feat(bokeh): implement facet-grid (#2764)

## Implementation: `facet-grid` - bokeh

Implements the **bokeh** version of `facet-grid`.

**File:** `plots/facet-grid/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20601058721)*

---------

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/facet-grid/implementations/bokeh.py

@@ -0,0 +1,125 @@
+""" pyplots.ai
+facet-grid: Faceted Grid Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from bokeh.io import export_png
+from bokeh.layouts import column, gridplot
+from bokeh.models import ColumnDataSource, Div
+from bokeh.plotting import figure
+
+
+# Data - Product performance across regions and seasons
+np.random.seed(42)
+
+regions = ["North", "South", "East"]
+seasons = ["Spring", "Summer", "Fall", "Winter"]
+
+data = []
+for region in regions:
+    for season in seasons:
+        n_points = 25
+        # Base values vary by region
+        base_x = {"North": 20, "South": 30, "East": 25}[region]
+        base_y = {"North": 50, "South": 70, "East": 60}[region]
+
+        # Seasonal adjustments
+        season_adj = {"Spring": 1.0, "Summer": 1.3, "Fall": 0.9, "Winter": 0.7}[season]
+
+        x = np.random.normal(base_x, 5, n_points)
+        y = base_y * season_adj + x * 0.8 + np.random.normal(0, 8, n_points)
+
+        for xi, yi in zip(x, y, strict=True):
+            data.append({"marketing_spend": xi, "sales": yi, "region": region, "season": season})
+
+df = pd.DataFrame(data)
+
+# Colors for each region - darker orange instead of yellow for better visibility
+colors = {"North": "#306998", "South": "#E69F00", "East": "#4ECDC4"}
+
+# Create grid of plots (rows=seasons, cols=regions)
+# Target: 4800x2700 total. With 3 cols, 4 rows + title/legend:
+# - Subplot width: 4800 / 3 = 1600
+# - Subplot height: (2700 - 120 title - 100 legend) / 4 = 620
+plots = []
+
+for season in seasons:
+    row_plots = []
+    for region in regions:
+        subset = df[(df["region"] == region) & (df["season"] == season)]
+        source = ColumnDataSource(data={"x": subset["marketing_spend"], "y": subset["sales"]})
+
+        # Create figure for each cell
+        p = figure(width=1600, height=620, x_range=(5, 50), y_range=(20, 130), tools="")
+
+        # Add scatter points
+        p.scatter(
+            x="x", y="y", source=source, size=18, color=colors[region], alpha=0.7, line_color="#333333", line_width=1
+        )
+
+        # Add facet label in top-left corner
+        p.text(
+            x=[8],
+            y=[122],
+            text=[f"{region} · {season}"],
+            text_font_size="18pt",
+            text_color="#333333",
+            text_font_style="bold",
+        )
+
+        # Style axes
+        p.xaxis.axis_label = "Marketing Spend ($K)" if season == seasons[-1] else ""
+        p.yaxis.axis_label = "Sales ($K)" if region == regions[0] else ""
+        p.xaxis.axis_label_text_font_size = "20pt"
+        p.yaxis.axis_label_text_font_size = "20pt"
+        p.xaxis.major_label_text_font_size = "16pt"
+        p.yaxis.major_label_text_font_size = "16pt"
+
+        # Grid styling
+        p.xgrid.grid_line_alpha = 0.3
+        p.ygrid.grid_line_alpha = 0.3
+        p.xgrid.grid_line_dash = "dashed"
+        p.ygrid.grid_line_dash = "dashed"
+
+        # Background
+        p.background_fill_color = "#fafafa"
+
+        row_plots.append(p)
+    plots.append(row_plots)
+
+# Create gridplot layout
+grid = gridplot(plots, toolbar_location=None, merge_tools=False)
+
+# Add overall title using Div for cleaner implementation
+title_div = Div(
+    text="<h1 style='text-align: center; color: #333333; font-size: 32pt; margin: 20px 0;'>"
+    "facet-grid · bokeh · pyplots.ai</h1>",
+    width=4800,
+    height=120,
+)
+
+# Create legend using Div for region-color mapping
+legend_html = (
+    "<div style='text-align: center; font-size: 18pt; padding: 20px 0;'>"
+    "<span style='font-weight: bold; margin-right: 30px;'>Region:</span>"
+)
+for region, color in colors.items():
+    legend_html += (
+        f"<span style='margin-right: 40px;'>"
+        f"<span style='display: inline-block; width: 20px; height: 20px; "
+        f"background-color: {color}; border: 1px solid #333; border-radius: 50%; "
+        f"vertical-align: middle; margin-right: 8px;'></span>"
+        f"{region}</span>"
+    )
+legend_html += "</div>"
+
+legend_div = Div(text=legend_html, width=4800, height=80)
+
+# Combine title, grid, and legend using column layout
+final_layout = column(title_div, grid, legend_div)
+
+# Save
+export_png(final_layout, filename="plot.png")

plots/facet-grid/metadata/bokeh.yaml

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+library: bokeh
+specification_id: facet-grid
+created: '2025-12-30T16:31:18Z'
+updated: '2025-12-30T16:49:58Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601058721
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/facet-grid/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/facet-grid/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/facet-grid/bokeh/plot.html
+quality_score: 90
+review:
+  strengths:
+  - Well-organized 4×3 grid layout that clearly shows patterns across regions and
+    seasons
+  - Excellent use of colorblind-safe color palette (blue/orange/teal)
+  - Good text sizing throughout - all labels are clearly readable
+  - Realistic business scenario with meaningful variation in the data
+  - Clean, reproducible code structure following KISS principles
+  weaknesses:
+  - HTML-based legend (Div element) does not render in PNG export - only small colored
+    artifacts appear in subplot corners
+  - No use of Bokeh distinctive interactive features (hover tooltips, linked brushing,
+    pan/zoom tools)