update(density-basic): seaborn — comprehensive quality review (#4381)

## Summary

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

Author: MarkusNeusinger

plots/density-basic/implementations/seaborn.py

@@ -1,38 +1,74 @@
 """ pyplots.ai
 density-basic: Basic Density Plot
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 93/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 91/100 | Updated: 2026-02-23
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
 
 
-# Data - create a realistic bimodal distribution of test scores
+# Data - bimodal distribution of test scores
 np.random.seed(42)
 test_scores = np.concatenate(
     [
-        np.random.normal(72, 10, 300),  # Main group centered at 72
-        np.random.normal(88, 5, 100),  # High achievers group
+        np.random.normal(68, 8, 280),  # Main group centered at 68
+        np.random.normal(90, 4, 120),  # High achievers group
     ]
 )
 
-# Create figure
+# Plot
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Plot density curve with fill
-sns.kdeplot(data=test_scores, ax=ax, fill=True, alpha=0.6, color="#306998", linewidth=3)
+# KDE line first (for peak detection), then fill
+sns.kdeplot(data=test_scores, ax=ax, fill=False, color="#306998", linewidth=3, bw_adjust=0.9)
+line = ax.get_lines()[0]
+x_kde, y_kde = line.get_xdata(), line.get_ydata()
+ax.fill_between(x_kde, y_kde, alpha=0.5, color="#306998")
 
-# Add rug plot to show individual observations
-sns.rugplot(data=test_scores, ax=ax, color="#306998", alpha=0.3, height=0.03)
+sns.rugplot(data=test_scores, ax=ax, color="#1a3a5c", alpha=0.5, height=0.05)
+
+# Annotate the two peaks to tell the bimodal story
+peak1_idx = np.argmax(y_kde[x_kde < 80])
+peak2_idx = len(y_kde[x_kde < 80]) + np.argmax(y_kde[x_kde >= 80])
+
+px1, py1 = x_kde[peak1_idx], y_kde[peak1_idx]
+ax.annotate(
+    "Main group",
+    xy=(px1, py1),
+    xytext=(px1 - 14, py1 - 0.008),
+    fontsize=16,
+    fontweight="medium",
+    color="#1a3a5c",
+    arrowprops={"arrowstyle": "->", "color": "#1a3a5c", "lw": 1.5},
+    ha="center",
+    va="top",
+)
+ax.axvline(px1, color="#306998", alpha=0.15, linestyle="--", linewidth=1.5)
+
+px2, py2 = x_kde[peak2_idx], y_kde[peak2_idx]
+ax.annotate(
+    "High achievers",
+    xy=(px2, py2),
+    xytext=(px2 + 12, py2 - 0.004),
+    fontsize=16,
+    fontweight="medium",
+    color="#1a3a5c",
+    arrowprops={"arrowstyle": "->", "color": "#1a3a5c", "lw": 1.5},
+    ha="center",
+    va="top",
+)
+ax.axvline(px2, color="#306998", alpha=0.15, linestyle="--", linewidth=1.5)
 
 # Style
 ax.set_xlabel("Test Score (points)", fontsize=20)
 ax.set_ylabel("Density", fontsize=20)
-ax.set_title("density-basic · seaborn · pyplots.ai", fontsize=24)
+ax.set_title("density-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium")
 ax.tick_params(axis="both", labelsize=16)
-ax.grid(True, alpha=0.3, linestyle="--")
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.yaxis.grid(True, alpha=0.2, linewidth=0.8)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")