feat(seaborn): implement andrews-curves (#2898)

## Implementation: `andrews-curves` - seaborn

Implements the **seaborn** version of `andrews-curves`.

**File:** `plots/andrews-curves/implementations/seaborn.py`

**Parent Issue:** #2859

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608490654)*

---------

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

Author: github-actions[bot]

plots/andrews-curves/implementations/seaborn.py

@@ -0,0 +1,82 @@
+""" pyplots.ai
+andrews-curves: Andrews Curves for Multivariate Data
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Load iris dataset from seaborn
+df = sns.load_dataset("iris")
+
+# Normalize variables to similar scales
+features = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
+for col in features:
+    df[col + "_norm"] = (df[col] - df[col].mean()) / df[col].std()
+
+norm_features = [f + "_norm" for f in features]
+
+# Generate t values from -pi to pi
+t = np.linspace(-np.pi, np.pi, 200)
+
+# Compute Andrews curves for all observations
+# Andrews curve: f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + ...
+curves_data = []
+for idx, row in df.iterrows():
+    values = row[norm_features].values.astype(float)
+    # Compute Fourier series for this observation
+    curve_vals = np.full_like(t, values[0] / np.sqrt(2))
+    for i in range(1, len(values)):
+        if i % 2 == 1:
+            curve_vals = curve_vals + values[i] * np.sin((i + 1) // 2 * t)
+        else:
+            curve_vals = curve_vals + values[i] * np.cos(i // 2 * t)
+
+    for t_val, y_val in zip(t, curve_vals, strict=True):
+        curves_data.append({"t": t_val, "f(t)": y_val, "species": row["species"], "obs_id": idx})
+
+curves_df = pd.DataFrame(curves_data)
+
+# Color palette using Python Blue, Yellow, and a third color
+colors = {"setosa": "#306998", "versicolor": "#FFD43B", "virginica": "#E74C3C"}
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+sns.set_style("whitegrid")
+
+# Plot Andrews curves using lineplot with grouped data
+sns.lineplot(
+    data=curves_df,
+    x="t",
+    y="f(t)",
+    hue="species",
+    palette=colors,
+    alpha=0.4,
+    linewidth=1.5,
+    units="obs_id",
+    estimator=None,
+    ax=ax,
+)
+
+# Style the plot
+ax.set_xlabel("t", fontsize=20)
+ax.set_ylabel("f(t)", fontsize=20)
+ax.set_title("andrews-curves · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+
+# Set x-axis ticks to show pi values
+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)
+
+# Customize legend
+ax.legend(title="Species", fontsize=16, title_fontsize=18, loc="upper right")
+
+# Subtle grid
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/andrews-curves/metadata/seaborn.yaml

@@ -0,0 +1,26 @@
+library: seaborn
+specification_id: andrews-curves
+created: '2025-12-30T23:57:52Z'
+updated: '2025-12-31T00:05:28Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608490654
+issue: 2859
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of Andrews curves Fourier transformation algorithm
+  - Perfect use of seaborn lineplot with units parameter to plot individual curves
+    without aggregation
+  - Good choice of colorblind-safe color palette with appropriate transparency
+  - Clean separation of species clusters visible in the visualization
+  - Correct mathematical notation for x-axis labels using π symbols
+  - Well-structured code following KISS principles
+  weaknesses:
+  - Axis labels use mathematical notation only (t, f(t)) without descriptive context
+    for newcomers
+  - Legend could be positioned to avoid any potential overlap with curve endpoints