feat(pygal): implement scatter-matrix (#2269)

## Implementation: `scatter-matrix` - pygal

Implements the **pygal** version of `scatter-matrix`.

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20525448000)*

---------

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/scatter-matrix/implementations/pygal.py

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+""" pyplots.ai
+scatter-matrix: Scatter Plot Matrix
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-26
+"""
+
+from io import BytesIO
+
+import cairosvg
+import numpy as np
+import pygal
+from PIL import Image, ImageDraw, ImageFont
+from pygal.style import Style
+
+
+# Data - Iris-like dataset with 4 variables
+np.random.seed(42)
+n_samples = 100
+
+# Create correlated multivariate data with both positive and negative correlations
+base = np.random.randn(n_samples)
+sepal_length = 5.8 + base * 0.8 + np.random.randn(n_samples) * 0.3
+sepal_width = 3.0 - base * 0.4 + np.random.randn(n_samples) * 0.25  # Negative correlation with sepal_length
+petal_length = 3.8 + base * 1.5 + np.random.randn(n_samples) * 0.4
+petal_width = 1.2 + base * 0.6 + np.random.randn(n_samples) * 0.2
+
+variables = {
+    "Sepal Length": sepal_length,
+    "Sepal Width": sepal_width,
+    "Petal Length": petal_length,
+    "Petal Width": petal_width,
+}
+var_names = list(variables.keys())
+n_vars = len(var_names)
+
+# Style configuration with improved transparency for overlapping points
+custom_style = Style(
+    background="white",
+    plot_background="#f8f8f8",
+    foreground="#333",
+    foreground_strong="#333",
+    foreground_subtle="#666",
+    colors=("#306998", "#FFD43B", "#4B8BBE", "#FFE873"),
+    title_font_size=28,
+    label_font_size=18,
+    major_label_font_size=16,
+    legend_font_size=16,
+    value_font_size=14,
+    opacity=0.55,
+    opacity_hover=0.85,
+)
+
+# Canvas dimensions
+total_width = 3600
+total_height = 3600
+margin_top = 120
+margin_bottom = 120
+margin_left = 120
+margin_right = 50
+plot_area_width = total_width - margin_left - margin_right
+plot_area_height = total_height - margin_top - margin_bottom
+cell_size = min(plot_area_width, plot_area_height) // n_vars
+gap = 10
+
+# Create composite image
+composite = Image.new("RGB", (total_width, total_height), "white")
+
+# Render each cell chart and paste into composite
+for i in range(n_vars):
+    for j in range(n_vars):
+        x_pos = margin_left + j * cell_size + gap // 2
+        y_pos = margin_top + i * cell_size + gap // 2
+        inner_size = cell_size - gap
+
+        var_x = var_names[j]
+        var_y = var_names[i]
+
+        if i == j:
+            # Diagonal: Histogram
+            chart = pygal.Histogram(
+                width=inner_size,
+                height=inner_size,
+                style=custom_style,
+                show_legend=False,
+                show_x_labels=(i == n_vars - 1),
+                show_y_labels=(j == 0),
+                x_label_rotation=0,
+                show_minor_x_labels=False,
+                show_minor_y_labels=False,
+                margin_top=8,
+                margin_right=8,
+                margin_bottom=40 if i == n_vars - 1 else 8,
+                margin_left=70 if j == 0 else 8,
+                spacing=0,
+                truncate_label=-1,
+            )
+
+            # Create histogram data
+            data = variables[var_x]
+            hist, bin_edges = np.histogram(data, bins=12)
+            hist_data = [(float(bin_edges[k]), float(bin_edges[k + 1]), float(hist[k])) for k in range(len(hist))]
+            chart.add(var_x, hist_data)
+        else:
+            # Off-diagonal: Scatter plot with smaller dots and better transparency
+            chart = pygal.XY(
+                width=inner_size,
+                height=inner_size,
+                style=custom_style,
+                show_legend=False,
+                show_x_labels=(i == n_vars - 1),
+                show_y_labels=(j == 0),
