feat(bokeh): implement scatter-basic (#1319)

## Implementation: `scatter-basic` - bokeh

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20446832275)*

---------

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-basic/implementations/bokeh.py

@@ -1,39 +1,48 @@
 """ pyplots.ai
 scatter-basic: Basic Scatter Plot
 Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 100/100 | Created: 2025-12-13
+Quality: 85/100 | Created: 2025-12-22
 """
 
 import numpy as np
 from bokeh.io import export_png
-from bokeh.models import ColumnDataSource
+from bokeh.models import ColumnDataSource, HoverTool
 from bokeh.plotting import figure, output_file, save
 
 
-# Data
+# Data - Study hours vs exam scores (realistic scenario)
 np.random.seed(42)
-x = np.random.randn(100) * 2 + 10
-y = x * 0.8 + np.random.randn(100) * 2
+study_hours = np.random.uniform(1, 10, 100)
+exam_scores = study_hours * 8 + np.random.randn(100) * 5 + 20
+exam_scores = np.clip(exam_scores, 0, 100)
 
 # Create ColumnDataSource
-source = ColumnDataSource(data={"x": x, "y": y})
+source = ColumnDataSource(data={"study_hours": study_hours, "exam_scores": exam_scores})
 
 # Create figure (4800 x 2700 px for 16:9 aspect ratio)
-p = figure(
-    width=4800, height=2700, title="scatter-basic · bokeh · pyplots.ai", x_axis_label="X Value", y_axis_label="Y Value"
-)
-
-# Plot scatter points
-p.scatter(x="x", y="y", source=source, size=15, color="#306998", alpha=0.7)
-
-# Styling (scaled for 4800x2700 px canvas)
-p.title.text_font_size = "28pt"
-p.xaxis.axis_label_text_font_size = "22pt"
-p.yaxis.axis_label_text_font_size = "22pt"
-p.xaxis.major_label_text_font_size = "18pt"
-p.yaxis.major_label_text_font_size = "18pt"
-p.grid.grid_line_alpha = 0.3
-p.grid.grid_line_dash = "dashed"
+p = figure(width=4800, height=2700, title="scatter-basic · bokeh · pyplots.ai")
+
+# Set axis labels explicitly (more reliable than figure parameters)
+p.xaxis.axis_label = "Study Hours (hrs)"
+p.yaxis.axis_label = "Exam Score (%)"
+
+# Plot scatter points (size increased for visibility on large canvas)
+p.scatter(x="study_hours", y="exam_scores", source=source, size=50, color="#306998", alpha=0.7)
+
+# Add HoverTool for interactivity (key Bokeh distinctive feature)
+hover = HoverTool(tooltips=[("Study Hours", "@study_hours{0.1} hrs"), ("Exam Score", "@exam_scores{0.1}%")])
+p.add_tools(hover)
+
+# Styling (scaled for 4800x2700 px canvas - larger sizes for readability)
+p.title.text_font_size = "72pt"
+p.xaxis.axis_label_text_font_size = "48pt"
+p.yaxis.axis_label_text_font_size = "48pt"
+p.xaxis.major_label_text_font_size = "36pt"
+p.yaxis.major_label_text_font_size = "36pt"
+
+# Grid styling (subtle, per quality criteria VQ-07: alpha 0.2-0.4)
+p.grid.grid_line_alpha = 0.35
+p.grid.grid_line_width = 2
 
 # Save as PNG
 export_png(p, filename="plot.png")

plots/scatter-basic/metadata/bokeh.yaml

@@ -1,17 +1,26 @@
 library: bokeh
 specification_id: scatter-basic
-created: 2025-12-13 23:13:59+00:00
-updated: 2025-12-13 23:13:59+00:00
+created: '2025-12-22T23:35:35Z'
+updated: '2025-12-23T00:15:21Z'
 generated_by: claude-opus-4-5-20251101
-workflow_run: 20199223493
-issue: 611
+workflow_run: 20446832275
+issue: 0
 python_version: 3.13.11
 library_version: 3.8.1
 preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-basic/bokeh/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-basic/bokeh/plot_thumb.png
 preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-basic/bokeh/plot.html
-quality_score: 100
+quality_score: 85
 review:
-  strengths: []
-  weaknesses: []
-  improvements: []
+  strengths:
+  - Excellent text legibility with well-scaled font sizes for 4800x2700 canvas
+  - Clean KISS code structure with proper reproducibility (seed=42)
+  - Realistic educational data scenario (study hours vs exam scores)
+  - Correct title format and descriptive axis labels with units
+  - Proper use of ColumnDataSource pattern
+  weaknesses:
+  - HoverTool adds no value to static PNG output - Bokeh distinctive features not
+    visible in rendered image
+  - Marker size=50 is slightly too large for 100 points, causing visual density issues
+    in clustered regions
+  - No legend to identify the data series (minor for single-series plot)