Response times (ms) for three different server regions
+np.random.seed(42)
+
+# Generate data for three server regions with different distributions
+us_east = np.random.normal(loc=45, scale=12, size=200)  # Fast region
+europe = np.random.normal(loc=65, scale=15, size=180)  # Medium region
+asia = np.random.normal(loc=80, scale=20, size=150)  # Slower region
+
+# Clip to realistic values
+us_east = np.clip(us_east, 10, 120)
+europe = np.clip(europe, 15, 140)
+asia = np.clip(asia, 20, 160)
+
+# Colors - Python Blue first, then Yellow, then complementary
+colors = ["#306998", "#FFD43B", "#5BA85B"]
+labels = ["US-East", "Europe", "Asia-Pacific"]
+
+# Create plot (4800x2700 px at 300 DPI = 16x9 inches)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Create stacked histogram
+ax.hist(
+    [us_east, europe, asia],
+    bins=20,
+    stacked=True,
+    color=colors,
+    label=labels,
+    edgecolor="white",
+    linewidth=0.8,
+    alpha=0.9,
+)
+
+# Labels and styling (scaled font sizes for 4800x2700)
+ax.set_xlabel("Response Time (ms)", fontsize=20)
+ax.set_ylabel("Number of Requests", fontsize=20)
+ax.set_title("histogram-stacked · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# Legend
+ax.legend(fontsize=16, loc="upper right", framealpha=0.9)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/histogram-stacked/metadata/matplotlib.yaml

@@ -0,0 +1,26 @@
+library: matplotlib
+specification_id: histogram-stacked
+created: '2025-12-30T10:38:07Z'
+updated: '2025-12-30T10:45:38Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594557552
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-stacked/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-stacked/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent realistic data scenario with server response times across geographic
+    regions
+  - Clean stacking visualization with good color separation using white edge lines
+  - All three distributions have distinct characteristics (different means, scales,
+    and sample sizes)
+  - Perfect title format and axis labeling with units
+  - Good use of alpha for slight transparency while maintaining visibility
+  weaknesses:
+  - No distinctive matplotlib features beyond basic histogram; could use annotations,
+    custom spine styling, or statistical overlays
+  - Legend visual separation could be enhanced with a border or better background