feat(bokeh): implement timeline-basic (#2461)

## Implementation: `timeline-basic` - bokeh

Implements the **bokeh** version of `timeline-basic`.

**File:** `plots/timeline-basic/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20584329995)*

---------

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

Author: github-actions[bot]

plots/timeline-basic/implementations/bokeh.py

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+""" pyplots.ai
+timeline-basic: Event Timeline
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-29
+"""
+
+import pandas as pd
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Label
+from bokeh.palettes import Category10
+from bokeh.plotting import figure
+
+
+# Data - Software project milestones
+events = [
+    ("2024-01-15", "Project Kickoff", "Planning"),
+    ("2024-02-01", "Requirements Complete", "Planning"),
+    ("2024-03-10", "Design Review", "Design"),
+    ("2024-04-20", "Prototype Ready", "Development"),
+    ("2024-05-15", "Alpha Release", "Development"),
+    ("2024-06-30", "Beta Testing", "Testing"),
+    ("2024-07-25", "Bug Fix Sprint", "Testing"),
+    ("2024-08-15", "Performance Audit", "Testing"),
+    ("2024-09-10", "Security Review", "Release"),
+    ("2024-10-01", "v1.0 Launch", "Release"),
+]
+
+df = pd.DataFrame(events, columns=["date", "event", "category"])
+df["date"] = pd.to_datetime(df["date"])
+
+# Assign alternating y positions for label readability (above/below axis)
+df["y_pos"] = [0.6 if i % 2 == 0 else -0.6 for i in range(len(df))]
+
+# Category colors
+categories = df["category"].unique().tolist()
+color_map = {cat: Category10[10][i] for i, cat in enumerate(categories)}
+df["color"] = df["category"].map(color_map)
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="timeline-basic · bokeh · pyplots.ai",
+    x_axis_type="datetime",
+    y_range=(-1.5, 1.5),
+    tools="",
+    toolbar_location=None,
+)
+
+# Draw the central timeline axis (horizontal line)
+p.line(
+    x=[df["date"].min() - pd.Timedelta(days=10), df["date"].max() + pd.Timedelta(days=10)],
+    y=[0, 0],
+    line_width=6,
+    line_color="#306998",
+    line_alpha=0.8,
+)
+
+# Draw vertical connector lines and markers for each event
+for _, row in df.iterrows():
+    # Vertical connector line from axis to marker
+    p.line(x=[row["date"], row["date"]], y=[0, row["y_pos"]], line_width=3, line_color=row["color"], line_alpha=0.7)
+
+# Plot event markers with category colors
+for cat in categories:
+    cat_df = df[df["category"] == cat]
+    cat_source = ColumnDataSource(cat_df)
+    p.scatter(
+        x="date",
+        y="y_pos",
+        source=cat_source,
+        size=35,
+        color=color_map[cat],
+        alpha=0.9,
+        marker="circle",
+        legend_label=cat,
+        line_color="white",
+        line_width=3,
+    )
+
+# Add event labels manually with proper positioning
+for _, row in df.iterrows():
+    y_offset = 80 if row["y_pos"] > 0 else -80
+    baseline = "bottom" if row["y_pos"] > 0 else "top"
+    label = Label(
+        x=row["date"],
+        y=row["y_pos"],
+        text=row["event"],
+        text_font_size="18pt",
+        text_color="#333333",
+        text_align="center",
+        text_baseline=baseline,
+        y_offset=y_offset,
+    )
+    p.add_layout(label)
+
+# Style the plot
+p.title.text_font_size = "32pt"
+p.title.text_color = "#306998"
+p.title.align = "center"
+
+p.xaxis.axis_label = "Date"
+p.xaxis.axis_label_text_font_size = "24pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.xaxis.major_label_orientation = 0.4
+p.xaxis.axis_line_width = 2
+p.xaxis.major_tick_line_width = 2
+p.xaxis.minor_tick_line_width = 1
+
+p.yaxis.visible = False
+p.ygrid.visible = False
+p.xgrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = "dashed"
+p.xgrid.grid_line_width = 2
+
+p.outline_line_color = None
+p.background_fill_color = "#fafafa"
+
+# Configure legend
+p.legend.location = "top_right"
+p.legend.title = "Phase"
+p.legend.title_text_font_size = "22pt"
+p.legend.label_text_font_size = "20pt"
+p.legend.glyph_height = 30
+p.legend.glyph_width = 30
+p.legend.border_line_color = "#cccccc"
+p.legend.background_fill_alpha = 0.9
+p.legend.padding = 15
+p.legend.spacing = 10
+
+# Save outputs
+export_png(p, filename="plot.png")
+
+# Also save HTML for interactive version
+output_file("plot.html", title="Event Timeline")
+save(p)

plots/timeline-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: timeline-basic
+created: '2025-12-29T22:48:09Z'
+updated: '2025-12-29T22:56:55Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584329995
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/timeline-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/timeline-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/timeline-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent alternating label positioning above/below axis prevents text overlap
+  - Clean category color-coding with proper legend showing all 5 project phases
+  - Good use of Bokeh ColumnDataSource for categorical grouping in scatter plot
+  - Proper 4800x2700 sizing with appropriate font sizes for the canvas
+  - Realistic software project milestone scenario with logical progression
+  - Both PNG and HTML outputs generated for static and interactive viewing
+  weaknesses:
+  - Event label font size (18pt) could be slightly larger for better readability at
+    full resolution
+  - Legend positioned far from data area in top-right corner
+  - Could benefit from Bokeh-specific features like HoverTool for interactivity