+                x_label_rotation=0,
+                show_minor_x_labels=False,
+                show_minor_y_labels=False,
+                margin_top=8,
+                margin_right=8,
+                margin_bottom=40 if i == n_vars - 1 else 8,
+                margin_left=70 if j == 0 else 8,
+                dots_size=7,
+                stroke=False,
+                truncate_label=-1,
+            )
+
+            # Scatter data as (x, y) tuples
+            x_data = variables[var_x]
+            y_data = variables[var_y]
+            scatter_data = [(float(x_data[k]), float(y_data[k])) for k in range(len(x_data))]
+            chart.add("Data", scatter_data)
+
+        # Render chart to PNG bytes
+        svg_bytes = chart.render()
+        png_bytes = cairosvg.svg2png(bytestring=svg_bytes, output_width=inner_size, output_height=inner_size)
+        cell_image = Image.open(BytesIO(png_bytes))
+
+        # Paste into composite
+        composite.paste(cell_image, (x_pos, y_pos))
+
+# Add title and labels using PIL
+draw = ImageDraw.Draw(composite)
+
+# Try to use a nice font, fall back to default
+try:
+    title_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 56)
+    label_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 36)
+except OSError:
+    title_font = ImageFont.load_default()
+    label_font = ImageFont.load_default()
+
+# Title
+title_text = "scatter-matrix · pygal · pyplots.ai"
+title_bbox = draw.textbbox((0, 0), title_text, font=title_font)
+title_width = title_bbox[2] - title_bbox[0]
+draw.text(((total_width - title_width) // 2, 35), title_text, fill="#333", font=title_font)
+
+# Variable labels along bottom and left
+for idx, var_name in enumerate(var_names):
+    # Bottom labels
+    x_label_pos = margin_left + idx * cell_size + cell_size // 2
+    y_label_pos = margin_top + n_vars * cell_size + 40
+    bbox = draw.textbbox((0, 0), var_name, font=label_font)
+    text_width = bbox[2] - bbox[0]
+    draw.text((x_label_pos - text_width // 2, y_label_pos), var_name, fill="#333", font=label_font)
+
+    # Left labels (rotated - draw text vertically, positioned closer to plots)
+    x_label_pos = 15
+    y_label_pos = margin_top + idx * cell_size + cell_size // 2
+
+    # Create rotated text image
+    txt_img = Image.new("RGBA", (350, 60), (255, 255, 255, 0))
+    txt_draw = ImageDraw.Draw(txt_img)
+    txt_draw.text((0, 0), var_name, fill="#333", font=label_font)
+    txt_rotated = txt_img.rotate(90, expand=True)
+
+    # Paste rotated text
+    bbox = draw.textbbox((0, 0), var_name, font=label_font)
+    text_height = bbox[2] - bbox[0]
+    paste_y = y_label_pos - text_height // 2
+    composite.paste(txt_rotated, (x_label_pos, paste_y), txt_rotated)
+
+# Save output
+composite.save("plot.png", "PNG", dpi=(300, 300))

plots/scatter-matrix/metadata/pygal.yaml

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+library: pygal
+specification_id: scatter-matrix
+created: '2025-12-26T16:17:50Z'
+updated: '2025-12-26T16:35:12Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20525448000
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/pygal/plot.html
+quality_score: 90
+review:
+  strengths:
+  - Creative solution using PIL composite to build a scatter matrix from individual
+    pygal charts
+  - Excellent use of pygal.Histogram for diagonal cells showing univariate distributions
+  - Good transparency handling (opacity=0.55) makes overlapping points distinguishable
+  - Clean Iris-like dataset with visible positive and negative correlations
+  - Proper title format following pyplots convention
+  - Variable labels positioned on both left (rotated) and bottom edges as per spec
+  - Square 3600x3600 format is appropriate for matrix visualization
+  weaknesses:
+  - Axis tick labels within cells are somewhat small relative to the overall image
+    size
+  - Could benefit from subtle grid lines within scatter cells for reference