feat(pygal): implement andrews-curves

plots/andrews-curves/implementations/pygal.py

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+""" pyplots.ai
+andrews-curves: Andrews Curves for Multivariate Data
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 88/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Generate synthetic Iris-like data (4 features, 3 species)
+np.random.seed(42)
+
+# Simulate sepal length, sepal width, petal length, petal width for 3 species
+# Species 1: Setosa - small petals, medium sepals
+setosa = np.column_stack(
+    [
+        np.random.normal(5.0, 0.35, 50),  # sepal length
+        np.random.normal(3.4, 0.38, 50),  # sepal width
+        np.random.normal(1.5, 0.17, 50),  # petal length
+        np.random.normal(0.2, 0.10, 50),  # petal width
+    ]
+)
+
+# Species 2: Versicolor - medium petals and sepals
+versicolor = np.column_stack(
+    [
+        np.random.normal(5.9, 0.52, 50),  # sepal length
+        np.random.normal(2.8, 0.31, 50),  # sepal width
+        np.random.normal(4.3, 0.47, 50),  # petal length
+        np.random.normal(1.3, 0.20, 50),  # petal width
+    ]
+)
+
+# Species 3: Virginica - large petals and sepals
+virginica = np.column_stack(
+    [
+        np.random.normal(6.6, 0.64, 50),  # sepal length
+        np.random.normal(3.0, 0.32, 50),  # sepal width
+        np.random.normal(5.5, 0.55, 50),  # petal length
+        np.random.normal(2.0, 0.27, 50),  # petal width
+    ]
+)
+
+# Combine data
+X = np.vstack([setosa, versicolor, virginica])
+y = np.array([0] * 50 + [1] * 50 + [2] * 50)
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+# Normalize variables (z-score standardization)
+X_mean = X.mean(axis=0)
+X_std = X.std(axis=0)
+X_scaled = (X - X_mean) / X_std
+
+# Andrews curve function: f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + ...
+t_values = np.linspace(-np.pi, np.pi, 100)
+
+# Colors for 3 species - colorblind-safe palette (blue, orange, purple)
+species_colors = ("#306998", "#E67E22", "#9B59B6")
+n_curves_per_species = 15
+
+# Custom style for large canvas with increased font sizes for readability
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=species_colors,
+    title_font_size=96,
+    label_font_size=64,
+    major_label_font_size=56,
+    legend_font_size=64,
+    value_font_size=48,
+    stroke_width=2,
+    opacity=0.4,
+    opacity_hover=0.8,
+    tooltip_font_size=48,
+)
+
+# Create XY chart with interactive features
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="andrews-curves · pygal · pyplots.ai",
+    x_title="t (radians)",
+    y_title="f(t)",
+    show_dots=False,
+    stroke_style={"width": 2},
+    show_x_guides=True,
+    show_y_guides=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=3,
+    legend_box_size=32,
+    truncate_legend=-1,
+    tooltip_border_radius=10,
+    js=["https://kozea.github.io/pygal.js/2.0.x/pygal-tooltips.min.js"],
+)
+
+# Plot curves for each species - group all curves into single series per species
+for species_idx in range(3):
+    species_mask = y == species_idx
+    species_data = X_scaled[species_mask]
+    original_data = X[species_mask]
+
+    # Sample curves per species for clarity
+    indices = np.random.choice(len(species_data), n_curves_per_species, replace=False)
+
+    # Collect all points for this species into a single series
+    all_points = []
+    for curve_num, idx in enumerate(indices):
+        row = species_data[idx]
+        orig = original_data[idx]
+        # Andrews transform: f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t)
+        curve_values = (
+            row[0] / np.sqrt(2) + row[1] * np.sin(t_values) + row[2] * np.cos(t_values) + row[3] * np.sin(2 * t_values)
+        )
+        # Create points with metadata for interactive tooltips
+        tooltip = (
+            f"{species_names[species_idx]}: Sepal {orig[0]:.1f}×{orig[1]:.1f}cm, Petal {orig[2]:.1f}×{orig[3]:.1f}cm"
+        )
+        points = [
+            {"value": (float(t), float(v)), "label": tooltip} for t, v in zip(t_values, curve_values, strict=True)
+        ]
+        all_points.extend(points)
+        # Add None to create a break between curves (discontinuity)
+        if curve_num < len(indices) - 1:
+            all_points.append(None)
+
+    # Add single series per species - clean legend with only 3 entries
+    chart.add(species_names[species_idx], all_points, show_dots=False)
+
+# Save outputs
+chart.render_to_file("plot.html")
+chart.render_to_png("plot.png")

plots/andrews-curves/metadata/pygal.yaml

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+library: pygal
+specification_id: andrews-curves
+created: '2025-12-31T21:35:14Z'
+updated: '2025-12-31T21:53:53Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20627536627
+issue: 2859
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/pygal/plot.html
+quality_score: 88
+review:
+  strengths:
+  - Excellent colorblind-safe color palette (blue/orange/purple)
+  - Proper data normalization (z-score standardization) as recommended in spec
+  - Interactive tooltips showing original iris measurements for each curve
+  - Clean legend with only 3 entries (one per species) rather than per-curve
+  - Correct Andrews curve formula implementation
+  - Good use of pygal custom Style for large canvas sizing
+  weaknesses:
+  - Grid lines could be slightly more subtle (alpha/opacity could be reduced)
+  - Some individual curves are hard to trace due to overlapping in the middle region