feat(pygal): implement violin-box (#2686)

## Implementation: `violin-box` - pygal

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595341267)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/violin-box/implementations/pygal.py

@@ -0,0 +1,178 @@
+""" pyplots.ai
+violin-box: Violin Plot with Embedded Box Plot
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 88/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Generate distributions for different categories with scores constrained to 0-100
+np.random.seed(42)
+raw_data = {
+    "Engineering": np.random.normal(75, 10, 200),
+    "Marketing": np.random.normal(62, 12, 200),
+    "Sales": np.random.normal(68, 14, 200),
+    "Operations": np.random.normal(55, 8, 200),
+}
+# Clip all values to 0-100 range
+data = {k: np.clip(v, 0, 100) for k, v in raw_data.items()}
+
+# Color palette: 4 violin colors + white for boxes + dark gray for whiskers/median/outliers
+# Pattern per category: violin, box, whisker*4, median, outliers = 8 series
+# 4 categories = 32 series, colors cycle through
+violin_colors = ["#306998", "#FFD43B", "#4CAF50", "#FF5722"]
+box_color = "#FFFFFF"  # White fill for box - improves internal contrast
+line_color = "#333333"  # Dark gray for whiskers, median, outliers
+
+# Build color sequence: for each violin, we need violin color, then white for box,
+# then dark gray for lines (whisker, cap, median, outliers)
+colors_list = []
+for vc in violin_colors:
+    colors_list.extend([vc, box_color, line_color, line_color, line_color, line_color, line_color, line_color])
+
+# Custom style for 4800x2700 px canvas
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=tuple(colors_list),
+    title_font_size=72,
+    label_font_size=48,
+    major_label_font_size=42,
+    legend_font_size=42,
+    value_font_size=36,
+    opacity=0.7,
+    opacity_hover=0.9,
+)
+
+# Create XY chart for violin plot with embedded box
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="violin-box · pygal · pyplots.ai",
+    x_title="Department",
+    y_title="Performance Score (0-100 scale)",
+    show_legend=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=4,
+    stroke=True,
+    fill=True,
+    dots_size=0,
+    show_x_guides=False,
+    show_y_guides=True,
+    range=(0, 110),
+    xrange=(0, 6),
+    margin=50,
+)
+
+# Parameters for violin shapes
+violin_width = 0.35
+n_points = 100
+
+# Box plot styling for stronger contrast
+box_stroke_style = {"width": 4, "dasharray": ""}
+median_stroke_style = {"width": 6, "dasharray": ""}
+whisker_stroke_style = {"width": 3, "dasharray": ""}
+
+# Track if legend entries have been added (only add once for first occurrence)
+added_box_legend = False
+added_median_legend = False
+added_outlier_legend = False
+
+# Add violins with embedded box plots for each category
+for i, (category, values) in enumerate(data.items()):
+    center_x = i + 1.5
+
+    # Compute KDE using Silverman's rule
+    n = len(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)
+
+    # Create range of y values for density
+    y_min, y_max = values.min(), values.max()
+    y_range = np.linspace(max(0, y_min - 5), min(100, y_max + 5), n_points)
+
+    # Gaussian kernel density estimation
+    density = np.zeros_like(y_range)
+    for v in values:
+        density += np.exp(-0.5 * ((y_range - v) / bandwidth) ** 2)
+    density /= n * bandwidth * np.sqrt(2 * np.pi)
+
+    # Normalize density to desired width
+    density = density / density.max() * violin_width
+
+    # Create violin shape (mirrored density)
+    left_points = [(center_x - d, y) for y, d in zip(y_range, density, strict=True)]
+    right_points = [(center_x + d, y) for y, d in zip(y_range[::-1], density[::-1], strict=True)]
+    violin_points = left_points + right_points + [left_points[0]]
+
+    chart.add(category, violin_points)
+
+    # Calculate box plot statistics
+    median = float(np.median(values))
+    q1 = float(np.percentile(values, 25))
+    q3 = float(np.percentile(values, 75))
+    iqr_val = q3 - q1
+
+    # Whiskers: 1.5 * IQR or data min/max
+    lower_whisker = max(values.min(), q1 - 1.5 * iqr_val)
+    upper_whisker = min(values.max(), q3 + 1.5 * iqr_val)
+
+    # Identify outliers
+    outliers = values[(values < lower_whisker) | (values > upper_whisker)]
+
+    box_width = 0.10
+
+    # Quartile box (IQR) - white filled for contrast
+    quartile_box = [
+        (center_x - box_width, q1),
+        (center_x - box_width, q3),
+        (center_x + box_width, q3),
+        (center_x + box_width, q1),
+        (center_x - box_width, q1),
+    ]
+    # Only add legend entry for box once
+    box_label = "Box (Q1-Q3)" if not added_box_legend else None
+    chart.add(box_label, quartile_box, stroke=True, fill=True, show_dots=False, stroke_style=box_stroke_style)
+    added_box_legend = True
+
+    # Whisker lines (vertical lines from box to whisker ends) - no legend
+    lower_whisker_line = [(center_x, q1), (center_x, lower_whisker)]
+    upper_whisker_line = [(center_x, q3), (center_x, upper_whisker)]
+    chart.add(None, lower_whisker_line, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
+    chart.add(None, upper_whisker_line, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
+
+    # Whisker caps (horizontal lines at ends) - no legend
+    cap_width = box_width * 0.8
+    lower_cap = [(center_x - cap_width, lower_whisker), (center_x + cap_width, lower_whisker)]
+    upper_cap = [(center_x - cap_width, upper_whisker), (center_x + cap_width, upper_whisker)]
+    chart.add(None, lower_cap, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
+    chart.add(None, upper_cap, stroke=True, fill=False, show_dots=False, stroke_style=whisker_stroke_style)
+
+    # Median line (thicker, contrasting)
+    median_line = [(center_x - box_width * 1.2, median), (center_x + box_width * 1.2, median)]
+    median_label = "Median" if not added_median_legend else None
+    chart.add(median_label, median_line, stroke=True, fill=False, show_dots=False, stroke_style=median_stroke_style)
+    added_median_legend = True
+
+    # Outliers as points - larger dots for better visibility
+    if len(outliers) > 0:
+        outlier_points = [(center_x, float(o)) for o in outliers]
+        outlier_label = "Outliers" if not added_outlier_legend else None
+        chart.add(outlier_label, outlier_points, stroke=False, fill=False, show_dots=True, dots_size=18)
+        added_outlier_legend = True
+
+# X-axis labels at violin positions
+chart.x_labels = ["", "Engineering", "Marketing", "Sales", "Operations", ""]
+chart.x_labels_major_count = 4
+
+# Save outputs
+chart.render_to_file("plot.html")
+chart.render_to_png("plot.png")

plots/violin-box/metadata/pygal.yaml

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+library: pygal
+specification_id: violin-box
+created: '2025-12-30T11:28:49Z'
+updated: '2025-12-30T11:54:03Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595341267
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-box/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-box/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/violin-box/pygal/plot.html
+quality_score: 88
+review:
+  strengths:
+  - Excellent manual KDE implementation using Gaussian kernel with Silverman's rule
+    for bandwidth selection
+  - Creative approach using XY chart to draw custom violin shapes since pygal lacks
+    native violin support
+  - Good visual distinction between categories with colorblind-safe palette
+  - 'Complete box plot components: median, quartiles, whiskers, and outliers all present'
+  - Proper legend entries distinguish between categories and statistical components
+  weaknesses:
+  - Y-axis range extends to 140 despite data being clipped to 0-100, wasting vertical
+    space
+  - Legend mixes category names with box plot element names, creating visual complexity