feat(altair): implement andrews-curves (#2896)

## Implementation: `andrews-curves` - altair

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

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

**Parent Issue:** #2859

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608497564)*

---------

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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/altair.py

@@ -0,0 +1,88 @@
+""" pyplots.ai
+andrews-curves: Andrews Curves for Multivariate Data
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
+
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+# Load and prepare data
+iris = load_iris()
+X = iris.data
+y = iris.target
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+# Normalize variables to similar scales
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Andrews curve transformation
+# f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + x5*cos(2t) + ...
+n_points = 100
+t = np.linspace(-np.pi, np.pi, n_points)
+
+# Compute Andrews curves for each observation
+curves_data = []
+for obs_idx in range(len(X_scaled)):
+    x = X_scaled[obs_idx]
+    curve = np.zeros(n_points)
+    curve += x[0] / np.sqrt(2)
+
+    for i in range(1, len(x)):
+        freq = (i + 1) // 2
+        if i % 2 == 1:
+            curve += x[i] * np.sin(freq * t)
+        else:
+            curve += x[i] * np.cos(freq * t)
+
+    for pt_idx in range(n_points):
+        curves_data.append(
+            {"t": t[pt_idx], "value": curve[pt_idx], "observation": obs_idx, "species": species_names[y[obs_idx]]}
+        )
+
+df = pd.DataFrame(curves_data)
+
+# Define colors for species
+species_colors = ["#306998", "#FFD43B", "#6B8E23"]
+
+# Create chart
+chart = (
+    alt.Chart(df)
+    .mark_line(opacity=0.4, strokeWidth=2)
+    .encode(
+        x=alt.X("t:Q", title="t (radians)", axis=alt.Axis(labelFontSize=18, titleFontSize=22)),
+        y=alt.Y("value:Q", title="Andrews Curve Value", axis=alt.Axis(labelFontSize=18, titleFontSize=22)),
+        color=alt.Color(
+            "species:N",
+            title="Species",
+            scale=alt.Scale(domain=["Setosa", "Versicolor", "Virginica"], range=species_colors),
+            legend=alt.Legend(titleFontSize=22, labelFontSize=20),
+        ),
+        detail="observation:N",
+    )
+    .properties(
+        width=1600,
+        height=900,
+        title=alt.Title(
+            "Iris Classification · andrews-curves · altair · pyplots.ai",
+            fontSize=28,
+            subtitle="Fourier series transformation of multivariate data for visual pattern comparison",
+            subtitleFontSize=18,
+        ),
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22)
+    .configure_view(strokeWidth=0)
+    .configure_title(fontSize=28)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/andrews-curves/metadata/altair.yaml

@@ -0,0 +1,28 @@
+library: altair
+specification_id: andrews-curves
+created: '2025-12-30T23:57:49Z'
+updated: '2025-12-31T00:17:13Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608497564
+issue: 2859
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/altair/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Correct Fourier series implementation for Andrews curves transformation
+  - Clear visual separation between iris species demonstrating the clustering capability
+  - Proper data normalization using StandardScaler as recommended in spec
+  - Clean, declarative Altair code with appropriate encoding types
+  - Title format follows the required spec-id · library · pyplots.ai pattern with
+    informative subtitle
+  - Good use of transparency (opacity=0.4) for overlapping curves as recommended
+  weaknesses:
+  - Legend could benefit from a semi-transparent background for better contrast against
+    the curves
+  - Yellow color (#FFD43B) could be slightly less bright for better visibility on
+    white background
+  - Could add tooltips to enable interactive exploration of individual observations