feat(pygal): implement scatter-marginal (#6129)

## Implementation: `scatter-marginal` - python/pygal

Implements the **python/pygal** version of `scatter-marginal`.

**File:** `plots/scatter-marginal/implementations/python/pygal.py`

**Parent Issue:** #2005

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25592866414)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-marginal/implementations/python/pygal.py

@@ -1,17 +1,30 @@
-""" pyplots.ai
+""" anyplot.ai
 scatter-marginal: Scatter Plot with Marginal Distributions
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 88/100 | Created: 2025-12-26
+Library: pygal 3.1.0 | Python 3.13.13
+Quality: 90/100 | Updated: 2026-05-09
 """
 
 import io
+import os
 
 import numpy as np
 import pygal
 from PIL import Image, ImageDraw, ImageFont
 from pygal.style import Style
 
 
+THEME = os.getenv("ANYPLOT_THEME", "light")
+
+# Theme-adaptive colors from default-style-guide.md
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
+
+# Okabe-Ito palette
+BRAND = "#009E73"  # First categorical series
+SECONDARY = "#D55E00"  # For marginals if colored
+
 # Data - correlated bivariate data with realistic measurement context
 np.random.seed(42)
 n_points = 150
@@ -22,68 +35,60 @@
 correlation = np.corrcoef(x, y)[0, 1]
 
 # Calculate histogram data for marginals
-# Use same number of bins and explicit ranges for better alignment
 n_bins = 10
 x_min, x_max = np.floor(x.min() / 5) * 5, np.ceil(x.max() / 5) * 5
 y_min, y_max = np.floor(y.min() / 5) * 5, np.ceil(y.max() / 5) * 5
 
 x_hist, x_edges = np.histogram(x, bins=n_bins, range=(x_min, x_max))
 y_hist, y_edges = np.histogram(y, bins=n_bins, range=(y_min, y_max))
 
-# Dimensions for layout - optimized spacing
+# Dimensions for layout
 total_width = 4800
 total_height = 2700
-margin_plot_size = 450  # Slightly smaller marginals
+margin_plot_size = 450
 title_height = 100
 gap = 15
-corner_size = margin_plot_size  # Size for corner annotation
 
-# Calculate main scatter dimensions
 scatter_width = total_width - margin_plot_size - gap * 3
 scatter_height = total_height - margin_plot_size - title_height - gap * 3
 
-# Shared margins for alignment
 left_margin = 100
 bottom_margin = 80
 top_margin = 20
 right_margin = 20
 
-# Custom style for main scatter
+# Custom style for main scatter - theme-adaptive
 scatter_style = Style(
-    background="#ffffff",
-    plot_background="#fafafa",
-    foreground="#333333",
-    foreground_strong="#333333",
-    foreground_subtle="#666666",
-    colors=("#306998",),
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_MUTED,
+    colors=(BRAND,),  # Okabe-Ito first series
     title_font_size=48,
     label_font_size=36,
     major_label_font_size=32,
     legend_font_size=32,
-    tooltip_font_size=24,
     opacity=0.65,
     opacity_hover=0.9,
-    guide_stroke_color="#e0e0e0",
-    major_guide_stroke_color="#cccccc",
 )
 
-# Custom style for marginal histograms - subtle color to not distract from main scatter
+# Custom style for marginal histograms - theme-adaptive, subtle color
 marginal_style = Style(
-    background="#ffffff",
-    plot_background="#f8f8f8",
-    foreground="#333333",
-    foreground_strong="#333333",
-    foreground_subtle="#666666",
-    colors=("#a8c5db",),  # Subtle, lighter blue for marginals
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_MUTED,
+    colors=(INK_SOFT,),  # Subtle gray for marginals
     title_font_size=32,
     label_font_size=32,
     major_label_font_size=30,
     legend_font_size=28,
-    opacity=0.6,  # More transparency for subtle appearance
-    guide_stroke_color="#e0e0e0",
+    opacity=0.6,
 )
 
-# Create main scatter plot with explicit axis ranges matching marginal histograms
+# Create main scatter plot
 scatter = pygal.XY(
     width=scatter_width,
     height=scatter_height,
@@ -102,22 +107,20 @@
     margin_right=right_margin,
     margin_bottom=bottom_margin,
     margin_left=left_margin,
-    range=(y_min - 5, y_max + 5),  # Y range with slight padding
-    xrange=(x_min - 5, x_max + 5),  # X range with slight padding
+    range=(y_min - 5, y_max + 5),
+    xrange=(x_min - 5, x_max + 5),
 )
 
-# Add scatter data
 scatter_points = [(float(xi), float(yi)) for xi, yi in zip(x, y, strict=True)]
 scatter.add("Data", scatter_points)
 
