feat(bokeh): implement ridgeline-basic (#5577)

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

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

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

**Parent Issue:** #923

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25145277876)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: 3 people

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

@@ -1,14 +1,25 @@
-""" pyplots.ai
+""" anyplot.ai
 ridgeline-basic: Basic Ridgeline Plot
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 89/100 | Updated: 2026-04-30
 """
 
+import os
+
 import numpy as np
 from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, FactorRange, HoverTool
+from bokeh.palettes import Viridis256
 from bokeh.plotting import figure
 
 
+# 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"
+
 # Data - Monthly temperature distributions
 np.random.seed(42)
 
@@ -18,45 +29,29 @@
 base_temps = [5, 7, 12, 16, 20, 24, 27, 26, 22, 16, 10, 6]
 temp_data = {}
 for i, month in enumerate(months):
-    temps = np.random.normal(base_temps[i], 3, 200)
-    temp_data[month] = temps
+    temp_data[month] = np.random.normal(base_temps[i], 3, 200)
 
-# Create plot (4800 × 2700 px)
-p = figure(
-    width=4800,
-    height=2700,
-    title="ridgeline-basic · bokeh · pyplots.ai",
-    x_axis_label="Temperature (°C)",
-    y_axis_label="Month",
-    y_range=months[::-1],  # Reverse to have January at top
-    toolbar_location=None,
-)
+# CVD-safe sequential gradient via Viridis256 — mapped to mean temperature
+# (cold months get dark purple, warm months get yellow)
+min_t, max_t = min(base_temps), max(base_temps)
+colors_by_month = {
+    month: Viridis256[int((base_temps[i] - min_t) / (max_t - min_t) * 255)] for i, month in enumerate(months)
+}
 
-# Color gradient from blue (cold) to yellow/orange (warm) to blue again
-colors = [
-    "#306998",  # Jan - cold blue
-    "#3A7CA5",  # Feb
-    "#50A3C1",  # Mar
-    "#6BBFCC",  # Apr
-    "#8FD4B4",  # May
-    "#C5E99B",  # Jun
-    "#FFD43B",  # Jul - warm yellow
-    "#FFAA33",  # Aug - warm orange
-    "#E7A467",  # Sep
-    "#DB8C7D",  # Oct
-    "#5BB6CF",  # Nov
-    "#306998",  # Dec - cold blue
-]
-
-# Spacing and overlap parameters
-ridge_height = 0.65  # Height multiplier for each ridge
+# Ridge parameters — height 1.5 → ~50% overlap between adjacent bands (spec: 50-70%)
+ridge_height = 1.5
 x_grid = np.linspace(-5, 40, 300)
 
-# Plot ridgelines (from bottom to top for proper overlapping)
-for i, month in enumerate(reversed(months)):
+# Pre-compute all patch coordinates for ColumnDataSource
+all_xs = []
+all_ys = []
+all_colors = []
+all_months_labels = []
+
+for _i, month in enumerate(reversed(months)):
     temps = temp_data[month]
 
-    # Compute KDE using Gaussian kernel (Silverman's rule for bandwidth)
+    # Gaussian KDE — Silverman's bandwidth rule
     n = len(temps)
     std = np.std(temps)
     iqr = np.percentile(temps, 75) - np.percentile(temps, 25)
@@ -68,54 +63,77 @@
         density += np.exp(-0.5 * ((x_grid - xi) / bandwidth) ** 2)
     density /= n * bandwidth * np.sqrt(2 * np.pi)
 
-    # Normalize density to fit within ridge height
     density_normalized = density / density.max() * ridge_height
 
-    # Y position (reversed order so Jan is at top)
-    color_idx = len(months) - 1 - i  # Original month index for color
-
-    # Create patch coordinates
     x_patch = np.concatenate([[x_grid[0]], x_grid, [x_grid[-1]]])
     y_patch_numeric = np.concatenate([[0], density_normalized, [0]])
+    # Categorical offset tuples: (month_label, float_offset)
+    y_patches = [(month, float(v)) for v in y_patch_numeric]
+
+    all_xs.append(list(x_patch))
+    all_ys.append(y_patches)
+    all_colors.append(colors_by_month[month])
+    all_months_labels.append(month)
+
+source = ColumnDataSource(data={"xs": all_xs, "ys": all_ys, "color": all_colors, "month": all_months_labels})
+
+# Plot (4800 × 2700 px) — FactorRange with top padding prevents Jan peak clipping
+p = figure(
+    width=4800,
+    height=2700,
+    title="ridgeline-basic · bokeh · anyplot.ai",
+    x_axis_label="Temperature (°C)",
+    y_axis_label="Month",
+    y_range=FactorRange(factors=months[::-1], range_padding=0.4),
+    toolbar_location=None,
+)
 
-    # For categorical y-axis, use factor offsets
-    y_patches = [(month, float(y)) for y in y_patch_numeric]
+p.patches("xs", "ys", fill_color="color", fill_alpha=0.85, line_color=INK_SOFT, line_width=2, source=source)
 
-    # Fill with color and add outline
-    p.patch(
-        x=list(x_patch), y=y_patches, fill_color=colors[color_idx], fill_alpha=0.85, line_color="#333333", line_width=2
-    )
+# HoverTool — distinctive Bokeh interactivity
+hover = HoverTool(tooltips=[("Month", "@month")])
+p.add_tools(hover)
 
-# Style the plot
+# Style
 p.title.text_font_size = "32pt"
+p.title.text_color = INK
 p.xaxis.axis_label_text_font_size = "24pt"
 p.yaxis.axis_label_text_font_size = "24pt"
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
 p.xaxis.major_label_text_font_size = "18pt"
 p.yaxis.major_label_text_font_size = "18pt"
-
-# Grid styling
-p.xgrid.grid_line_alpha = 0.3
-p.xgrid.grid_line_dash = "dashed"
-p.ygrid.grid_line_alpha = 0
-
-# Axis styling
+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.xaxis.axis_line_width = 2
 p.yaxis.axis_line_width = 2
 p.xaxis.major_tick_line_width = 2
 p.yaxis.major_tick_line_width = 2
 
-# Set x-axis range to show all data
-p.x_range.start = -5
-p.x_range.end = 40
+# Grid
+p.xgrid.grid_line_color = INK
+p.xgrid.grid_line_alpha = 0.10
+p.xgrid.grid_line_dash = "solid"
+p.ygrid.grid_line_color = INK
+p.ygrid.grid_line_alpha = 0.05
 
-# Remove y-axis ticks (we have categorical labels)
+# Remove y-axis tick marks
 p.yaxis.major_tick_line_color = None
 p.yaxis.minor_tick_line_color = None
 
-# Background
-p.background_fill_color = "#FAFAFA"
+# Set x-axis range
+p.x_range.start = -5
+p.x_range.end = 40
+
+# Background — remove four-sided outline box for cleaner L-frame look
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+p.outline_line_color = None
 
-# Save outputs
-output_file("plot.html")
+# Save
+export_png(p, filename=f"plot-{THEME}.png")
+output_file(f"plot-{THEME}.html")
 save(p)
-export_png(p, filename="plot.png")