feat(bokeh): implement ecdf-basic (#5358)

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

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

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

**Parent Issue:** #976

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/24890383540)*

---------

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

@@ -1,59 +1,107 @@
-""" pyplots.ai
+""" anyplot.ai
 ecdf-basic: Basic ECDF 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: 87/100 | Updated: 2026-04-24
 """
 
+import os
+
 import numpy as np
-from bokeh.io import export_png, save
-from bokeh.models import ColumnDataSource
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, HoverTool, Label, Span
 from bokeh.plotting import figure
-from bokeh.resources import CDN
 
 
-# Data
-np.random.seed(42)
-values = np.random.randn(200) * 15 + 50  # Normal distribution: mean=50, std=15
+# 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 position 1 — always first series
 
-# Compute ECDF
-sorted_values = np.sort(values)
-ecdf_y = np.arange(1, len(sorted_values) + 1) / len(sorted_values)
+# Data: marathon finish times (minutes) for 300 recreational runners
+np.random.seed(42)
+n_runners = 300
+finish_times = np.random.normal(loc=240, scale=32, size=n_runners)
 
-# Create step function data
-# For proper ECDF step visualization, duplicate points for horizontal/vertical steps
-x_step = np.repeat(sorted_values, 2)[1:]
-y_step = np.repeat(ecdf_y, 2)[:-1]
+# ECDF
+sorted_times = np.sort(finish_times)
+cumulative = np.arange(1, n_runners + 1) / n_runners
 
-# Add starting point at first value with y=0
-x_step = np.concatenate([[sorted_values[0]], x_step])
-y_step = np.concatenate([[0], y_step])
+# Key percentiles for storytelling
+q25, q50, q75 = np.percentile(sorted_times, [25, 50, 75])
 
-source = ColumnDataSource(data={"x": x_step, "y": y_step})
+source = ColumnDataSource(data={"x": sorted_times, "y": cumulative})
 
 # Plot
 p = figure(
     width=4800,
     height=2700,
-    title="ecdf-basic · bokeh · pyplots.ai",
-    x_axis_label="Value",
-    y_axis_label="Cumulative Proportion",
-    y_range=(0, 1.05),
+    title="Marathon Finish Times · ecdf-basic · bokeh · anyplot.ai",
+    x_axis_label="Finish Time (minutes)",
+    y_axis_label="Cumulative Proportion of Runners",
+    y_range=(0, 1.02),
+    background_fill_color=PAGE_BG,
+    border_fill_color=PAGE_BG,
+    toolbar_location=None,
 )
 
-# Draw step line
-p.line(x="x", y="y", source=source, line_width=4, line_color="#306998", alpha=0.9)
+# ECDF step line (step-after matches the 1/n jump at each data point)
+step_renderer = p.step(x="x", y="y", source=source, line_width=4, line_color=BRAND, mode="after")
+
+# Percentile reference lines (25th, 50th, 75th)
+for q_val, q_lbl in [(q25, "25th"), (q50, "50th (median)"), (q75, "75th")]:
+    p.add_layout(
+        Span(location=q_val, dimension="height", line_color=INK_SOFT, line_dash="dashed", line_width=2, line_alpha=0.55)
+    )
+    p.add_layout(
+        Label(
+            x=q_val,
+            y=0.03,
+            text=f"{q_lbl}: {q_val:.0f} min",
+            text_font_size="20pt",
+            text_color=INK_SOFT,
+            text_font_style="italic",
+            x_offset=14,
+        )
+    )
 
-# Style
+# Hover tool — bokeh's interactive strength
+p.add_tools(
+    HoverTool(
+        renderers=[step_renderer], tooltips=[("Finish Time", "@x{0.0} min"), ("Cumulative", "@y{0.0%}")], mode="vline"
+    )
+)
+
+# Typography
 p.title.text_font_size = "28pt"
+p.title.text_color = INK
+p.title.text_font_style = "bold"
 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"
 
-# Grid styling
-p.grid.grid_line_alpha = 0.3
-p.grid.grid_line_dash = "dashed"
+# Chrome colors
+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_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: y-only, subtle solid lines; remove box outline
+p.outline_line_color = None
+p.xgrid.grid_line_color = None
+p.ygrid.grid_line_color = INK
+p.ygrid.grid_line_alpha = 0.10
 
 # Save
-export_png(p, filename="plot.png")
-save(p, filename="plot.html", resources=CDN, title="ecdf-basic · bokeh · pyplots.ai")
+export_png(p, filename=f"plot-{THEME}.png")
+output_file(f"plot-{THEME}.html", title="Marathon Finish Times · ecdf-basic · bokeh · anyplot.ai")
+save(p)