feat(plotnine): implement subplot-grid-custom (#2900)

## Implementation: `subplot-grid-custom` - plotnine

Implements the **plotnine** version of `subplot-grid-custom`.

**File:** `plots/subplot-grid-custom/implementations/plotnine.py`

**Parent Issue:** #2856

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608471415)*

---------

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/subplot-grid-custom/implementations/plotnine.py

@@ -0,0 +1,130 @@
+""" pyplots.ai
+subplot-grid-custom: Custom Subplot Grid Layout
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_line,
+    element_rect,
+    element_text,
+    geom_bar,
+    geom_histogram,
+    geom_line,
+    geom_point,
+    ggplot,
+    labs,
+    scale_fill_manual,
+    stat_smooth,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Investment Portfolio Dashboard
+np.random.seed(42)
+
+# Main time series: Daily portfolio value over 60 days
+n_days = 60
+dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
+portfolio_value = 100000 + np.cumsum(np.random.randn(n_days) * 500 + 50)
+df_portfolio = pd.DataFrame({"day": range(n_days), "value": portfolio_value, "date": dates})
+
+# Volume/Activity data - Trading volume per day
+volume = np.abs(np.random.randn(n_days) * 1000 + 3000)
+df_volume = pd.DataFrame({"day": range(n_days), "volume": volume})
+
+# Asset allocation (pie chart equivalent - bar chart for plotnine)
+assets = ["Stocks", "Bonds", "Real Estate", "Cash"]
+allocations = [55, 25, 12, 8]
+df_allocation = pd.DataFrame(
+    {"asset": pd.Categorical(assets, categories=assets, ordered=True), "allocation": allocations}
+)
+
+# Daily returns distribution
+returns = np.diff(portfolio_value) / portfolio_value[:-1] * 100
+df_returns = pd.DataFrame({"returns": returns})
+
+# Colors
+colors = ["#306998", "#FFD43B", "#4A8BBF", "#E07A5F"]
+
+# Shared theme - sized for 4800x2700 canvas
+base_theme = theme_minimal() + theme(
+    plot_title=element_text(size=18, face="bold", ha="center", margin={"b": 8}),
+    axis_title=element_text(size=16),
+    axis_text=element_text(size=13),
+    legend_text=element_text(size=13),
+    legend_title=element_text(size=14, face="bold"),
+    panel_grid_major=element_line(color="#cccccc", alpha=0.3),
+    panel_grid_minor=element_line(color="#eeeeee", alpha=0.2),
+    panel_background=element_rect(fill="white"),
+    plot_margin=0.01,
+)
+
+# Small theme for right-side panels (more compact titles)
+small_theme = base_theme + theme(
+    plot_title=element_text(size=16, face="bold", margin={"b": 5, "t": 5}),
+    axis_title=element_text(size=14),
+    axis_text=element_text(size=11),
+)
+
+# Main Plot (spans full left side): Portfolio Value Over Time
+p_main = (
+    ggplot(df_portfolio, aes(x="day", y="value"))
+    + geom_line(size=1.8, color="#306998")
+    + geom_point(size=2.5, color="#306998", alpha=0.6)
+    + stat_smooth(method="lm", se=True, color="#FFD43B", fill="#FFD43B", alpha=0.2, size=1.2)
+    + labs(title="Portfolio Value Trend", x="Trading Day", y="Portfolio Value ($)")
+    + base_theme
+    + theme(plot_title=element_text(size=22, margin={"b": 10, "t": 40}))
+)
+
+# Top Right: Asset Allocation (horizontal bar chart)
+p_allocation = (
+    ggplot(df_allocation, aes(x="asset", y="allocation", fill="asset"))
+    + geom_bar(stat="identity", width=0.7, show_legend=False)
+    + scale_fill_manual(values=colors)
+    + labs(title="Asset Allocation", x="", y="Allocation (%)")
+    + small_theme
+    + theme(axis_text_x=element_text(size=11, angle=0))
+)
+
+# Middle Right: Trading Volume
+p_volume = (
+    ggplot(df_volume, aes(x="day", y="volume"))
+    + geom_bar(stat="identity", fill="#306998", alpha=0.7, width=0.8)
+    + labs(title="Daily Trading Volume", x="Trading Day", y="Volume (Units)")
+    + small_theme
+)
+
+# Bottom Right: Returns Distribution
+p_returns = (
+    ggplot(df_returns, aes(x="returns"))
+    + geom_histogram(bins=15, fill="#FFD43B", color="#306998", alpha=0.8, size=0.5)
+    + labs(title="Returns Distribution", x="Daily Return (%)", y="Frequency")
+    + small_theme
+)
+
+# Create custom grid layout using plotnine composition operators
+# Layout: Main plot (full height left) | 3 stacked smaller plots (right column)
+# This creates a dashboard with main visualization and supporting detail panels
+right_column = p_allocation / p_volume / p_returns
+custom_grid = p_main | right_column
+
+# Draw and customize the figure
+fig = custom_grid.draw()
+
+# Set figure size for 4800x2700 px at 300 DPI
+fig.set_size_inches(16, 9)
+
+# Adjust subplot spacing for clean layout with main title
+fig.subplots_adjust(top=0.88, bottom=0.08, left=0.06, right=0.98, hspace=0.35, wspace=0.18)
+
+# Add main title positioned above all subplot titles
+fig.suptitle("subplot-grid-custom · plotnine · pyplots.ai", fontsize=28, fontweight="bold", y=0.98)
+
+# Save
+fig.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")

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

@@ -0,0 +1,29 @@
+library: plotnine
+specification_id: subplot-grid-custom
+created: '2025-12-30T23:58:25Z'
+updated: '2025-12-31T00:02:32Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608471415
+issue: 2856
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent dashboard layout demonstrating custom grid with non-uniform cell sizes
+    using plotnine composition operators
+  - Clear visual hierarchy with main plot given appropriate prominence
+  - Real-world investment portfolio scenario is highly relevant to the spec executive
+    dashboard application
+  - Clean code structure following KISS principles with shared theme definitions
+  - Good use of stat_smooth for trend line with confidence band in main plot
+  - Colorblind-safe color palette with good contrast
+  weaknesses:
+  - Text sizes on right-panel subplots are slightly smaller than optimal for the canvas
+    size
+  - The grid layout uses | and / operators which create a 2-column layout, but does
+    not demonstrate explicit colspan/rowspan parameters mentioned in spec notes
+  - Library features score could be higher with additional ggplot2 grammar features