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feat(pygal): implement andrews-curves (#3143)
## Implementation: `andrews-curves` - pygal Implements the **pygal** version of `andrews-curves`. **File:** `plots/andrews-curves/implementations/pygal.py` **Parent Issue:** #2859 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20627536627)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/andrews-curves/implementations/pygal.py‎

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

‎plots/andrews-curves/metadata/pygal.yaml‎

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

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