feat(matplotlib): implement ridgeline-basic

plots/ridgeline-basic/implementations/matplotlib.py

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+"""
+ridgeline-basic: Ridgeline Plot
+Library: matplotlib
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Monthly temperature distributions
+np.random.seed(42)
+months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+# Generate temperature data with seasonal variation
+temperature_data = []
+base_temps = [2, 4, 8, 13, 17, 21, 24, 23, 19, 13, 7, 3]
+for i in range(len(months)):
+    temperature_data.append(np.random.normal(base_temps[i], 4, 200))
+
+# Color palette - gradient from cool to warm
+colors = [
+    "#306998",
+    "#3A7CA5",
+    "#4A90B2",
+    "#5BA4BF",
+    "#7BB8C9",
+    "#9CCCD3",
+    "#FFD43B",
+    "#FFCC33",
+    "#F9B926",
+    "#F9A619",
+    "#F97316",
+    "#DC2626",
+]
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# X-axis range for KDE
+x_range = np.linspace(-10, 35, 500)
+
+# KDE bandwidth (Silverman's rule-of-thumb)
+bandwidth = 2.0
+
+# Overlap factor and scale
+overlap = 0.25
+scale = 0.85
+
+# Plot each distribution from top to bottom
+for i, month in enumerate(reversed(months)):
+    idx = len(months) - 1 - i
+    data = temperature_data[idx]
+
+    # Compute Gaussian KDE (inline, no scipy)
+    n = len(data)
+    density = np.zeros_like(x_range)
+    for d in data:
+        density += np.exp(-0.5 * ((x_range - d) / bandwidth) ** 2)
+    density /= n * bandwidth * np.sqrt(2 * np.pi)
+
+    # Normalize density
+    density = density / density.max() * scale
+
+    # Vertical offset
+    offset = i * overlap
+
+    # Plot filled density
+    ax.fill_between(x_range, offset, density + offset, alpha=0.7, color=colors[idx], edgecolor="white", linewidth=1.5)
+
+    # Add month label
+    ax.text(-12, offset + 0.15, month, fontsize=16, fontweight="bold", va="center", ha="right")
+
+# Styling
+ax.set_xlabel("Temperature (°C)", fontsize=20)
+ax.set_title("Monthly Temperature Distribution", fontsize=20, fontweight="bold", pad=20)
+
+# Clean up axes
+ax.set_xlim(-15, 35)
+ax.set_ylim(-0.1, len(months) * overlap + scale + 0.1)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+ax.set_yticks([])
+ax.tick_params(axis="x", labelsize=16)
+
+# Add subtle grid on x-axis only
+ax.xaxis.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")