feat(bokeh): implement dumbbell-basic (#5418)

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

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

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

**Parent Issue:** #945

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/24945052285)*

---------

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/dumbbell-basic/implementations/python/bokeh.py

@@ -1,15 +1,27 @@
-""" pyplots.ai
+""" anyplot.ai
 dumbbell-basic: Basic Dumbbell Chart
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: bokeh 3.9.0 | Python 3.14.4
+Quality: 88/100 | Updated: 2026-04-26
 """
 
+import os
+
 from bokeh.io import export_png, output_file, save
-from bokeh.models import ColumnDataSource
+from bokeh.models import ColumnDataSource, HoverTool
 from bokeh.plotting import figure
 
 
-# Data - Employee satisfaction scores before and after policy changes
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+BRAND = "#009E73"  # Okabe-Ito 1 — "Before"
+ACCENT = "#D55E00"  # Okabe-Ito 2 — "After"
+
+# Data — Employee satisfaction scores before and after policy changes
+# (one department regressed, the rest improved by varying amounts)
 categories = [
     "Engineering",
     "Marketing",
@@ -20,63 +32,116 @@
     "Operations",
     "Research & Development",
 ]
-start_values = [62, 58, 71, 55, 68, 72, 60, 65]  # Before policy changes
-end_values = [78, 74, 82, 70, 85, 80, 73, 88]  # After policy changes
+start_values = [62, 58, 71, 55, 68, 78, 60, 65]
+end_values = [78, 74, 82, 70, 85, 72, 73, 88]
 
-# Sort by difference for better visualization
-differences = [e - s for s, e in zip(start_values, end_values, strict=True)]
-sorted_data = sorted(zip(categories, start_values, end_values, differences, strict=True), key=lambda x: x[3])
-categories = [d[0] for d in sorted_data]
-start_values = [d[1] for d in sorted_data]
-end_values = [d[2] for d in sorted_data]
+# Sort ascending by change so the largest improvements sit at the top
+deltas = [e - s for s, e in zip(start_values, end_values, strict=True)]
+ordered = sorted(zip(categories, start_values, end_values, deltas, strict=True), key=lambda d: d[3])
+categories = [d[0] for d in ordered]
+start_values = [d[1] for d in ordered]
+end_values = [d[2] for d in ordered]
+deltas = [d[3] for d in ordered]
 
-# Create figure with categorical y-axis (horizontal orientation)
+# Plot
 p = figure(
     width=4800,
     height=2700,
     y_range=categories,
-    title="dumbbell-basic · bokeh · pyplots.ai",
+    x_range=(45, 95),
+    title="Employee Satisfaction · dumbbell-basic · bokeh · anyplot.ai",
     x_axis_label="Satisfaction Score",
     y_axis_label="Department",
+    background_fill_color=PAGE_BG,
+    border_fill_color=PAGE_BG,
+    toolbar_location=None,
 )
 
-# Create connecting lines (thin and subtle)
-for i, cat in enumerate(categories):
-    p.line(x=[start_values[i], end_values[i]], y=[cat, cat], line_width=4, line_color="#888888", line_alpha=0.6)
+# Connecting segments — thin, subtle, behind the dots
+seg_source = ColumnDataSource(
+    data={"y": categories, "x_start": start_values, "x_end": end_values, "delta": [f"{d:+d}" for d in deltas]}
+)
+p.segment(
+    x0="x_start", x1="x_end", y0="y", y1="y", source=seg_source, line_color=INK_SOFT, line_alpha=0.45, line_width=4
+)
 
-# Data sources for dots
-source_start = ColumnDataSource(data={"x": start_values, "y": categories, "label": ["Before"] * len(categories)})
-source_end = ColumnDataSource(data={"x": end_values, "y": categories, "label": ["After"] * len(categories)})
+# "Before" dots — Okabe-Ito brand green
+before_source = ColumnDataSource(data={"x": start_values, "y": categories, "phase": ["Before"] * len(categories)})
+before_glyph = p.scatter(
+    x="x",
+    y="y",
+    source=before_source,
+    size=34,
+    fill_color=BRAND,
+    line_color=PAGE_BG,
+    line_width=2,
+    legend_label="Before policy changes",
+)
 
