feat(matplotlib): implement andrews-curves

plots/andrews-curves/implementations/matplotlib.py

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+""" pyplots.ai
+andrews-curves: Andrews Curves for Multivariate Data
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+
+
+# Load and prepare data
+iris = load_iris()
+X = iris.data
+y = iris.target
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+# Normalize data to prevent dominant variables
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Generate t values from -π to π
+t = np.linspace(-np.pi, np.pi, 200)
+
+# Build Andrews curve transformation matrix
+# f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + ...
+n_features = X_scaled.shape[1]
+basis = np.zeros((len(t), n_features))
+basis[:, 0] = 1 / np.sqrt(2)
+for i in range(1, n_features):
+    freq = (i + 1) // 2
+    if i % 2 == 1:
+        basis[:, i] = np.sin(freq * t)
+    else:
+        basis[:, i] = np.cos(freq * t)
+
+# Compute all Andrews curves: each row of X_scaled dot basis.T gives one curve
+curves = X_scaled @ basis.T  # shape: (150, 200)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Colors for each species (Python Blue, Python Yellow, and a complementary color)
+colors = ["#306998", "#FFD43B", "#E06C75"]
+
+# Plot Andrews curves for each observation
+for i in range(len(curves)):
+    ax.plot(t, curves[i], color=colors[y[i]], alpha=0.4, linewidth=1.5)
+
+# Create legend with sample lines
+for idx, species in enumerate(species_names):
+    ax.plot([], [], color=colors[idx], linewidth=3, label=species, alpha=0.8)
+
+# Styling
+ax.set_xlabel("t (radians)", fontsize=20)
+ax.set_ylabel("f(t)", fontsize=20)
+ax.set_title("andrews-curves · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=16, loc="upper right")
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Set x-axis ticks at meaningful positions
+ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
+ax.set_xticklabels(["-π", "-π/2", "0", "π/2", "π"], fontsize=16)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/andrews-curves/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: andrews-curves
+created: '2025-12-30T23:55:55Z'
+updated: '2025-12-31T00:02:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608490813
+issue: 2859
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of Andrews curves algorithm using Fourier basis functions
+  - Clean visualization that clearly shows Setosa species separation from Versicolor/Virginica
+  - Proper normalization with StandardScaler prevents dominant variables
+  - Appropriate transparency (alpha=0.4) for 150 overlapping curves reveals density
+    patterns
+  - X-axis uses mathematically meaningful tick labels (-π, -π/2, 0, π/2, π)
+  weaknesses:
+  - Missing np.random.seed(42) in code (though Iris dataset is deterministic, best
+    practice is to include it)
+  - Linewidth=1.5 is slightly below recommended 2-4 for high-resolution plots per
+    library guidelines
+  - Legend sample lines use alpha=0.8 while actual curves use alpha=0.4, creating
+    visual inconsistency