feat(bokeh): implement scatter-annotated (#2807)

## Implementation: `scatter-annotated` - bokeh

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602453064)*

---------

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

Author: github-actions[bot]

plots/scatter-annotated/implementations/bokeh.py

@@ -0,0 +1,98 @@
+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+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
+from bokeh.models import ColumnDataSource, LabelSet
+from bokeh.plotting import figure
+
+
+# Data - Company market performance example
+np.random.seed(42)
+companies = [
+    "TechCorp",
+    "DataSys",
+    "CloudNet",
+    "AIVenture",
+    "NetFlow",
+    "CodeBase",
+    "ByteWorks",
+    "DigiCore",
+    "InfoTech",
+    "WebScale",
+    "AppLogic",
+    "SoftPeak",
+    "CyberLink",
+    "DevOps",
+    "QuantumBit",
+]
+
+# Revenue (billions) and Market Cap (billions)
+revenue = np.array([12.5, 8.3, 22.1, 5.7, 15.8, 9.2, 18.4, 6.9, 11.3, 25.6, 7.4, 14.2, 19.8, 4.5, 10.1])
+market_cap = np.array([45.2, 28.1, 85.3, 32.5, 52.8, 35.6, 68.9, 25.4, 41.7, 98.2, 22.3, 55.4, 72.1, 18.9, 38.5])
+
+# Create ColumnDataSource
+source = ColumnDataSource(data={"x": revenue, "y": market_cap, "labels": companies})
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="scatter-annotated · bokeh · pyplots.ai",
+    x_axis_label="Revenue (Billions $)",
+    y_axis_label="Market Cap (Billions $)",
+    tools="pan,wheel_zoom,box_zoom,reset",
+)
+
+# Plot scatter points - larger size for visibility
+p.scatter(x="x", y="y", source=source, size=40, color="#306998", alpha=0.7, line_color="#1a3d5c", line_width=3)
+
+# Add text labels with offset - larger font for 4800x2700 canvas
+labels = LabelSet(
+    x="x",
+    y="y",
+    text="labels",
+    source=source,
+    x_offset=25,
+    y_offset=15,
+    text_font_size="24pt",
+    text_color="#333333",
+    text_font_style="bold",
+)
+p.add_layout(labels)
+
+# Style title - large for visibility
+p.title.text_font_size = "42pt"
+p.title.text_color = "#333333"
+p.title.align = "center"
+
+# Style axes labels - large for readability
+p.xaxis.axis_label_text_font_size = "32pt"
+p.yaxis.axis_label_text_font_size = "32pt"
+p.xaxis.axis_label_text_color = "#333333"
+p.yaxis.axis_label_text_color = "#333333"
+
+# Style tick labels - larger
+p.xaxis.major_label_text_font_size = "24pt"
+p.yaxis.major_label_text_font_size = "24pt"
+
+# Style grid
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = [6, 4]
+p.grid.grid_line_color = "#888888"
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+
+# Axis styling
+p.axis.axis_line_color = "#555555"
+p.axis.axis_line_width = 2
+p.axis.major_tick_line_width = 2
+p.axis.minor_tick_line_width = 1
+
+# Save
+export_png(p, filename="plot.png")

plots/scatter-annotated/metadata/bokeh.yaml

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+library: bokeh
+specification_id: scatter-annotated
+created: '2025-12-30T17:48:20Z'
+updated: '2025-12-30T17:55:48Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602453064
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent text sizing throughout - all elements perfectly legible at 4800x2700
+    resolution
+  - Well-chosen realistic business scenario (tech company market performance) with
+    appropriate data ranges
+  - Good use of Bokeh's ColumnDataSource and LabelSet for clean data binding
+  - Proper styling with subtle grid, good color choice, and appropriate marker sizing
+    for the data density
+  - Clean KISS code structure with reproducible seed
+  weaknesses:
+  - Missing connecting lines/arrows from labels to points as specified in the notes
+    section of the specification
+  - Some label pairs are close together (ByteWorks/CyberLink, SoftPeak/NetFlow) -
+    could benefit from collision avoidance
+  - Does not leverage Bokeh's distinctive hover tooltip feature which would enhance
+    the visualization