-# Plot start dots (Before - Python Blue)
-p.scatter(x="x", y="y", source=source_start, size=25, color="#306998", alpha=0.9, legend_label="Before Policy Changes")
+# "After" dots — Okabe-Ito vermillion
+after_source = ColumnDataSource(data={"x": end_values, "y": categories, "phase": ["After"] * len(categories)})
+after_glyph = p.scatter(
+    x="x",
+    y="y",
+    source=after_source,
+    size=34,
+    fill_color=ACCENT,
+    line_color=PAGE_BG,
+    line_width=2,
+    legend_label="After policy changes",
+)
 
-# Plot end dots (After - Python Yellow)
-p.scatter(x="x", y="y", source=source_end, size=25, color="#FFD43B", alpha=0.9, legend_label="After Policy Changes")
+# Hover tooltip (HTML interactivity)
+p.add_tools(
+    HoverTool(
+        renderers=[before_glyph, after_glyph], tooltips=[("Department", "@y"), ("Phase", "@phase"), ("Score", "@x")]
+    )
+)
 
-# Styling for 4800x2700 px
+# Typography
 p.title.text_font_size = "36pt"
+p.title.text_color = INK
+p.title.text_font_style = "normal"
+p.title.align = "center"
+
 p.xaxis.axis_label_text_font_size = "24pt"
 p.yaxis.axis_label_text_font_size = "24pt"
-p.xaxis.major_label_text_font_size = "18pt"
-p.yaxis.major_label_text_font_size = "18pt"
-
-# Grid styling (subtle)
-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"
-
-# Legend styling
-p.legend.label_text_font_size = "18pt"
-p.legend.location = "bottom_right"
-p.legend.background_fill_alpha = 0.7
-
-# Set x-axis range with padding
-p.x_range.start = 45
-p.x_range.end = 95
-
-# Save outputs
-export_png(p, filename="plot.png")
-output_file("plot.html")
+p.xaxis.major_label_text_font_size = "20pt"
+p.yaxis.major_label_text_font_size = "20pt"
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
+p.xaxis.major_label_text_color = INK_SOFT
+p.yaxis.major_label_text_color = INK_SOFT
+p.xaxis.axis_label_standoff = 18
+p.yaxis.axis_label_standoff = 18
+
+# Spines and ticks — keep an L-shape, suppress chart outline
+p.outline_line_color = None
+p.xaxis.axis_line_color = INK_SOFT
+p.yaxis.axis_line_color = INK_SOFT
+p.xaxis.major_tick_line_color = INK_SOFT
+p.yaxis.major_tick_line_color = INK_SOFT
+p.xaxis.minor_tick_line_color = None
+p.yaxis.minor_tick_line_color = None
+
+# Grid — subtle vertical guides only
+p.xgrid.grid_line_color = INK
+p.xgrid.grid_line_alpha = 0.10
+p.ygrid.grid_line_color = None
+
+# Legend — placed inside top-left so it never collides with the data range
+p.legend.location = "top_left"
+p.legend.background_fill_color = ELEVATED_BG
+p.legend.background_fill_alpha = 0.95
+p.legend.border_line_color = INK_SOFT
+p.legend.border_line_alpha = 0.4
+p.legend.label_text_color = INK_SOFT
+p.legend.label_text_font_size = "20pt"
+p.legend.spacing = 10
+p.legend.padding = 18
+p.legend.margin = 24
+
+# Save
+export_png(p, filename=f"plot-{THEME}.png")
+output_file(f"plot-{THEME}.html", title="Employee Satisfaction · dumbbell-basic · bokeh · anyplot.ai")
 save(p)