update(heatmap-basic): matplotlib — comprehensive quality review (#4252)

## Summary

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

**Changes:** Comprehensive quality review — fix weaknesses from prior
reviews, preserve strengths, improve quality across all dimensions.

### Changes
- Addressed review weaknesses from prior quality assessment
- Improved data choice and visual design
- Enhanced library-specific feature usage
- Updated to current library and Python versions
- Quality self-assessment: pending CI review

## 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/heatmap-basic/implementations/matplotlib.py

@@ -1,53 +1,83 @@
 """ pyplots.ai
 heatmap-basic: Basic Heatmap
-Library: matplotlib 3.10.8 | Python 3.13.11
-Quality: 94/100 | Created: 2025-12-23
+Library: matplotlib 3.10.8 | Python 3.14.3
+Quality: 90/100 | Updated: 2026-02-16
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib.colors import TwoSlopeNorm
 
 
-# Data - correlation-like matrix with meaningful labels
-np.random.seed(42)
-categories = ["Sales", "Marketing", "Support", "Dev", "HR", "Finance", "Ops", "Legal"]
-n = len(categories)
+# Data - department correlation matrix with clear clustering patterns
+# Grouped: Revenue (Sales, Marketing, Finance) cluster positively;
+# Technical (Dev, Ops, Support) cluster positively; HR & Legal are distinct
+departments = ["Sales", "Marketing", "Finance", "Dev", "Ops", "Support", "HR", "Legal"]
+n = len(departments)
 
-# Generate a correlation-like symmetric matrix
-raw = np.random.randn(n, n)
-data = (raw + raw.T) / 2  # Make symmetric
-np.fill_diagonal(data, 1)  # Perfect correlation on diagonal
-data = np.clip(data, -1, 1)  # Clip to valid correlation range
+data = np.array(
+    [
+        [1.00, 0.82, 0.61, 0.12, 0.44, 0.35, -0.15, 0.08],  # Sales
+        [0.82, 1.00, 0.48, 0.18, 0.30, 0.28, 0.10, -0.20],  # Marketing
+        [0.61, 0.48, 1.00, -0.65, 0.05, -0.38, 0.30, 0.40],  # Finance
+        [0.12, 0.18, -0.65, 1.00, 0.42, 0.55, -0.10, -0.30],  # Dev
+        [0.44, 0.30, 0.05, 0.42, 1.00, 0.60, -0.08, 0.20],  # Ops
+        [0.35, 0.28, -0.38, 0.55, 0.60, 1.00, 0.22, 0.15],  # Support
+        [-0.15, 0.10, 0.30, -0.10, -0.08, 0.22, 1.00, 0.52],  # HR
+        [0.08, -0.20, 0.40, -0.30, 0.20, 0.15, 0.52, 1.00],  # Legal
+    ]
+)
 
-# Create plot (3600x3600 px - square format best for heatmaps)
+# Plot - square figure for 3600x3600 at 300 dpi
 fig, ax = plt.subplots(figsize=(12, 12))
 
-# Heatmap with diverging colormap for positive/negative values
-im = ax.imshow(data, cmap="RdBu_r", vmin=-1, vmax=1, aspect="equal")
+# Heatmap with RdBu_r - perceptually uniform and colorblind-friendly diverging map
+norm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
+im = ax.imshow(data, cmap="RdBu_r", norm=norm, aspect="equal")
 
-# Add colorbar
-cbar = fig.colorbar(im, ax=ax, shrink=0.8, pad=0.02)
-cbar.ax.tick_params(labelsize=16)
-cbar.set_label("Correlation Coefficient", fontsize=18)
+# Remove spines for a modern look
+for spine in ax.spines.values():
+    spine.set_visible(False)
+
+# Cell separation via minor-tick grid lines
+ax.set_xticks(np.arange(n + 1) - 0.5, minor=True)
+ax.set_yticks(np.arange(n + 1) - 0.5, minor=True)
+ax.grid(which="minor", color="white", linewidth=2)
+ax.tick_params(which="minor", bottom=False, left=False)
 
-# Set ticks and labels
+# Tick labels
 ax.set_xticks(np.arange(n))
 ax.set_yticks(np.arange(n))
-ax.set_xticklabels(categories, fontsize=16, rotation=45, ha="right")
-ax.set_yticklabels(categories, fontsize=16)
+ax.set_xticklabels(departments, fontsize=16, rotation=45, ha="right")
+ax.set_yticklabels(departments, fontsize=16)
+ax.tick_params(axis="both", length=0)
 
-# Add value annotations in cells
+# Colorbar
+cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, aspect=30)
+cbar.ax.tick_params(labelsize=16)
+cbar.set_label("Correlation Coefficient", fontsize=18, labelpad=12)
+cbar.outline.set_visible(False)
+
+# Cell annotations - adaptive color and emphasis on strong correlations
 for i in range(n):
     for j in range(n):
-        value = data[i, j]
-        # Use white text on dark cells, black on light cells
-        text_color = "white" if abs(value) > 0.5 else "black"
-        ax.text(j, i, f"{value:.2f}", ha="center", va="center", fontsize=14, color=text_color, fontweight="bold")
-
-# Labels and title
-ax.set_xlabel("Department", fontsize=20)
-ax.set_ylabel("Department", fontsize=20)
-ax.set_title("heatmap-basic · matplotlib · pyplots.ai", fontsize=24, pad=15)
+        val = data[i, j]
+        strong = abs(val) >= 0.6 and i != j
+        ax.text(
+            j,
+            i,
+            f"{val:.2f}",
+            ha="center",
+            va="center",
+            fontsize=16 if strong else 13,
+            fontweight="bold" if strong else "medium",
+            color="white" if abs(val) > 0.55 else "#333333",
+        )
+
+# Title and differentiated axis labels
+ax.set_title("heatmap-basic · matplotlib · pyplots.ai", fontsize=24, fontweight="medium", pad=20)
+ax.set_ylabel("Department (row)", fontsize=20, labelpad=12)
+ax.set_xlabel("Department (column)", fontsize=20, labelpad=12)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")