feat(bokeh): implement scatter-basic (#5321)

## Implementation: `scatter-basic` - python/bokeh

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

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

**Parent Issue:** #611

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/24860109612)*

---------

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

Author: github-actions[bot]

plots/scatter-basic/implementations/python/bokeh.py

@@ -1,14 +1,14 @@
 """ anyplot.ai
 scatter-basic: Basic Scatter Plot
 Library: bokeh 3.9.0 | Python 3.14.4
-Quality: 92/100 | Created: 2026-04-23
+Quality: 90/100 | Updated: 2026-04-23
 """
 
 import os
 
 import numpy as np
 from bokeh.io import export_png, output_file, save
-from bokeh.models import Band, ColumnDataSource, HoverTool, Slope
+from bokeh.models import ColumnDataSource, HoverTool
 from bokeh.plotting import figure
 
 
@@ -18,90 +18,58 @@
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 BRAND = "#009E73"
 
-# Data — study hours vs exam scores
+# Data — study hours vs exam scores, moderate positive correlation
 np.random.seed(42)
-n_points = 150
-study_hours = np.random.uniform(1, 10, n_points)
-exam_scores = study_hours * 7 + np.random.randn(n_points) * 6 + 25
-
-# Natural outliers (test anxiety, gifted, under-performers)
-exam_scores[5] = 38
-exam_scores[22] = 92
-exam_scores[47] = 30
-exam_scores[71] = 95
-exam_scores[88] = 42
-
-exam_scores = np.clip(exam_scores, 15, 98)
-
-# Linear regression for trend line and confidence band
-slope_coef = np.polyfit(study_hours, exam_scores, 1)
-predicted = np.polyval(slope_coef, study_hours)
-residual_std = np.std(exam_scores - predicted)
-
-sort_idx = np.argsort(study_hours)
-x_sorted = study_hours[sort_idx]
-y_pred_sorted = np.polyval(slope_coef, x_sorted)
-band_source = ColumnDataSource(
-    data={"x": x_sorted, "upper": y_pred_sorted + 1.5 * residual_std, "lower": y_pred_sorted - 1.5 * residual_std}
-)
+n_points = 180
+study_hours = np.random.uniform(0.8, 9.6, n_points)
+exam_scores = study_hours * 7.2 + np.random.normal(0, 6.5, n_points) + 26
+exam_scores = np.clip(exam_scores, 18, 99)
 
-source = ColumnDataSource(data={"x": study_hours, "y": exam_scores})
+source = ColumnDataSource(data={"study_hours": study_hours, "exam_scores": exam_scores})
 
+# Plot
 p = figure(
     width=4800,
     height=2700,
     title="scatter-basic · bokeh · anyplot.ai",
-    x_axis_label="Study Hours per Day (hrs)",
+    x_axis_label="Study Hours per Day",
     y_axis_label="Exam Score (%)",
     toolbar_location=None,
-    x_range=(0, 11),
-    y_range=(12, 103),
-)
-
-# Confidence band — Bokeh-native Band glyph
-band = Band(
-    base="x",
-    lower="lower",
-    upper="upper",
-    source=band_source,
-    level="underlay",
-    fill_alpha=0.15,
-    fill_color=BRAND,
-    line_width=0,
-)
-p.add_layout(band)
-
-# Trend line via Slope model — Bokeh-specific annotation
-trend = Slope(
-    gradient=slope_coef[0],
-    y_intercept=slope_coef[1],
-    line_color=BRAND,
-    line_width=5,
-    line_alpha=0.55,
-    line_dash="dashed",
+    x_range=(0, 10.5),
+    y_range=(10, 104),
 )
-p.add_layout(trend)
 
-# Scatter points with white edge for definition in dense areas
 scatter_renderer = p.scatter(
-    x="x", y="y", source=source, size=32, color=BRAND, alpha=0.7, line_color="white", line_width=1
+    x="study_hours", y="exam_scores", source=source, size=34, color=BRAND, alpha=0.7, line_color=PAGE_BG, line_width=1.2
 )
 
-# HoverTool — Bokeh's distinctive interactive feature
-hover = HoverTool(renderers=[scatter_renderer], tooltips=[("Study Hours", "@x{0.1} hrs"), ("Exam Score", "@y{0.0}%")])
+# HoverTool — Bokeh's distinctive interactive feature (HTML only; PNG stays clean)
+hover = HoverTool(
+    renderers=[scatter_renderer],
+    tooltips=[("Study Hours", "@study_hours{0.1} hrs"), ("Exam Score", "@exam_scores{0.0}%")],
+)
 p.add_tools(hover)
 
-# Typography — explicitly sized for 4800×2700 canvas
+# Typography — sized for 4800×2700 canvas
 p.title.text_font_size = "42pt"
-p.title.text_color = INK
 p.title.text_font_style = "bold"
+p.title.text_color = INK
+p.title.align = "center"
 
 p.xaxis.axis_label_text_font_size = "32pt"
 p.yaxis.axis_label_text_font_size = "32pt"
+p.xaxis.axis_label_text_font_style = "normal"
+p.yaxis.axis_label_text_font_style = "normal"
 p.xaxis.major_label_text_font_size = "24pt"
 p.yaxis.major_label_text_font_size = "24pt"
+p.xaxis.axis_label_standoff = 28
+p.yaxis.axis_label_standoff = 28
 
 # Theme-adaptive chrome
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = None
+
 p.xaxis.axis_label_text_color = INK
 p.yaxis.axis_label_text_color = INK
 p.xaxis.major_label_text_color = INK_SOFT
@@ -113,20 +81,18 @@
 p.xaxis.minor_tick_line_color = None
 p.yaxis.minor_tick_line_color = None
 
+# Clean L-frame: keep left+bottom axis lines only (handled above via axis_line_color)
 p.xgrid.grid_line_color = INK
 p.ygrid.grid_line_color = INK
 p.xgrid.grid_line_alpha = 0.10
 p.ygrid.grid_line_alpha = 0.10
 p.xgrid.grid_line_width = 2
 p.ygrid.grid_line_width = 2
 
-p.background_fill_color = PAGE_BG
-p.border_fill_color = PAGE_BG
-p.outline_line_color = None
-
 p.xaxis.ticker.desired_num_ticks = 10
 p.yaxis.ticker.desired_num_ticks = 8
 
+# Save
 export_png(p, filename=f"plot-{THEME}.png")
 output_file(f"plot-{THEME}.html", title="scatter-basic · bokeh · anyplot.ai")
 save(p)