feat(matplotlib): implement subplot-grid

plots/subplot-grid/implementations/matplotlib.py

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+""" pyplots.ai
+subplot-grid: Subplot Grid Layout
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Financial dashboard example
+np.random.seed(42)
+
+# Time axis (trading days)
+days = np.arange(1, 101)
+
+# Price data (random walk with drift)
+price_changes = np.random.randn(100) * 2 + 0.05
+prices = 100 + np.cumsum(price_changes)
+
+# Volume data (lognormal distribution)
+volumes = np.random.lognormal(mean=10, sigma=0.5, size=100)
+
+# Daily returns
+returns = np.diff(prices) / prices[:-1] * 100
+
+# Moving averages
+ma_20 = np.convolve(prices, np.ones(20) / 20, mode="valid")
+
+# Create 2x2 subplot grid
+fig, axes = plt.subplots(2, 2, figsize=(16, 9))
+
+# Subplot 1: Price Line Chart (top-left)
+ax1 = axes[0, 0]
+ax1.plot(days, prices, linewidth=2.5, color="#306998", label="Price")
+ax1.plot(days[19:], ma_20, linewidth=2, color="#FFD43B", linestyle="--", label="20-day MA")
+ax1.set_xlabel("Trading Day", fontsize=16)
+ax1.set_ylabel("Price ($)", fontsize=16)
+ax1.set_title("Stock Price", fontsize=18, fontweight="bold")
+ax1.tick_params(axis="both", labelsize=14)
+ax1.legend(fontsize=14, loc="upper left")
+ax1.grid(True, alpha=0.3, linestyle="--")
+
+# Subplot 2: Volume Bar Chart (top-right)
+ax2 = axes[0, 1]
+colors = ["#306998" if r >= 0 else "#D94A4A" for r in np.append(0, returns)]
+ax2.bar(days, volumes / 1000, width=0.8, color=colors, alpha=0.8, edgecolor="none")
+ax2.set_xlabel("Trading Day", fontsize=16)
+ax2.set_ylabel("Volume (thousands)", fontsize=16)
+ax2.set_title("Trading Volume", fontsize=18, fontweight="bold")
+ax2.tick_params(axis="both", labelsize=14)
+ax2.grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# Subplot 3: Returns Histogram (bottom-left)
+ax3 = axes[1, 0]
+ax3.hist(returns, bins=20, color="#306998", edgecolor="white", linewidth=1.5, alpha=0.8)
+ax3.axvline(x=0, color="#FFD43B", linewidth=2.5, linestyle="-", label="Zero Return")
+ax3.axvline(x=np.mean(returns), color="#D94A4A", linewidth=2.5, linestyle="--", label=f"Mean: {np.mean(returns):.2f}%")
+ax3.set_xlabel("Daily Return (%)", fontsize=16)
+ax3.set_ylabel("Frequency", fontsize=16)
+ax3.set_title("Return Distribution", fontsize=18, fontweight="bold")
+ax3.tick_params(axis="both", labelsize=14)
+ax3.legend(fontsize=12, loc="upper right")
+ax3.grid(True, alpha=0.3, linestyle="--", axis="y")
+
+# Subplot 4: Price vs Volume Scatter (bottom-right)
+ax4 = axes[1, 1]
+scatter = ax4.scatter(
+    volumes[1:] / 1000,  # Match returns size (99 elements)
+    np.abs(returns),
+    s=80,
+    c=returns,
+    cmap="RdYlGn",
+    alpha=0.7,
+    edgecolor="white",
+    linewidth=0.5,
+)
+ax4.set_xlabel("Volume (thousands)", fontsize=16)
+ax4.set_ylabel("Absolute Return (%)", fontsize=16)
+ax4.set_title("Volume vs Return Magnitude", fontsize=18, fontweight="bold")
+ax4.tick_params(axis="both", labelsize=14)
+cbar = plt.colorbar(scatter, ax=ax4)
+cbar.ax.tick_params(labelsize=12)
+cbar.set_label("Return (%)", fontsize=14)
+ax4.grid(True, alpha=0.3, linestyle="--")
+
+# Main title
+fig.suptitle("subplot-grid · matplotlib · pyplots.ai", fontsize=24, fontweight="bold", y=0.98)
+
+plt.tight_layout(rect=[0, 0, 1, 0.95])
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/subplot-grid/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: subplot-grid
+created: '2025-12-30T17:48:50Z'
+updated: '2025-12-30T17:56:40Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602446975
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent financial dashboard example perfectly demonstrating the subplot-grid
+    concept with four distinct but related visualizations
+  - 'Creative color encoding: volume bars colored by return sign, scatter colored
+    by return value'
+  - Moving average overlay adds depth to the price chart
+  - Statistical annotations (mean, zero line) in histogram enhance educational value
+  - Clean, professional appearance with consistent styling across all subplots
+  weaknesses:
+  - Font sizes slightly below recommended guidelines (titles 18pt vs 24pt, labels
+    16pt vs 20pt)
+  - Legend fontsize could be larger for better readability (12-14 vs 16)