feat(bokeh): implement subplot-grid-custom

plots/subplot-grid-custom/implementations/bokeh.py

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+""" pyplots.ai
+subplot-grid-custom: Custom Subplot Grid Layout
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.layouts import column, row
+from bokeh.models import ColumnDataSource, Title
+from bokeh.plotting import figure
+
+
+# Data
+np.random.seed(42)
+
+# Main time series data (simulating daily stock prices)
+n_days = 100
+dates = np.arange(n_days)
+prices = 100 + np.cumsum(np.random.randn(n_days) * 2)
+volumes = np.random.uniform(1, 5, n_days) * 1e6
+returns = np.diff(prices) / prices[:-1] * 100
+
+# Additional data for supporting plots
+categories = ["Product A", "Product B", "Product C", "Product D"]
+sales = [45, 72, 38, 65]
+
+scatter_x = np.random.randn(80)
+scatter_y = scatter_x * 0.8 + np.random.randn(80) * 0.5
+
+# Column data sources
+price_source = ColumnDataSource(data={"x": dates, "y": prices})
+bar_source = ColumnDataSource(data={"x": categories, "y": sales})
+scatter_source = ColumnDataSource(data={"x": scatter_x, "y": scatter_y})
+
+# Color palette
+python_blue = "#306998"
+python_yellow = "#FFD43B"
+accent_green = "#2E8B57"
+accent_red = "#CD5C5C"
+
+# Plot 1: Main time series (large - spans conceptually 2 columns)
+p1 = figure(
+    width=3000,
+    height=1400,
+    title="Price Trend Over Time",
+    x_axis_label="Day",
+    y_axis_label="Price ($)",
+    toolbar_location=None,
+)
+p1.line("x", "y", source=price_source, line_width=4, color=python_blue, alpha=0.9)
+p1.scatter("x", "y", source=price_source, size=12, color=python_blue, alpha=0.6)
+p1.title.text_font_size = "32pt"
+p1.xaxis.axis_label_text_font_size = "24pt"
+p1.yaxis.axis_label_text_font_size = "24pt"
+p1.xaxis.major_label_text_font_size = "20pt"
+p1.yaxis.major_label_text_font_size = "20pt"
+p1.grid.grid_line_alpha = 0.3
+p1.outline_line_color = "#cccccc"
+p1.outline_line_width = 2
+
+# Add main title above p1
+main_title = Title(text="subplot-grid-custom · bokeh · pyplots.ai", text_font_size="36pt", align="center")
+p1.add_layout(main_title, "above")
+
+# Plot 2: Volume bar chart (right side panel)
+p2 = figure(
+    width=1600,
+    height=1400,
+    title="Daily Trading Volume",
+    x_axis_label="Day",
+    y_axis_label="Volume (millions)",
+    toolbar_location=None,
+)
+p2.vbar(x=dates, top=volumes / 1e6, width=0.7, color=python_yellow, alpha=0.85)
+p2.title.text_font_size = "32pt"
+p2.xaxis.axis_label_text_font_size = "24pt"
+p2.yaxis.axis_label_text_font_size = "24pt"
+p2.xaxis.major_label_text_font_size = "20pt"
+p2.yaxis.major_label_text_font_size = "20pt"
+p2.grid.grid_line_alpha = 0.3
+p2.outline_line_color = "#cccccc"
+p2.outline_line_width = 2
+
+# Plot 3: Returns histogram (bottom left)
+hist, edges = np.histogram(returns, bins=20)
+hist_source = ColumnDataSource(data={"top": hist, "left": edges[:-1], "right": edges[1:]})
+p3 = figure(
+    width=1500,
+    height=1100,
+    title="Returns Distribution",
+    x_axis_label="Daily Return (%)",
+    y_axis_label="Frequency",
+    toolbar_location=None,
+)
+p3.quad(
+    top="top",
+    bottom=0,
+    left="left",
+    right="right",
+    source=hist_source,
+    fill_color=accent_green,
+    line_color="white",
+    line_width=2,
