update(density-basic): matplotlib — comprehensive quality review (#4380)

## Summary

Updated **matplotlib** implementation for **density-basic**.

**Changes:** Comprehensive quality review

### Changes
- Improved visual design and spec compliance
- Quality: 92/100 (local self-evaluation)

## Test Plan

- [x] Preview images uploaded to GCS staging
- [x] Implementation file passes ruff format/check
- [x] Metadata YAML updated with current versions
- [ ] Automated review triggered

---
Generated with [Claude Code](https://claude.com/claude-code) `/update`
command

---------

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

Author: MarkusNeusinger

plots/density-basic/implementations/matplotlib.py

@@ -1,54 +1,79 @@
 """ pyplots.ai
 density-basic: Basic Density Plot
-Library: matplotlib 3.10.8 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: matplotlib 3.10.8 | Python 3.14.3
+Quality: 91/100 | Updated: 2026-02-23
 """
 
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
 import numpy as np
+from matplotlib.collections import EventCollection
+from matplotlib.colors import LinearSegmentedColormap
+from matplotlib.patches import PathPatch
+from matplotlib.path import Path
 from scipy import stats
 
 
-# Data - Simulating test scores with realistic distribution characteristics
+# Data - Response times (ms) showing bimodal server behavior
 np.random.seed(42)
-# Create a slightly left-skewed distribution typical of test scores
-values = np.concatenate(
-    [
-        np.random.normal(72, 12, 400),  # Main group of students
-        np.random.normal(45, 8, 100),  # Lower performing group (creates slight bimodality)
-    ]
-)
-values = np.clip(values, 0, 100)  # Clip to valid test score range
+cached_responses = np.random.normal(45, 12, 350)  # Fast cache-hit requests
+db_responses = np.random.normal(140, 25, 150)  # Slower database-query requests
+response_times = np.concatenate([cached_responses, db_responses])
+response_times = response_times[response_times > 0]
 
-# Calculate kernel density estimation
-kde = stats.gaussian_kde(values)
-x_range = np.linspace(values.min() - 5, values.max() + 5, 500)
+# Compute KDE with Silverman bandwidth selection
+kde = stats.gaussian_kde(response_times, bw_method="silverman")
+x_range = np.linspace(0, response_times.max() + 30, 600)
 density = kde(x_range)
 
-# Create plot (4800x2700 px)
+# Plot
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Plot density curve with fill
-ax.fill_between(x_range, density, alpha=0.4, color="#306998")
-ax.plot(x_range, density, linewidth=3, color="#306998")
+# Vertical gradient fill (dark at base, light at top) clipped to density curve
+cmap = LinearSegmentedColormap.from_list("blue_fade", ["#306998", "#c4daf0"])
+gradient = np.linspace(0, 1, 256).reshape(-1, 1)
+ax.imshow(
+    gradient,
+    extent=[x_range[0], x_range[-1], 0, density.max()],
+    aspect="auto",
+    cmap=cmap,
+    alpha=0.5,
+    origin="lower",
+    zorder=1,
+)
+verts = np.column_stack([np.concatenate([x_range, [x_range[-1], x_range[0]]]), np.concatenate([density, [0, 0]])])
+codes = [Path.MOVETO] + [Path.LINETO] * (len(verts) - 1)
+clip_path = PathPatch(Path(verts, codes), transform=ax.transData, facecolor="none", edgecolor="none")
+ax.add_patch(clip_path)
+for artist in ax.get_images():
+    artist.set_clip_path(clip_path)
+
+# Density curve
+ax.plot(x_range, density, linewidth=3, color="#306998", zorder=3)
 
-# Add rug plot showing individual observations
-rug_height = -0.0015
-ax.plot(values, np.full_like(values, rug_height), "|", color="#306998", alpha=0.4, markersize=12)
+# Rug plot using EventCollection
+rug = EventCollection(
+    response_times, lineoffset=-0.0006, linelength=0.001, linewidth=0.8, color="#306998", alpha=0.4, zorder=2
+)
+ax.add_collection(rug)
 
-# Labels and styling (scaled font sizes for 4800x2700)
-ax.set_xlabel("Test Score", fontsize=20)
+# Style
+ax.set_xlabel("Response Time (ms)", fontsize=20)
 ax.set_ylabel("Density", fontsize=20)
-ax.set_title("density-basic · matplotlib · pyplots.ai", fontsize=24)
-ax.tick_params(axis="both", labelsize=16)
-ax.grid(True, alpha=0.3, linestyle="--")
+ax.set_title("density-basic \u00b7 matplotlib \u00b7 pyplots.ai", fontsize=24, fontweight="medium", pad=16)
+ax.tick_params(axis="both", labelsize=16, length=0)
+ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%g"))
 
-# Remove top and right spines for cleaner look
+# Grid and spines
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
+ax.spines["left"].set_color("#888888")
+ax.spines["bottom"].set_color("#888888")
 
-# Set y-axis to show rug plot
-ax.set_ylim(bottom=-0.003)
+# Axis limits
+ax.set_xlim(x_range[0], x_range[-1])
+ax.set_ylim(bottom=-0.0018)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")