update(violin-basic): bokeh — comprehensive quality review (#4325)

## Summary

Updated **bokeh** implementation for **violin-basic**.

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

### Changes
- Improved data generation with distinct distribution shapes per
category
- Enhanced visual design (explicit font sizes, refined color palette,
layout balance)
- Fixed review weaknesses from previous evaluation
- Updated metadata with current library/Python versions
- Preview images uploaded to GCS staging

## 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/violin-basic/implementations/bokeh.py

@@ -1,28 +1,47 @@
 """ 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: 92/100 | Updated: 2026-02-21
 """
 
 import numpy as np
 from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, HoverTool, NumeralTickFormatter
 from bokeh.plotting import figure
+from scipy.stats import gaussian_kde
 
 
 # Data - Salary distributions by department (realistic scenario)
 np.random.seed(42)
 categories = ["Engineering", "Marketing", "Sales", "Support"]
-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
-}
 
-# Colors - Python Blue and Yellow first, then accessible colors
-colors = ["#306998", "#FFD43B", "#4B8BBE", "#FFE873"]
+# Engineering: normal, high mean — represents typical salaried professionals
+eng = np.random.normal(85000, 15000, 150)
 
-# Create figure with categorical x-axis
+# Marketing: normal, mid-range
+mkt = np.random.normal(65000, 12000, 150)
+
+# Sales: right-skewed — most earn base salary, some earn high commissions
+sales_base = np.random.exponential(15000, 150) + 45000
+sales = np.clip(sales_base, 30000, 150000)
+
+# Support: bimodal — junior vs senior tiers with distinct pay bands
+support_junior = np.random.normal(42000, 5000, 90)
+support_senior = np.random.normal(62000, 6000, 60)
+support = np.concatenate([support_junior, support_senior])
+
+data = {"Engineering": eng, "Marketing": mkt, "Sales": sales, "Support": support}
+
+# Colors - four distinct colorblind-safe hues
+colors = ["#306998", "#E8943A", "#2A9D8F", "#E76F6F"]
+
+# Visual hierarchy: emphasize non-normal distributions to guide the viewer
+alphas = [0.55, 0.55, 0.85, 0.85]
+
+# Distribution type labels for data storytelling
+dist_labels = ["normal", "normal", "right-skewed", "bimodal"]
+
+# Create figure with subtle warm background tint
 p = figure(
     width=4800,
     height=2700,
@@ -31,114 +50,159 @@
     y_axis_label="Annual Salary (USD)",
     x_range=categories,
     toolbar_location=None,
+    background_fill_color="#FAFAF8",
 )
 
-# Styling for 4800x2700 px
+# Title styling — lighter secondary color for visual weight
 p.title.text_font_size = "36pt"
+p.title.text_color = "#2D3436"
+p.title.text_font_style = "bold"
+
+# Text sizing for 4800x2700 px
 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"
+p.xaxis.axis_label_text_color = "#555555"
+p.yaxis.axis_label_text_color = "#555555"
+
+# Format y-axis as readable currency
+p.yaxis.formatter = NumeralTickFormatter(format="$0,0")
 
