feat(pygal): implement scatter-annotated (#2835)

## Implementation: `scatter-annotated` - pygal

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20603305140)*

---------

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

Author: github-actions[bot]

plots/scatter-annotated/implementations/pygal.py

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+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Company market performance (market cap vs revenue)
+np.random.seed(42)
+companies = [
+    "TechFlow",
+    "DataPrime",
+    "CloudNine",
+    "NetWave",
+    "CodeSphere",
+    "ByteLogic",
+    "SoftEdge",
+    "DevStack",
+    "AppForge",
+    "WebCore",
+    "CyberLink",
+    "DigiTech",
+]
+
+# Generate realistic market cap (x) and revenue (y) data in billions
+# Spread out to avoid overlap
+market_cap = np.array([15, 45, 75, 105, 135, 25, 55, 85, 115, 145, 35, 95])
+revenue = np.array([8, 22, 35, 28, 48, 12, 18, 42, 32, 55, 15, 38])
+
+# Create color palette - cycle through distinct colors for each company
+colors = (
+    "#306998",  # Python Blue
+    "#FFD43B",  # Python Yellow
+    "#E74C3C",  # Red
+    "#2ECC71",  # Green
+    "#9B59B6",  # Purple
+    "#3498DB",  # Light Blue
+    "#E67E22",  # Orange
+    "#1ABC9C",  # Teal
+    "#34495E",  # Dark Gray
+    "#F39C12",  # Gold
+    "#16A085",  # Dark Teal
+    "#8E44AD",  # Dark Purple
+)
+
+# Custom style for large canvas with larger value font for annotations
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=colors,
+    title_font_size=48,
+    label_font_size=32,
+    major_label_font_size=28,
+    legend_font_size=24,
+    value_font_size=28,  # Font size for annotations
+    tooltip_font_size=24,
+    stroke_width=2,
+)
+
+# Store company names for value formatter
+company_data = {}
+
+# Create XY chart (scatter plot)
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="scatter-annotated · pygal · pyplots.ai",
+    x_title="Market Cap (Billion $)",
+    y_title="Annual Revenue (Billion $)",
+    show_legend=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=6,
+    show_x_guides=True,
+    show_y_guides=True,
+    dots_size=18,
+    stroke=False,
+    show_dots=True,
+    truncate_label=-1,
+    x_label_rotation=0,
+    range=(0, 65),
+    xrange=(0, 165),
+    print_values=True,
+    print_values_position="top",
+)
+
+# Add each company as individual series with its own color for legend identification
+# Use formatter dict to show company name instead of coordinates
+for i, company in enumerate(companies):
+    company_data[(market_cap[i], revenue[i])] = company
+    chart.add(
+        company,
+        [{"value": (market_cap[i], revenue[i]), "label": company, "formatter": lambda x, c=company: c}],
+        dots_size=20,
+        formatter=lambda x, c=company: c,
+    )
+
+# Render to PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/scatter-annotated/metadata/pygal.yaml

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+library: pygal
+specification_id: scatter-annotated
+created: '2025-12-30T18:37:02Z'
+updated: '2025-12-30T18:39:14Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20603305140
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/pygal/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of individual series per company to achieve distinct colors for
+    each data point
+  - Clean annotation placement with company names displayed directly on the chart
+    using print_values and formatter
+  - Good use of pygal custom Style for consistent theming with appropriate font sizes
+  - Well-chosen business context (tech companies market cap vs revenue) that is realistic
+    and neutral
+  - Proper canvas size (4800x2700) with appropriately scaled fonts
+  weaknesses:
+  - Legend at bottom is redundant with on-chart annotations and appears very small/hard
+    to read
+  - Some color pairs are similar (two purples, two greens) which could cause confusion