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

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

Author: MarkusNeusinger

plots/heatmap-basic/implementations/matplotlib.py

@@ -1,53 +1,71 @@
-""" pyplots.ai
+"""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: /100 | Updated: 2026-02-15
 """
 
 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 realistic patterns
+departments = ["Sales", "Marketing", "Support", "Dev", "HR", "Finance", "Ops", "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
+# Craft realistic correlations: related departments correlate more strongly
+data = np.array(
+    [
+        [1.00, 0.82, 0.35, 0.12, -0.05, 0.61, 0.44, 0.08],  # Sales
+        [0.82, 1.00, 0.28, 0.18, 0.10, 0.48, 0.30, 0.05],  # Marketing
+        [0.35, 0.28, 1.00, 0.55, 0.22, -0.10, 0.60, 0.15],  # Support
+        [0.12, 0.18, 0.55, 1.00, 0.08, -0.25, 0.42, -0.12],  # Dev
+        [-0.05, 0.10, 0.22, 0.08, 1.00, 0.30, 0.18, 0.52],  # HR
+        [0.61, 0.48, -0.10, -0.25, 0.30, 1.00, 0.35, 0.40],  # Finance
+        [0.44, 0.30, 0.60, 0.42, 0.18, 0.35, 1.00, 0.20],  # Ops
+        [0.08, 0.05, 0.15, -0.12, 0.52, 0.40, 0.20, 1.00],  # Legal
+    ]
+)
 
-# Create plot (3600x3600 px - square format best for heatmaps)
+# Plot (square format for matrix visualization)
 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 diverging colormap centered at zero
+norm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=1)
+im = ax.matshow(data, cmap="RdBu_r", norm=norm)
 
-# Add colorbar
-cbar = fig.colorbar(im, ax=ax, shrink=0.8, pad=0.02)
+# Colorbar
+cbar = fig.colorbar(im, ax=ax, shrink=0.78, pad=0.02, aspect=30)
 cbar.ax.tick_params(labelsize=16)
-cbar.set_label("Correlation Coefficient", fontsize=18)
+cbar.set_label("Correlation Coefficient", fontsize=18, labelpad=12)
+cbar.outline.set_visible(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="left")
+ax.set_yticklabels(departments, fontsize=16)
+ax.xaxis.set_ticks_position("bottom")
+ax.tick_params(axis="both", length=0)
 
-# Add value annotations in cells
+# Cell value annotations with adaptive text color
 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"
+        text_color = "white" if abs(value) > 0.55 else "black"
         ax.text(j, i, f"{value:.2f}", ha="center", va="center", fontsize=14, color=text_color, fontweight="bold")
 
+# Subtle cell borders
+for i in range(n + 1):
+    ax.axhline(i - 0.5, color="white", linewidth=1.5)
+    ax.axvline(i - 0.5, color="white", linewidth=1.5)
+
 # 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)
+ax.set_xlabel("Department", fontsize=20, labelpad=12)
+ax.set_ylabel("Department", fontsize=20, labelpad=12)
+ax.set_title(
+    "Department Correlation · heatmap-basic · matplotlib · pyplots.ai", fontsize=24, fontweight="medium", pad=15
+)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/heatmap-basic/metadata/matplotlib.yaml

@@ -1,16 +1,16 @@
 library: matplotlib
 specification_id: heatmap-basic
 created: '2025-12-23T00:46:03Z'
-updated: '2025-12-23T00:49:08Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-15T21:25:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20447951294
 issue: 0
-python_version: 3.13.11
+python_version: 3.14.3
 library_version: 3.10.8
 preview_url: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/matplotlib/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/heatmap-basic/matplotlib/plot_thumb.png
 preview_html: null
-quality_score: 94
+quality_score: null
 impl_tags:
   dependencies: []
   techniques:

plots/heatmap-basic/specification.md

@@ -23,4 +23,4 @@ A heatmap displaying values in a matrix format using color intensity. Each cell'
 - Use a diverging colormap for data with positive/negative values
 - Add value annotations in cells when readable
 - Include a colorbar legend
-- Consider clustering rows/columns for better pattern visibility
+- Order rows/columns logically (alphabetical, by magnitude, or by similarity)

plots/heatmap-basic/specification.yaml

@@ -6,7 +6,7 @@ title: Basic Heatmap
 
 # Specification tracking
 created: 2025-12-14T09:02:34Z
-updated: 2025-12-14T09:02:34Z
+updated: 2026-02-15T12:00:00Z
 issue: 691
 suggested: MarkusNeusinger
 
@@ -18,6 +18,7 @@ tags:
   data_type:
     - numeric
     - categorical
+    - matrix
   domain:
     - statistics
     - general