 # Create top marginal histogram (X distribution)
-# Use bar chart with x_labels matching scatter X range bins
 x_margin = pygal.Bar(
     width=scatter_width,
     height=margin_plot_size,
     style=marginal_style,
     show_legend=False,
-    show_x_labels=False,  # Hide to avoid clutter
+    show_x_labels=False,
     show_y_labels=True,
     show_y_guides=True,
     show_x_guides=False,
@@ -126,12 +129,11 @@
     margin_bottom=20,
     margin_left=left_margin,
     explicit_size=True,
-    spacing=2,  # Tighter spacing for better visual continuity
+    spacing=2,
 )
 x_margin.add("X Distribution", [float(h) for h in x_hist])
 
-# Create right marginal histogram (Y distribution) - horizontal bars
-# Use same margin settings as scatter for better alignment
+# Create right marginal histogram (Y distribution)
 y_margin = pygal.HorizontalBar(
     width=margin_plot_size,
     height=scatter_height,
@@ -143,12 +145,11 @@
     show_x_guides=False,
     margin_top=top_margin,
     margin_right=30,
-    margin_bottom=bottom_margin,  # Match scatter bottom margin
+    margin_bottom=bottom_margin,
     margin_left=10,
     explicit_size=True,
-    spacing=2,  # Tighter spacing matching X marginal
+    spacing=2,
 )
-# Reverse order to match scatter Y axis orientation (pygal HorizontalBar goes top-to-bottom)
 y_margin.add("Y Distribution", [float(h) for h in y_hist[::-1]])
 
 # Render each chart to PNG in memory
@@ -161,10 +162,10 @@
 x_margin_img = Image.open(io.BytesIO(x_margin_png))
 y_margin_img = Image.open(io.BytesIO(y_margin_png))
 
-# Create final composite image
-final_img = Image.new("RGB", (total_width, total_height), "white")
+# Create final composite image with theme-adaptive background
+final_img = Image.new("RGB", (total_width, total_height), PAGE_BG)
 
-# Calculate positions - aligned for better visual coherence
+# Calculate positions
 scatter_x = gap
 scatter_y = title_height + margin_plot_size + gap
 x_margin_x = gap
@@ -179,7 +180,7 @@
 
 # Add title and corner annotation
 draw = ImageDraw.Draw(final_img)
-title_text = "scatter-marginal · pygal · pyplots.ai"
+title_text = "scatter-marginal · pygal · anyplot.ai"
 try:
     title_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 60)
     stats_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 36)
@@ -194,33 +195,35 @@
 text_width = bbox[2] - bbox[0]
 text_x = (total_width - text_width) // 2
 text_y = 30
-draw.text((text_x, text_y), title_text, fill="#333333", font=title_font)
+draw.text((text_x, text_y), title_text, fill=INK, font=title_font)
 
 # Add statistics in the corner space (top-right empty area)
-# This corner is at: x = right of top marginal, y = below title and above right marginal
-corner_x = y_margin_x + 30  # Right side where y_margin is
-corner_y = title_height + 30  # Just below title area
+corner_x = y_margin_x + 30
+corner_y = title_height + 30
 corner_width = margin_plot_size - 60
 corner_height = margin_plot_size - 80
 
-# Draw subtle background for stats box
+# Draw subtle background for stats box with theme-adaptive colors
+elevated_bg = "#FFFDF6" if THEME == "light" else "#242420"
+box_border = INK_MUTED
+
 stats_box = [(corner_x, corner_y), (corner_x + corner_width, corner_y + corner_height)]
-draw.rounded_rectangle(stats_box, radius=15, fill="#f5f5f5", outline="#c0c0c0", width=2)
+draw.rounded_rectangle(stats_box, radius=15, fill=elevated_bg, outline=box_border, width=2)
 
-# Add statistics text - centered in box
+# Add statistics text
 stats_title = "Summary"
-draw.text((corner_x + 35, corner_y + 25), stats_title, fill="#333333", font=stats_font_bold)
+draw.text((corner_x + 35, corner_y + 25), stats_title, fill=INK, font=stats_font_bold)
 
 stats_lines = [f"n = {n_points}", f"r = {correlation:.3f}", f"A̅ = {np.mean(x):.1f}", f"B̅ = {np.mean(y):.1f}"]
 line_y = corner_y + 85
 for line in stats_lines:
-    draw.text((corner_x + 35, line_y), line, fill="#555555", font=stats_font)
+    draw.text((corner_x + 35, line_y), line, fill=INK_SOFT, font=stats_font)
     line_y += 50
 
-# Save final image
-final_img.save("plot.png", "PNG")
+# Save final image and HTML
+final_img.save(f"plot-{THEME}.png", "PNG")
 
 # Also save the scatter SVG as HTML for interactivity
 scatter_svg_full = scatter.render().decode("utf-8")
-with open("plot.html", "w", encoding="utf-8") as f:
+with open(f"plot-{THEME}.html", "w", encoding="utf-8") as f:
     f.write(scatter_svg_full)