feat(bokeh): implement ridgeline-basic (#547)

## Summary
Implements `ridgeline-basic` for **bokeh**.

**Parent Issue:** #539
**Base Branch:** `plot/ridgeline-basic`

## Files
- `plots/ridgeline-basic/implementations/bokeh.py`

## Preview
Preview will be uploaded to GCS staging after this workflow completes.

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

Author: claude[bot]

plots/ridgeline-basic/implementations/bokeh.py

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+"""
+ridgeline-basic: Ridgeline Plot
+Library: bokeh
+"""
+
+import numpy as np
+from bokeh.io import export_png, output_file, save
+from bokeh.models import Range1d
+from bokeh.plotting import figure
+from scipy import stats
+
+
+# Data - Monthly temperature readings
+np.random.seed(42)
+
+months = [
+    "January",
+    "February",
+    "March",
+    "April",
+    "May",
+    "June",
+    "July",
+    "August",
+    "September",
+    "October",
+    "November",
+    "December",
+]
+
+# Generate realistic temperature distributions for each month (Northern Hemisphere)
+base_temps = [2, 4, 8, 12, 17, 21, 24, 23, 19, 13, 7, 3]
+data = {}
+for i, month in enumerate(months):
+    temps = np.random.normal(base_temps[i], 3, 200)
+    data[month] = temps
+
+# Colors for gradient effect (from cool to warm and back)
+colors = [
+    "#306998",
+    "#3B7AAF",
+    "#4A8BC5",
+    "#59A5DC",
+    "#FFD43B",
+    "#F97316",
+    "#DC2626",
+    "#F97316",
+    "#FFD43B",
+    "#59A5DC",
+    "#4A8BC5",
+    "#306998",
+]
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="Monthly Temperature Distributions",
+    x_axis_label="Temperature (°C)",
+    y_axis_label="",
+    tools="",
+    toolbar_location=None,
+)
+
+# Calculate KDE for each category and plot as overlapping ridges
+n_categories = len(months)
+overlap = 0.7  # Overlap factor for ridgeline effect
+x_range = np.linspace(-10, 35, 500)
+
+# Plot from bottom to top (reversed order for proper layering)
+for idx, month in enumerate(reversed(months)):
+    values = data[month]
+    kde = stats.gaussian_kde(values)
+    density = kde(x_range)
+
+    # Normalize density and scale for visual effect
+    density_scaled = density / density.max() * 0.8
+
+    # Calculate vertical offset for this category
+    y_offset = idx * overlap
+
+    # Create patch coordinates (filled area)
+    xs = np.concatenate([[x_range[0]], x_range, [x_range[-1]]])
+    ys = np.concatenate([[y_offset], density_scaled + y_offset, [y_offset]])
+
+    # Draw filled patch
+    color_idx = n_categories - 1 - idx
+    p.patch(xs, ys, fill_color=colors[color_idx], fill_alpha=0.7, line_color="white", line_width=1.5)
+
+# Configure y-axis to show month labels
+y_positions = [i * overlap for i in range(n_categories)]
+y_labels = list(reversed(months))
+
+p.yaxis.ticker = y_positions
+p.yaxis.major_label_overrides = dict(zip(y_positions, y_labels, strict=False))
+
+# Styling
+p.y_range = Range1d(-0.3, (n_categories - 1) * overlap + 1.2)
+p.x_range = Range1d(-10, 35)
+
+p.title.text_font_size = "32pt"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "22pt"
+p.yaxis.major_label_text_font_size = "22pt"
+
+p.xgrid.grid_line_color = "#E0E0E0"
+p.xgrid.grid_line_alpha = 0.5
+p.ygrid.grid_line_color = None
+
+p.outline_line_color = None
+p.background_fill_color = "#FAFAFA"
+
+# Save outputs
+export_png(p, filename="plot.png")
+
+output_file("plot.html")
+save(p)