update(violin-basic): bokeh — 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/bokeh.py

@@ -1,12 +1,14 @@
 """ pyplots.ai
 violin-basic: Basic Violin Plot
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: bokeh 3.8.2 | Python 3.14.3
+Quality: /100 | Updated: 2026-02-21
 """
 
 import numpy as np
 from bokeh.io import export_png, output_file, save
+from bokeh.models import NumeralTickFormatter
 from bokeh.plotting import figure
+from scipy.stats import gaussian_kde
 
 
 # Data - Salary distributions by department (realistic scenario)
@@ -15,66 +17,62 @@
 data = {
     "Engineering": np.random.normal(85000, 15000, 150),
     "Marketing": np.random.normal(65000, 12000, 150),
-    "Sales": np.random.normal(70000, 20000, 150),  # Higher variance
-    "Support": np.random.normal(50000, 8000, 150),  # Lower variance
+    "Sales": np.random.normal(70000, 20000, 150),
+    "Support": np.random.normal(50000, 8000, 150),
 }
 
-# Colors - Python Blue and Yellow first, then accessible colors
+# Colors - Python Blue first, then accessible palette
 colors = ["#306998", "#FFD43B", "#4B8BBE", "#FFE873"]
 
-# Create figure with categorical x-axis
+# Create figure
 p = figure(
     width=4800,
     height=2700,
-    title="violin-basic · bokeh · pyplots.ai",
+    title="violin-basic \u00b7 bokeh \u00b7 pyplots.ai",
     x_axis_label="Department",
     y_axis_label="Annual Salary (USD)",
     x_range=categories,
     toolbar_location=None,
 )
 
-# Styling for 4800x2700 px
+# Text sizing for 4800x2700 px
 p.title.text_font_size = "36pt"
 p.xaxis.axis_label_text_font_size = "28pt"
 p.yaxis.axis_label_text_font_size = "28pt"
 p.xaxis.major_label_text_font_size = "22pt"
 p.yaxis.major_label_text_font_size = "22pt"
 
-# Grid styling
+# Format y-axis as readable currency
+p.yaxis.formatter = NumeralTickFormatter(format="$0,0")
+
+# Visual refinement - clean design
 p.xgrid.grid_line_color = None
-p.ygrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.2
 p.ygrid.grid_line_dash = "dashed"
+p.outline_line_color = None
+p.axis.minor_tick_line_color = None
+p.axis.major_tick_line_color = None
+p.axis.axis_line_color = "#cccccc"
 
-# Violin width scaling (0.4 = 40% of category spacing)
+# Violin width scaling
 violin_width = 0.4
 
 # Draw violins for each category
 for i, cat in enumerate(categories):
     values = data[cat]
-    n = len(values)
 
-    # Compute KDE using Gaussian kernel (Silverman's rule for bandwidth)
+    # Compute KDE using scipy (idiomatic, robust bandwidth selection)
+    kde = gaussian_kde(values)
     std = np.std(values)
-    iqr = np.percentile(values, 75) - np.percentile(values, 25)
-    bandwidth = 0.9 * min(std, iqr / 1.34) * n ** (-0.2)
-    bandwidth = max(bandwidth, 0.1)
-
     y_grid = np.linspace(values.min() - std, values.max() + std, 100)
-    density = np.zeros_like(y_grid, dtype=float)
-    for xi in values:
-        density += np.exp(-0.5 * ((y_grid - xi) / bandwidth) ** 2)
-    density /= n * bandwidth * np.sqrt(2 * np.pi)
+    density = kde(y_grid)
 
     # Scale density to violin width
     density_scaled = density / density.max() * violin_width
 
-    # Create violin shape (mirrored on both sides)
-    x_left = -density_scaled
-    x_right = density_scaled
-
-    # Convert to categorical offset format for bokeh
-    xs_left = [(cat, float(xl)) for xl in x_left]
-    xs_right = [(cat, float(xr)) for xr in x_right[::-1]]
+    # Create mirrored violin shape using categorical offset tuples
+    xs_left = [(cat, float(-d)) for d in density_scaled]
+    xs_right = [(cat, float(d)) for d in density_scaled[::-1]]
 
     # Draw violin patch
     p.patch(
@@ -86,10 +84,10 @@
         line_width=3,
     )
 
-    # Compute quartiles
+    # Quartiles and median
     q1, median, q3 = np.percentile(values, [25, 50, 75])
 
-    # Draw thin box inside violin (quartile markers)
+    # Inner box (Q1-Q3)
     box_width = 0.06
     p.quad(
         left=[(cat, -box_width)],
@@ -102,7 +100,7 @@
         line_width=3,
     )
 
-    # Draw median line
+    # Median line
     p.segment(
         x0=[(cat, -box_width * 1.5)],
         y0=[median],
@@ -112,12 +110,11 @@
         line_width=5,
     )
 
-    # Whiskers (to 1.5*IQR or data extent)
+    # Whiskers (1.5*IQR or data extent)
     iqr_val = q3 - q1
     whisker_low = max(values.min(), q1 - 1.5 * iqr_val)
     whisker_high = min(values.max(), q3 + 1.5 * iqr_val)
 
-    # Vertical whisker lines
     p.segment(x0=[cat], y0=[q1], x1=[cat], y1=[whisker_low], line_color="black", line_width=3)
     p.segment(x0=[cat], y0=[q3], x1=[cat], y1=[whisker_high], line_color="black", line_width=3)
 

plots/violin-basic/metadata/bokeh.yaml

@@ -1,16 +1,16 @@
 library: bokeh
 specification_id: violin-basic
 created: '2025-12-23T00:36:10Z'
-updated: '2025-12-23T00:39:05Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-21T22:25:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20447780513
 issue: 0
-python_version: 3.13.11
-library_version: 3.8.1
+python_version: '3.14.3'
+library_version: '3.8.2'
 preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-basic/bokeh/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-basic/bokeh/plot_thumb.png
 preview_html: https://storage.googleapis.com/pyplots-images/plots/violin-basic/bokeh/plot.html
-quality_score: 91
+quality_score: null
 impl_tags:
   dependencies: []
   techniques: