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

Comprehensive quality review improving code quality, data choice, visual design, spec compliance, and library feature usage.

Author: MarkusNeusinger

plots/violin-basic/implementations/matplotlib.py

@@ -1,62 +1,68 @@
 """ pyplots.ai
 violin-basic: Basic Violin 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: /100 | Updated: 2026-02-21
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 
 
-# Data - simulated test scores across different schools
+# Data - simulated test scores (0-100) across different schools
 np.random.seed(42)
 categories = ["School A", "School B", "School C", "School D"]
 data = [
-    np.random.normal(75, 10, 150),  # School A: centered around 75
-    np.random.normal(82, 8, 150),  # School B: higher scores, less spread
-    np.random.normal(68, 15, 150),  # School C: lower average, more spread
-    np.random.normal(78, 12, 150),  # School D: moderate
+    np.clip(np.random.normal(75, 10, 150), 0, 100),  # School A: centered around 75
+    np.clip(np.random.normal(85, 6, 150), 0, 100),  # School B: high scores, tight cluster
+    np.clip(np.random.normal(62, 15, 150), 0, 100),  # School C: lower average, wide spread
+    np.clip(
+        np.concatenate([np.random.normal(70, 5, 80), np.random.normal(88, 4, 70)]), 0, 100
+    ),  # School D: bimodal (two subgroups)
 ]
 
-# Create plot (4800x2700 px)
+# Plot
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Create violin plot with quartile markers
-parts = ax.violinplot(data, positions=range(len(categories)), showmeans=False, showmedians=True, showextrema=True)
+# Violin plot with built-in quartile lines and facecolor/linecolor params
+parts = ax.violinplot(
+    data,
+    positions=range(len(categories)),
+    quantiles=[[0.25, 0.5, 0.75]] * len(categories),
+    showmeans=False,
+    showmedians=False,
+    showextrema=False,
+    bw_method=0.3,
+    widths=0.75,
+)
 
-# Style the violins with Python Blue
-for pc in parts["bodies"]:
-    pc.set_facecolor("#306998")
+# Style violin bodies with shade intensity by median value
+medians = [np.median(d) for d in data]
+median_min, median_max = min(medians), max(medians)
+base_blue = np.array([0x30, 0x69, 0x98]) / 255  # Python Blue #306998
+
+for i, pc in enumerate(parts["bodies"]):
+    t = (medians[i] - median_min) / (median_max - median_min) if median_max > median_min else 0.5
+    pc.set_facecolor(base_blue * (0.6 + 0.4 * t))
     pc.set_edgecolor("#1e4a6e")
-    pc.set_alpha(0.7)
+    pc.set_alpha(0.75)
     pc.set_linewidth(2)
 
-# Style the lines (median, min, max)
-parts["cmedians"].set_color("#FFD43B")
-parts["cmedians"].set_linewidth(3)
-parts["cmins"].set_color("#1e4a6e")
-parts["cmins"].set_linewidth(2)
-parts["cmaxes"].set_color("#1e4a6e")
-parts["cmaxes"].set_linewidth(2)
-parts["cbars"].set_color("#1e4a6e")
-parts["cbars"].set_linewidth(2)
-
-# Add quartile markers (Q1 and Q3) as box indicators
-quartile1 = [np.percentile(d, 25) for d in data]
-quartile3 = [np.percentile(d, 75) for d in data]
-
-# Draw thin boxes for interquartile range
-for i, (q1, q3) in enumerate(zip(quartile1, quartile3, strict=True)):
-    ax.vlines(i, q1, q3, color="#1e4a6e", linewidth=6, zorder=3)
-
-# Labels and styling (scaled font sizes for 4800x2700)
+# Style quantile lines: white for Q1/Q3, yellow for median
+parts["cquantiles"].set_colors(["white", "#FFD43B", "white"] * len(categories))
+parts["cquantiles"].set_linewidths([2, 3.5, 2] * len(categories))
+
+# Labels and styling
 ax.set_xticks(range(len(categories)))
 ax.set_xticklabels(categories)
 ax.set_xlabel("School", fontsize=20)
 ax.set_ylabel("Test Score (points)", fontsize=20)
-ax.set_title("violin-basic · matplotlib · pyplots.ai", fontsize=24)
+ax.set_title("violin-basic \u00b7 matplotlib \u00b7 pyplots.ai", fontsize=24, fontweight="medium")
 ax.tick_params(axis="both", labelsize=16)
-ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# Spine removal and grid per library rules
+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")

plots/violin-basic/metadata/matplotlib.yaml

@@ -1,16 +1,16 @@
 library: matplotlib
 specification_id: violin-basic
 created: '2025-12-23T00:35:08Z'
-updated: '2025-12-23T00:38:15Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-21T22:25:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20447775895
 issue: 0
-python_version: 3.13.11
-library_version: 3.10.8
+python_version: '3.14.3'
+library_version: '3.10.8'
 preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-basic/matplotlib/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-basic/matplotlib/plot_thumb.png
 preview_html: null
-quality_score: 92
+quality_score: null
 impl_tags:
   dependencies: []
   techniques:

plots/violin-basic/specification.md

@@ -16,10 +16,10 @@ A violin plot combining a box plot with a kernel density estimation on each side
 - `category` (string) - group labels for comparison
 - `value` (numeric) - numerical values to plot
 - Size: 30-1000 points per category, 2-6 categories
+- Example: Test scores (50-100) across 4 class groups with distinct distribution shapes
 
 ## Notes
 
 - Show quartile markers inside the violin
 - Use mirrored density on both sides
 - Include median line
-- Consider split violins for comparing two conditions

plots/violin-basic/specification.yaml

@@ -6,7 +6,7 @@ title: Basic Violin Plot
 
 # Specification tracking
 created: 2025-12-14T09:45:06Z
-updated: 2025-12-14T09:45:06Z
+updated: 2026-02-21T12:00:00Z
 issue: 722
 suggested: MarkusNeusinger
 
@@ -16,6 +16,7 @@ tags:
     - violin
   data_type:
     - numeric
+    - continuous
     - categorical
   domain:
     - statistics
@@ -24,3 +25,4 @@ tags:
     - basic
     - distribution
     - density
+    - comparison