feat(bokeh): implement errorbar-basic (#5386)

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

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

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

**Parent Issue:** #973

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/24927158256)*

---------

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

@@ -1,74 +1,137 @@
-""" pyplots.ai
+""" anyplot.ai
 errorbar-basic: Basic Error Bar Plot
-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: 2026-04-25
 """
 
+import os
+
 import numpy as np
 from bokeh.io import export_png, output_file, save
-from bokeh.models import ColumnDataSource, TeeHead, Whisker
+from bokeh.models import ColumnDataSource, Label, TeeHead, Whisker
 from bokeh.plotting import figure
 
 
-# Data - experimental measurements with associated uncertainties
+# 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"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
+
+# Okabe-Ito categorical palette (positions 1-6)
+OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9"]
+
+# Data — experimental measurements with associated uncertainties
 np.random.seed(42)
 categories = ["Control", "Treatment A", "Treatment B", "Treatment C", "Treatment D", "Treatment E"]
 means = np.array([25.3, 38.7, 42.1, 35.8, 48.2, 31.5])
 
-# Asymmetric errors to demonstrate feature (some treatments have larger lower uncertainty)
+# Asymmetric errors — Treatment C has highest variability, Treatment D has highest mean
 lower_errors = np.array([2.1, 3.5, 2.8, 6.5, 4.8, 2.5])
 upper_errors = np.array([2.1, 3.5, 2.8, 2.8, 2.2, 2.5])
 
-# Calculate upper and lower bounds for whiskers
 upper = means + upper_errors
 lower = means - lower_errors
+colors = OKABE_ITO[: len(categories)]
 
-# Create ColumnDataSource
-source = ColumnDataSource(data={"categories": categories, "means": means, "upper": upper, "lower": lower})
+source = ColumnDataSource(
+    data={"categories": categories, "means": means, "upper": upper, "lower": lower, "colors": colors}
+)
 
-# Create figure with categorical x-axis
+# Plot
 p = figure(
     width=4800,
     height=2700,
     x_range=categories,
-    title="errorbar-basic · bokeh · pyplots.ai",
+    title="errorbar-basic · bokeh · anyplot.ai",
     x_axis_label="Experimental Group",
     y_axis_label="Response Value (units)",
+    toolbar_location=None,
+    tools="",
 )
 
-# Add error bars using Whisker with TeeHead caps
-whisker = Whisker(
-    base="categories",
-    upper="upper",
-    lower="lower",
-    source=source,
-    line_color="#306998",
-    line_width=4,
-    upper_head=TeeHead(size=20, line_color="#306998", line_width=4),
-    lower_head=TeeHead(size=20, line_color="#306998", line_width=4),
+# Error bars (Whisker with TeeHead caps) — one whisker per group so each can take its own color
+for cat, up, lo, col in zip(categories, upper, lower, colors, strict=True):
+    grp_source = ColumnDataSource(data={"x": [cat], "upper": [up], "lower": [lo]})
+    whisker = Whisker(
+        base="x",
+        upper="upper",
+        lower="lower",
+        source=grp_source,
+        line_color=col,
+        line_width=5,
+        upper_head=TeeHead(size=40, line_color=col, line_width=5),
+        lower_head=TeeHead(size=40, line_color=col, line_width=5),
+    )
+    p.add_layout(whisker)
+
+# Mean markers — colored per group
+p.scatter(x="categories", y="means", source=source, size=28, color="colors", line_color=PAGE_BG, line_width=2)
+
+# Annotation: highlight highest variability (Treatment C, index 3)
+focus_idx = int(np.argmax(lower_errors + upper_errors))
+focus_label = Label(
+    x=focus_idx,
+    y=float(lower[focus_idx]),
+    x_units="data",
+    y_units="data",
+    x_offset=24,
+    y_offset=-12,
+    text="highest variability",
+    text_color=INK_MUTED,
+    text_font_size="20pt",
+    text_font_style="italic",
 )
-p.add_layout(whisker)
+p.add_layout(focus_label)
 
-# Add scatter points for the mean values
-p.scatter(x="categories", y="means", source=source, size=20, color="#306998", alpha=0.9)
+# Style — background and outline
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = None
 
-# Styling for 4800x2700 px
+# Title
+p.title.text_color = INK
 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.ygrid.grid_line_alpha = 0.3
-p.ygrid.grid_line_dash = "dashed"
+p.title.text_font_style = "normal"
+p.title.align = "left"
+
+# Axis labels
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
+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"
+
+# Tick labels
+p.xaxis.major_label_text_color = INK_SOFT
+p.yaxis.major_label_text_color = INK_SOFT
+p.xaxis.major_label_text_font_size = "24pt"
+p.yaxis.major_label_text_font_size = "24pt"
+
+# Axis lines and ticks
+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
+
+# Subtle y-grid only
+p.ygrid.grid_line_color = INK
+p.ygrid.grid_line_alpha = 0.10
 p.xgrid.grid_line_color = None
 
-# Set y-axis range with padding
-p.y_range.start = 0
-p.y_range.end = max(upper) * 1.15
+# Y-range trimmed to data — eliminates dead space below
+y_min = float(min(lower))
+y_max = float(max(upper))
+y_pad = (y_max - y_min) * 0.15
+p.y_range.start = max(0.0, y_min - y_pad)
+p.y_range.end = y_max + y_pad
 
-# Save outputs
-export_png(p, filename="plot.png")
-output_file("plot.html", title="errorbar-basic · bokeh · pyplots.ai")
+# Save
+export_png(p, filename=f"plot-{THEME}.png")
+output_file(f"plot-{THEME}.html", title="errorbar-basic · bokeh · anyplot.ai")
 save(p)