feat(matplotlib): implement subplot-grid-custom (#2884)

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

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

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

**Parent Issue:** #2856

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608469966)*

---------

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

Author: github-actions[bot]

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

@@ -0,0 +1,122 @@
+""" pyplots.ai
+subplot-grid-custom: Custom Subplot Grid Layout
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import matplotlib.dates as mdates
+import matplotlib.gridspec as gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data
+np.random.seed(42)
+
+# Time series data for main plot (spanning 2 columns)
+dates = pd.date_range("2024-01-01", periods=120, freq="D")
+base_price = 100
+returns = np.random.randn(120) * 0.02
+prices = base_price * np.cumprod(1 + returns)
+
+# Volume data for bar chart
+volume = np.random.uniform(1e6, 5e6, 120)
+
+# Returns distribution for histogram
+daily_returns = np.diff(prices) / prices[:-1] * 100
+
+# Scatter data for correlation plot
+x_corr = np.random.randn(50) * 10 + 50
+y_corr = x_corr * 0.7 + np.random.randn(50) * 5
+
+# Category data for bar chart
+categories = ["A", "B", "C", "D"]
+values = [85, 72, 93, 67]
+
+# Create figure with GridSpec (custom layout)
+fig = plt.figure(figsize=(16, 9))
+gs = gridspec.GridSpec(3, 3, figure=fig, height_ratios=[2, 1, 1], wspace=0.35, hspace=0.5)
+
+# Main plot: Time series spanning 2 columns and 2 rows (top-left)
+ax_main = fig.add_subplot(gs[0:2, 0:2])
+ax_main.plot(dates, prices, color="#306998", linewidth=2.5, label="Price")
+ax_main.fill_between(dates, prices.min() * 0.95, prices, alpha=0.2, color="#306998")
+ax_main.set_xlabel("Date", fontsize=18)
+ax_main.set_ylabel("Price ($)", fontsize=18)
+ax_main.set_title("Price Trend (Main View)", fontsize=20, fontweight="bold")
+ax_main.tick_params(axis="both", labelsize=14)
+ax_main.grid(True, alpha=0.3, linestyle="--")
+ax_main.legend(fontsize=14, loc="upper left")
+# Format date labels to avoid overlap
+ax_main.xaxis.set_major_locator(mdates.MonthLocator())
+ax_main.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
+
+# Top right: Correlation scatter plot
+ax_scatter = fig.add_subplot(gs[0, 2])
+ax_scatter.scatter(x_corr, y_corr, s=150, alpha=0.7, color="#FFD43B", edgecolor="#306998", linewidth=1.5)
+ax_scatter.set_xlabel("Variable X", fontsize=16)
+ax_scatter.set_ylabel("Variable Y", fontsize=16)
+ax_scatter.set_title("Correlation", fontsize=18, fontweight="bold")
+ax_scatter.tick_params(axis="both", labelsize=12)
+ax_scatter.grid(True, alpha=0.3, linestyle="--")
+
+# Middle right: Category bar chart
+ax_bar = fig.add_subplot(gs[1, 2])
+bars = ax_bar.bar(
+    categories, values, color=["#306998", "#FFD43B", "#306998", "#FFD43B"], edgecolor="white", linewidth=2
+)
+ax_bar.set_xlabel("Product", fontsize=16)
+ax_bar.set_ylabel("Score", fontsize=16)
+ax_bar.set_title("Performance", fontsize=18, fontweight="bold")
+ax_bar.tick_params(axis="both", labelsize=12)
+ax_bar.set_ylim(0, 110)
+for bar, val in zip(bars, values, strict=True):
+    ax_bar.text(bar.get_x() + bar.get_width() / 2, val + 3, str(val), ha="center", fontsize=12, fontweight="bold")
+
+# Bottom left: Volume bar chart
+ax_volume = fig.add_subplot(gs[2, 0])
+ax_volume.bar(range(len(volume)), volume / 1e6, color="#306998", alpha=0.7, width=1.0)
+ax_volume.set_xlabel("Days", fontsize=16)
+ax_volume.set_ylabel("Volume (M)", fontsize=16)
+ax_volume.set_title("Daily Volume", fontsize=18, fontweight="bold")
+ax_volume.tick_params(axis="both", labelsize=12)
+ax_volume.set_xlim(0, len(volume))
+
+# Bottom middle: Returns histogram
+ax_hist = fig.add_subplot(gs[2, 1])
+ax_hist.hist(daily_returns, bins=25, color="#FFD43B", edgecolor="#306998", linewidth=1.5, alpha=0.8)
+ax_hist.axvline(0, color="#306998", linestyle="--", linewidth=2)
+ax_hist.set_xlabel("Daily Return (%)", fontsize=16)
+ax_hist.set_ylabel("Frequency", fontsize=16)
+ax_hist.set_title("Return Distribution", fontsize=18, fontweight="bold")
+ax_hist.tick_params(axis="both", labelsize=12)
+
+# Bottom right: Summary statistics text box
+ax_stats = fig.add_subplot(gs[2, 2])
+ax_stats.axis("off")
+stats_text = (
+    f"Start Price: ${base_price:.2f}\n"
+    f"End Price: ${prices[-1]:.2f}\n"
+    f"Total Return: {(prices[-1] / base_price - 1) * 100:.1f}%\n"
+    f"Avg Daily Vol: {np.mean(volume) / 1e6:.1f}M\n"
+    f"Volatility: {np.std(daily_returns):.2f}%"
+)
+ax_stats.text(
+    0.5,
+    0.45,
+    stats_text,
+    transform=ax_stats.transAxes,
+    fontsize=15,
+    verticalalignment="center",
+    horizontalalignment="center",
+    bbox={"boxstyle": "round,pad=0.6", "facecolor": "#306998", "alpha": 0.1, "edgecolor": "#306998", "linewidth": 2},
+    family="monospace",
+    linespacing=1.8,
+)
+ax_stats.set_title("Summary Stats", fontsize=18, fontweight="bold")
+
+# Overall title
+fig.suptitle("subplot-grid-custom · matplotlib · pyplots.ai", fontsize=24, fontweight="bold", y=0.99)
+
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

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

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+library: matplotlib
+specification_id: subplot-grid-custom
+created: '2025-12-30T23:55:41Z'
+updated: '2025-12-31T00:01:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608469966
+issue: 2856
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent GridSpec layout with main panel spanning 2 rows and 2 columns, demonstrating
+    the core spec feature
+  - Professional financial dashboard aesthetic with consistent color scheme (Python
+    blue/yellow)
+  - 'Good use of matplotlib-specific features: GridSpec with height_ratios, fill_between,
+    mdates formatting'
+  - All text is clearly legible with appropriate font sizes
+  - Realistic financial data scenario with time series, volume, returns distribution,
+    and summary statistics
+  weaknesses:
+  - Bottom row subplots appear slightly cramped compared to the main panel proportions
+  - Library features score could be improved by using shared axes between related
+    plots (e.g., dates) or constrained_layout