feat(seaborn): implement heatmap-basic (#1379)

## Implementation: `heatmap-basic` - seaborn

Implements the **seaborn** version of `heatmap-basic`.

**File:** `plots/heatmap-basic/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20447949053)*

---------

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: github-actions[bot]

plots/heatmap-basic/implementations/seaborn.py

@@ -1,7 +1,7 @@
 """ pyplots.ai
 heatmap-basic: Basic Heatmap
 Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 94/100 | Created: 2025-12-14
+Quality: 93/100 | Created: 2025-12-23
 """
 
 import matplotlib.pyplot as plt
@@ -14,15 +14,18 @@
 departments = ["Sales", "Marketing", "Engineering", "Support", "Finance", "HR", "Operations"]
 months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
 
-# Generate performance data (values ranging from -50 to 100)
-data = np.random.randn(len(departments), len(months)) * 30 + 50
-# Add some structure to make patterns visible
-data[0, :6] += 20  # Sales good first half
-data[2, 6:] += 25  # Engineering good second half
-data[4, :] = data[4, :] * 0.5 + 60  # Finance stable
-
-# Plot
-fig, ax = plt.subplots(figsize=(16, 9))
+# Generate performance data (values 0-100)
+data = np.random.randn(len(departments), len(months)) * 20 + 50
+# Add patterns to demonstrate heatmap capabilities
+data[0, :6] += 20  # Sales strong first half
+data[2, 6:] += 25  # Engineering strong second half
+data[4, :] = data[4, :] * 0.3 + 70  # Finance consistently stable
+data[5, 3:9] -= 15  # HR dip mid-year
+# Clip to valid performance range
+data = np.clip(data, 5, 95)
+
+# Plot - using square format for heatmap
+fig, ax = plt.subplots(figsize=(12, 12))
 sns.heatmap(
     data,
     annot=True,
@@ -31,23 +34,25 @@
     center=50,
     xticklabels=months,
     yticklabels=departments,
-    linewidths=0.5,
+    linewidths=1,
     linecolor="white",
-    cbar_kws={"label": "Performance Score", "shrink": 0.8},
-    annot_kws={"fontsize": 14},
+    cbar_kws={"label": "Performance Score", "shrink": 0.75},
+    annot_kws={"fontsize": 16},
     ax=ax,
+    vmin=0,
+    vmax=100,
 )
 
 # Style
 ax.set_xlabel("Month", fontsize=20)
 ax.set_ylabel("Department", fontsize=20)
-ax.set_title("heatmap-basic · seaborn · pyplots.ai", fontsize=24)
+ax.set_title("heatmap-basic · seaborn · pyplots.ai", fontsize=24, pad=20)
 ax.tick_params(axis="both", labelsize=16)
 
 # Adjust colorbar label size
 cbar = ax.collections[0].colorbar
 cbar.ax.tick_params(labelsize=14)
-cbar.ax.set_ylabel("Performance Score", fontsize=16)
+cbar.ax.set_ylabel("Performance Score", fontsize=18)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/heatmap-basic/metadata/seaborn.yaml

@@ -1,17 +1,30 @@
 library: seaborn
 specification_id: heatmap-basic
-created: 2025-12-14 09:11:58+00:00
-updated: 2025-12-14 09:11:58+00:00
+created: '2025-12-23T00:46:00Z'
+updated: '2025-12-23T00:49:07Z'
 generated_by: claude-opus-4-5-20251101
-workflow_run: 20205724549
-issue: 691
+workflow_run: 20447949053
+issue: 0
 python_version: 3.13.11
 library_version: 0.13.2
 preview_url: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/seaborn/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/seaborn/plot_thumb.png
 preview_html: null
-quality_score: 94
+quality_score: 93
 review:
-  strengths: []
-  weaknesses: []
-  improvements: []
+  strengths:
+  - Excellent use of diverging RdBu colormap centered at 50, making it easy to distinguish
+    above/below average performance
+  - Data patterns are intentionally crafted to demonstrate heatmap capabilities (Sales
+    strong first half, Engineering strong second half, Finance consistently stable,
+    HR mid-year dip)
+  - Cell annotations with appropriate font size (16pt) make exact values readable
+    without cluttering
+  - Square figure format (12×12) is perfect for this 7×12 matrix, allowing cells to
+    be nearly square
+  - White gridlines with linewidth=1 provide clear cell separation without being distracting
+  - Colorbar properly configured with shrink, label, and tick formatting
+  weaknesses:
+  - Could use sns.clustermap or add dendrograms to showcase seaborn clustering capabilities
+    as mentioned in spec notes
+  - Colorbar label is set twice (in cbar_kws and manually), which is redundant code