+    alpha=0.85,
+)
+p3.title.text_font_size = "28pt"
+p3.xaxis.axis_label_text_font_size = "22pt"
+p3.yaxis.axis_label_text_font_size = "22pt"
+p3.xaxis.major_label_text_font_size = "18pt"
+p3.yaxis.major_label_text_font_size = "18pt"
+p3.grid.grid_line_alpha = 0.3
+p3.outline_line_color = "#cccccc"
+p3.outline_line_width = 2
+
+# Plot 4: Categorical bar chart (bottom center)
+p4 = figure(
+    width=1500,
+    height=1100,
+    x_range=categories,
+    title="Sales by Product Category",
+    x_axis_label="Product",
+    y_axis_label="Units Sold",
+    toolbar_location=None,
+)
+p4.vbar(x="x", top="y", source=bar_source, width=0.6, color=python_blue, alpha=0.85)
+p4.title.text_font_size = "28pt"
+p4.xaxis.axis_label_text_font_size = "22pt"
+p4.yaxis.axis_label_text_font_size = "22pt"
+p4.xaxis.major_label_text_font_size = "18pt"
+p4.yaxis.major_label_text_font_size = "18pt"
+p4.xaxis.major_label_orientation = 0.3
+p4.grid.grid_line_alpha = 0.3
+p4.outline_line_color = "#cccccc"
+p4.outline_line_width = 2
+
+# Plot 5: Scatter plot with correlation (bottom right)
+p5 = figure(
+    width=1600,
+    height=1100,
+    title="Variable Correlation Analysis",
+    x_axis_label="X Variable",
+    y_axis_label="Y Variable",
+    toolbar_location=None,
+)
+p5.scatter("x", "y", source=scatter_source, size=18, color=accent_red, alpha=0.7)
+p5.title.text_font_size = "28pt"
+p5.xaxis.axis_label_text_font_size = "22pt"
+p5.yaxis.axis_label_text_font_size = "22pt"
+p5.xaxis.major_label_text_font_size = "18pt"
+p5.yaxis.major_label_text_font_size = "18pt"
+p5.grid.grid_line_alpha = 0.3
+p5.outline_line_color = "#cccccc"
+p5.outline_line_width = 2
+
+# Custom grid layout demonstrating non-uniform cell sizes
+# Row 1: Main time series (3000px) + Volume sidebar (1600px) = 4600px
+# Row 2: Histogram (1500px) + Bar chart (1500px) + Scatter (1600px) = 4600px
+# This creates a dashboard with varied cell sizes (colspan/rowspan concept)
+
+top_row = row(p1, p2)
+bottom_row = row(p3, p4, p5)
+final_layout = column(top_row, bottom_row)
+
+# Save
+export_png(final_layout, filename="plot.png")

plots/subplot-grid-custom/metadata/bokeh.yaml

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+library: bokeh
+specification_id: subplot-grid-custom
+created: '2025-12-30T23:54:36Z'
+updated: '2025-12-30T23:59:23Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608471124
+issue: 2856
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent dashboard-style layout demonstrating non-uniform subplot sizes with
+    larger main chart and smaller detail panels
+  - Consistent visual styling across all subplots with matching font sizes, grid alpha,
+    and outline styling
+  - Realistic financial dashboard scenario with coherent data story (price, volume,
+    returns analysis)
+  - Clean KISS code structure with proper use of ColumnDataSource for all data
+  - Good color palette using Python blue/yellow theme with accent colors for variety
+  weaknesses:
+  - Bottom row panels have slight width inconsistency (1500+1500+1600 vs top row)
+    creating minor visual imbalance
+  - Does not demonstrate rowspan (tall sidebar plot) which is mentioned in the spec
+    notes
+  - Could leverage more Bokeh-specific interactive features like linked brushing or
+    hover tooltips