feat(bokeh): implement violin-box (#2683)

## Implementation: `violin-box` - bokeh

Implements the **bokeh** version of `violin-box`.

**File:** `plots/violin-box/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595338446)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Author: github-actions[bot]

plots/violin-box/implementations/bokeh.py

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+""" pyplots.ai
+violin-box: Violin Plot with Embedded Box Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Legend, LegendItem
+from bokeh.plotting import figure
+
+
+# Data - Test scores by study method (educational context)
+np.random.seed(42)
+categories = ["Method A", "Method B", "Method C", "Method D"]
+
+# Create distributions with different characteristics to showcase features
+data = {
+    "Method A": np.random.normal(72, 12, 120),  # Standard distribution
+    "Method B": np.concatenate(
+        [
+            np.random.normal(78, 8, 100),  # Main group
+            np.array([48, 52, 95, 98]),  # Outliers both ends
+        ]
+    ),
+    "Method C": np.random.normal(65, 15, 120),  # Wider spread
+    "Method D": np.concatenate(
+        [
+            np.random.normal(80, 6, 90),  # Tight main group
+            np.random.normal(55, 3, 20),  # Bimodal lower group
+            np.array([38, 40, 42]),  # Low outliers
+        ]
+    ),
+}
+
+# Colors - Python Blue for violin, golden yellow for box
+violin_color = "#306998"
+box_color = "#FFD43B"
+
+# Create figure with categorical x-axis
+p = figure(
+    width=4800,
+    height=2700,
+    title="violin-box \u00b7 bokeh \u00b7 pyplots.ai",
+    x_axis_label="Study Method",
+    y_axis_label="Test Score",
+    x_range=categories,
+    toolbar_location=None,
+)
+
+# Styling 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 - visible but subtle
+p.xgrid.grid_line_color = None
+p.ygrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_dash = "dashed"
+
+# Background styling
+p.background_fill_color = None
+p.border_fill_color = None
+
+# Violin width scaling (0.4 = 40% of category spacing)
+violin_width = 0.38
+
+# Track renderers for legend
+violin_renderer = None
+box_renderer = None
+median_renderer = None
+outlier_renderer = None
+
+# Collect outlier data for all categories
+all_outliers_x = []
+all_outliers_y = []
+
+# Draw violins with embedded box plots for each category
+for cat in categories:
+    values = np.array(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)
+
+    # 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]]
+
+    # Draw violin patch
+    vr = p.patch(
+        xs_left + xs_right,
+        list(y_grid) + list(y_grid[::-1]),
+        fill_color=violin_color,
+        fill_alpha=0.6,
+        line_color=violin_color,
+        line_width=3,
+    )
+    if violin_renderer is None:
+        violin_renderer = vr
+
+    # Compute box plot statistics
+    q1 = np.percentile(values, 25)
+    median = np.percentile(values, 50)
+    q3 = np.percentile(values, 75)
+    iqr_val = q3 - q1
+    whisker_low = max(values.min(), q1 - 1.5 * iqr_val)
+    whisker_high = min(values.max(), q3 + 1.5 * iqr_val)
+
+    # Draw box inside violin (IQR from Q1 to Q3)
+    box_width = 0.08
+    br = p.quad(
+        left=[(cat, -box_width)],
+        right=[(cat, box_width)],
+        top=[q3],
+        bottom=[q1],
+        fill_color=box_color,
+        fill_alpha=0.9,
+        line_color="black",
+        line_width=3,
+    )
+    if box_renderer is None:
+        box_renderer = br
+
+    # Draw median line
+    mr = p.segment(
+        x0=[(cat, -box_width * 1.3)],
+        y0=[median],
+        x1=[(cat, box_width * 1.3)],
+        y1=[median],
+        line_color="black",
+        line_width=5,
+    )
+    if median_renderer is None:
+        median_renderer = mr
+
+    # Whiskers (vertical lines from box to whisker limits)
+    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 caps
+    cap_width = 0.05
+    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,
+    )
+
+    # Collect outliers
+    outliers = values[(values < whisker_low) | (values > whisker_high)]
+    for out in outliers:
+        all_outliers_x.append(cat)
+        all_outliers_y.append(out)
+
+# Draw all outliers
+if len(all_outliers_x) > 0:
+    outlier_source = ColumnDataSource(data={"x": all_outliers_x, "y": all_outliers_y})
+    outlier_renderer = p.scatter(
+        x="x",
+        y="y",
+        source=outlier_source,
+        size=18,
+        fill_color="white",
+        line_color="black",
+        line_width=3,
+        marker="circle",
+    )
+
+# Create legend
+legend_items = [
+    LegendItem(label="Distribution (KDE)", renderers=[violin_renderer]),
+    LegendItem(label="IQR (Q1-Q3)", renderers=[box_renderer]),
+    LegendItem(label="Median", renderers=[median_renderer]),
+]
+if outlier_renderer is not None:
+    legend_items.append(LegendItem(label="Outliers", renderers=[outlier_renderer]))
+
+legend = Legend(items=legend_items, location="top_right")
+legend.label_text_font_size = "20pt"
+legend.glyph_height = 30
+legend.glyph_width = 30
+legend.spacing = 15
+legend.padding = 20
+legend.background_fill_alpha = 0.8
+p.add_layout(legend, "right")
+
+# Save outputs
+export_png(p, filename="plot.png")
+output_file("plot.html")
+save(p)

plots/violin-box/metadata/bokeh.yaml

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+library: bokeh
+specification_id: violin-box
+created: '2025-12-30T11:26:52Z'
+updated: '2025-12-30T11:55:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595338446
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-box/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-box/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/violin-box/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent manual implementation of violin plot using KDE calculation and patch
+    rendering
+  - Clean color scheme with Python Blue violins and golden yellow boxes provides excellent
+    visual distinction
+  - Proper legend with all four components clearly identified
+  - Good data variety showing different distribution shapes (normal, bimodal, with
+    outliers)
+  - Correct title format following pyplots.ai conventions
+  - Appropriate font sizing for 4800x2700 canvas
+  weaknesses:
+  - Legend is placed outside the plot area creating slight visual separation from
+    the main plot
+  - Outlier markers could be slightly larger for better visibility at full resolution
+  - Grid/legend positioning could be improved by placing legend inside the plot area