feat(bokeh): implement point-basic (#2586)

## Implementation: `point-basic` - bokeh

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593350532)*

---------

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/point-basic/implementations/bokeh.py

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+""" pyplots.ai
+point-basic: Point Estimate Plot
+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, save
+from bokeh.models import ColumnDataSource, Span, TeeHead, Whisker
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+
+
+# Data - Effect sizes for different treatment groups with 95% confidence intervals
+np.random.seed(42)
+categories = ["Treatment A", "Treatment B", "Treatment C", "Treatment D", "Treatment E", "Control"]
+estimates = np.array([2.5, 1.8, 3.2, -0.5, 1.2, 0.0])
+# Generate confidence intervals with varying widths
+ci_widths = np.array([0.8, 1.2, 0.6, 0.9, 1.5, 0.4])
+lower = estimates - ci_widths
+upper = estimates + ci_widths
+
+# Create ColumnDataSource
+source = ColumnDataSource(data={"categories": categories, "estimates": estimates, "lower": lower, "upper": upper})
+
+# Create figure with categorical y-axis (horizontal orientation for readability)
+p = figure(
+    width=4800,
+    height=2700,
+    y_range=categories[::-1],  # Reverse order so first category is at top
+    title="point-basic · bokeh · pyplots.ai",
+    x_axis_label="Effect Size",
+    y_axis_label="Treatment Group",
+)
+
+# Add reference line at zero (null hypothesis)
+zero_line = Span(location=0, dimension="height", line_color="#888888", line_width=3, line_dash="dashed")
+p.add_layout(zero_line)
+
+# Add error bars using Whisker with TeeHead caps (horizontal)
+whisker = Whisker(
+    source=source,
+    base="categories",
+    lower="lower",
+    upper="upper",
+    dimension="width",
+    line_color="#306998",
+    line_width=6,
+    upper_head=TeeHead(size=30, line_color="#306998", line_width=6),
+    lower_head=TeeHead(size=30, line_color="#306998", line_width=6),
+)
+p.add_layout(whisker)
+
+# Plot points (estimates) with larger markers for visibility
+p.scatter(x="estimates", y="categories", source=source, size=40, color="#306998", fill_color="#FFD43B", line_width=4)
+
+# Styling for large canvas
+p.title.text_font_size = "36pt"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "22pt"
+p.yaxis.major_label_text_font_size = "22pt"
+
+# Grid styling
+p.xgrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = "dashed"
+p.ygrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_dash = "dashed"
+
+# Remove toolbar and outline for cleaner look
+p.toolbar_location = None
+p.outline_line_color = None
+
+# Save as PNG and HTML
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="point-basic · bokeh · pyplots.ai")

plots/point-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: point-basic
+created: '2025-12-30T09:33:27Z'
+updated: '2025-12-30T09:44:17Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593350532
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/point-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/point-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/point-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of horizontal point estimate plot with proper orientation
+    for readability
+  - Proper use of Bokeh's Whisker annotation with TeeHead caps for error bars
+  - Clean reference line at zero using Span model
+  - Appropriate font sizing for the large canvas (36pt title, 28pt labels, 22pt ticks)
+  - Good visual hierarchy with yellow-filled points outlined in blue
+  - Realistic treatment effect data with varying confidence interval widths
+  weaknesses:
+  - Could benefit from HoverTool to show exact values on mouse-over (Bokeh's key interactive
+    feature)
+  - Axis labels lack units (e.g., "Effect Size (Cohen's d)" would be more informative)