feat(matplotlib): implement histogram-density

plots/histogram-density/implementations/matplotlib.py

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+""" pyplots.ai
+histogram-density: Density Histogram
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-29
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data: Generate realistic test score data with a normal distribution
+np.random.seed(42)
+test_scores = np.random.normal(loc=75, scale=12, size=500)
+# Clip to realistic score range
+test_scores = np.clip(test_scores, 0, 100)
+
+# Create theoretical normal PDF for overlay (using numpy)
+mu, sigma = 75, 12
+x_pdf = np.linspace(30, 110, 200)
+pdf = (1 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x_pdf - mu) / sigma) ** 2)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Density histogram (density=True normalizes so area = 1)
+ax.hist(
+    test_scores,
+    bins=25,
+    density=True,
+    alpha=0.7,
+    color="#306998",
+    edgecolor="white",
+    linewidth=1.5,
+    label="Observed Distribution",
+)
+
+# Overlay theoretical normal PDF
+ax.plot(x_pdf, pdf, color="#FFD43B", linewidth=3, label="Normal PDF (μ=75, σ=12)")
+
+# Labels and styling
+ax.set_xlabel("Test Score (points)", fontsize=20)
+ax.set_ylabel("Probability Density", fontsize=20)
+ax.set_title("histogram-density · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=16, loc="upper left")
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Set axis limits for clean display
+ax.set_xlim(30, 110)
+ax.set_ylim(0, None)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/histogram-density/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: histogram-density
+created: '2025-12-29T22:46:41Z'
+updated: '2025-12-29T22:50:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584327554
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-density/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-density/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent specification compliance with density histogram and theoretical PDF
+    overlay
+  - Clean, readable code following KISS principles
+  - Great color contrast between histogram bars and PDF curve
+  - Perfect title format and axis labeling with units
+  - Realistic test score data scenario with appropriate sample size (n=500)
+  weaknesses:
+  - Could use more distinctive matplotlib features like fill_between for PDF shading
+    or axvline for mean
+  - Legend positioned in upper left slightly overlaps with potential data area