-# Grid styling
+# Visual refinement - clean, polished design
 p.xgrid.grid_line_color = None
-p.ygrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.15
 p.ygrid.grid_line_dash = "dashed"
-
-# Violin width scaling (0.4 = 40% of category spacing)
+p.ygrid.grid_line_color = "#B0B0B0"
+p.outline_line_color = None
+p.axis.minor_tick_line_color = None
+p.axis.major_tick_line_color = None
+p.axis.axis_line_color = "#D5D5D5"
+p.border_fill_color = "#FAFAF8"
+
+# Tighten y-axis to data range with room for annotations
+all_values = np.concatenate(list(data.values()))
+y_pad = (all_values.max() - all_values.min()) * 0.12
+p.y_range.start = all_values.min() - y_pad
+p.y_range.end = all_values.max() + y_pad
+
+# 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)
-    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)
+    # Compute KDE using scipy (idiomatic, robust bandwidth selection)
+    kde = gaussian_kde(values)
+    y_grid = np.linspace(values.min() - np.std(values) * 0.5, values.max() + np.std(values) * 0.5, 100)
+    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
+    # Draw violin patch via ColumnDataSource with varying alpha for hierarchy
+    violin_source = ColumnDataSource(data={"x": xs_left + xs_right, "y": list(y_grid) + list(y_grid[::-1])})
     p.patch(
-        xs_left + xs_right,
-        list(y_grid) + list(y_grid[::-1]),
+        x="x",
+        y="y",
+        source=violin_source,
         fill_color=colors[i],
-        fill_alpha=0.7,
+        fill_alpha=alphas[i],
         line_color=colors[i],
+        line_alpha=min(alphas[i] + 0.15, 1.0),
         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) with ColumnDataSource for HoverTool
     box_width = 0.06
-    p.quad(
-        left=[(cat, -box_width)],
-        right=[(cat, box_width)],
-        top=[q3],
-        bottom=[q1],
+    box_source = ColumnDataSource(
+        data={
+            "left": [(cat, -box_width)],
+            "right": [(cat, box_width)],
+            "top": [q3],
+            "bottom": [q1],
+            "dept": [cat],
+            "median_val": [f"${median:,.0f}"],
+            "q1_val": [f"${q1:,.0f}"],
+            "q3_val": [f"${q3:,.0f}"],
+            "n": [str(len(values))],
+        }
+    )
+    box_renderer = p.quad(
+        left="left",
+        right="right",
+        top="top",
+        bottom="bottom",
+        source=box_source,
         fill_color="white",
         fill_alpha=0.9,
         line_color="black",
         line_width=3,
     )
 
-    # Draw median line
-    p.segment(
-        x0=[(cat, -box_width * 1.5)],
-        y0=[median],
-        x1=[(cat, box_width * 1.5)],
-        y1=[median],
-        line_color="black",
-        line_width=5,
+    # Add HoverTool for interactive HTML output
+    hover = HoverTool(
+        renderers=[box_renderer],
+        tooltips=[
+            ("Department", "@dept"),
+            ("Median", "@median_val"),
+            ("Q1", "@q1_val"),
+            ("Q3", "@q3_val"),
+            ("N", "@n"),
+        ],
+    )
+    p.add_tools(hover)
+
+    # Median line
+    med_source = ColumnDataSource(
+        data={"x0": [(cat, -box_width * 1.5)], "y0": [median], "x1": [(cat, box_width * 1.5)], "y1": [median]}
     )
+    p.segment(x0="x0", y0="y0", x1="x1", y1="y1", source=med_source, line_color="black", 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)
+    whisker_source = ColumnDataSource(
+        data={"x0": [cat, cat], "y0": [q1, q3], "x1": [cat, cat], "y1": [whisker_low, whisker_high]}
+    )
+    p.segment(x0="x0", y0="y0", x1="x1", y1="y1", source=whisker_source, line_color="black", line_width=3)
 
     # Whisker caps
     cap_width = 0.04
-    p.segment(
-        x0=[(cat, -cap_width)],
-        y0=[whisker_low],
-        x1=[(cat, cap_width)],
-        y1=[whisker_low],
-        line_color="black",
-        line_width=3,
-    )
-    p.segment(
-        x0=[(cat, -cap_width)],
-        y0=[whisker_high],
-        x1=[(cat, cap_width)],
-        y1=[whisker_high],
-        line_color="black",
-        line_width=3,
+    cap_source = ColumnDataSource(
+        data={
+            "x0": [(cat, -cap_width), (cat, -cap_width)],
+            "y0": [whisker_low, whisker_high],
+            "x1": [(cat, cap_width), (cat, cap_width)],
+            "y1": [whisker_low, whisker_high],
+        }
     )
+    p.segment(x0="x0", y0="y0", x1="x1", y1="y1", source=cap_source, line_color="black", line_width=3)
+
+# Distribution type annotations — guide the viewer to the data story
+annotation_y = all_values.min() - y_pad * 0.65
+ann_source = ColumnDataSource(data={"x": categories, "y": [annotation_y] * len(categories), "text": dist_labels})
+p.text(
+    x="x",
+    y="y",
+    text="text",
+    source=ann_source,
+    text_font_size="18pt",
+    text_font_style="italic",
+    text_color="#999999",
+    text_align="center",
+    text_baseline="top",
+)
 
 # Save outputs
 export_png(p, filename="plot.png")