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feat(highcharts): implement andrews-curves (#2906)
## Implementation: `andrews-curves` - highcharts Implements the **highcharts** version of `andrews-curves`. **File:** `plots/andrews-curves/implementations/highcharts.py` **Parent Issue:** #2859 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608527516)* --------- 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/highcharts.py‎

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""" pyplots.ai
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andrews-curves: Andrews Curves for Multivariate Data
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Library: highcharts unknown | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-31
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"""
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import tempfile
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import time
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import urllib.request
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from pathlib import Path
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import numpy as np
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from highcharts_core.chart import Chart
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from highcharts_core.options import HighchartsOptions
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from highcharts_core.options.series.area import LineSeries
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from selenium import webdriver
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from selenium.webdriver.chrome.options import Options
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import StandardScaler
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# Data - Iris dataset normalized
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iris = load_iris()
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X = StandardScaler().fit_transform(iris.data)
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y = iris.target
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species_names = ["Setosa", "Versicolor", "Virginica"]
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# Andrews curve transformation: f(t) = x1/sqrt(2) + x2*sin(t) + x3*cos(t) + x4*sin(2t) + ...
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t = np.linspace(-np.pi, np.pi, 200)
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def andrews_curve(x, t):
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"""Transform multivariate observation to Andrews curve"""
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n = len(x)
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result = x[0] / np.sqrt(2)
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for i in range(1, n):
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if i % 2 == 1:
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result += x[i] * np.sin((i // 2 + 1) * t)
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else:
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result += x[i] * np.cos((i // 2) * t)
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return result
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# Create chart
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chart = Chart(container="container")
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chart.options = HighchartsOptions()
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# Chart settings
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chart.options.chart = {
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"type": "line",
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"width": 4800,
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"height": 2700,
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"backgroundColor": "#ffffff",
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"marginBottom": 180,
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"marginTop": 120,
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"marginLeft": 150,
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"marginRight": 100,
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}
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# Title
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chart.options.title = {
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"text": "Iris Species · andrews-curves · highcharts · pyplots.ai",
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"style": {"fontSize": "56px", "fontWeight": "bold"},
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"y": 60,
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}
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# Axes
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chart.options.x_axis = {
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"title": {"text": "t (radians)", "style": {"fontSize": "40px"}, "margin": 30},
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"labels": {"style": {"fontSize": "32px"}, "y": 40},
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0,0,0,0.15)",
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"tickInterval": 1,
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"min": -3.15,
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"max": 3.15,
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}
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chart.options.y_axis = {
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"title": {"text": "f(t)", "style": {"fontSize": "40px"}, "margin": 30},
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"labels": {"style": {"fontSize": "32px"}, "x": -15},
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0,0,0,0.15)",
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"tickInterval": 1,
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}
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# Colors for species (colorblind-safe)
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colors = ["#306998", "#FFD43B", "#9467BD"]
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# Legend styling - positioned inside chart area to avoid clipping
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chart.options.legend = {
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"enabled": True,
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"itemStyle": {"fontSize": "36px", "fontWeight": "bold"},
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"symbolWidth": 60,
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"symbolHeight": 4,
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"align": "right",
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"verticalAlign": "top",
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"layout": "vertical",
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"x": -50,
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"y": 80,
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"itemDistance": 20,
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"itemMarginTop": 10,
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"backgroundColor": "rgba(255,255,255,0.9)",
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"borderWidth": 1,
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"borderColor": "#cccccc",
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"padding": 15,
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}
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# Plot options for transparency
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chart.options.plot_options = {
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"line": {"lineWidth": 3, "marker": {"enabled": False}, "animation": False},
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"series": {"animation": False},
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}
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# Generate curves for each observation (sample 20 per species for clarity)
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np.random.seed(42)
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samples_per_species = 20
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for species_idx in range(3):
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species_mask = y == species_idx
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species_X = X[species_mask]
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sample_indices = np.random.choice(len(species_X), min(samples_per_species, len(species_X)), replace=False)
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for i, idx in enumerate(sample_indices):
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curve_values = andrews_curve(species_X[idx], t)
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data_points = [[float(t[j]), float(curve_values[j])] for j in range(len(t))]
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series = LineSeries()
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series.data = data_points
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series.name = species_names[species_idx]
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series.color = colors[species_idx]
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series.opacity = 0.5
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series.show_in_legend = i == 0 # Only show first curve in legend
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series.line_width = 2
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chart.add_series(series)
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# Download Highcharts JS
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highcharts_url = "https://code.highcharts.com/highcharts.js"
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with urllib.request.urlopen(highcharts_url, timeout=30) as response:
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highcharts_js = response.read().decode("utf-8")
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# Generate HTML with inline script
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html_str = chart.to_js_literal()
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html_content = f"""<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<script>{highcharts_js}</script>
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</head>
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<body style="margin:0;">
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<div id="container" style="width: 4800px; height: 2700px;"></div>
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<script>{html_str}</script>
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</body>
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</html>"""
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# Write temp HTML and capture screenshot
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with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
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f.write(html_content)
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temp_path = f.name
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chrome_options = Options()
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chrome_options.add_argument("--headless")
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chrome_options.add_argument("--no-sandbox")
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chrome_options.add_argument("--disable-dev-shm-usage")
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chrome_options.add_argument("--disable-gpu")
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chrome_options.add_argument("--window-size=4900,2800")
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driver = webdriver.Chrome(options=chrome_options)
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driver.get(f"file://{temp_path}")
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time.sleep(5)
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# Screenshot the chart element for exact dimensions
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container = driver.find_element("id", "container")
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container.screenshot("plot.png")
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driver.quit()
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Path(temp_path).unlink()
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# Also save HTML for interactive version
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with open("plot.html", "w", encoding="utf-8") as f:
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f.write(html_content)

‎plots/andrews-curves/metadata/highcharts.yaml‎

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library: highcharts
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specification_id: andrews-curves
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created: '2025-12-31T00:08:21Z'
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updated: '2025-12-31T00:13:07Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20608527516
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issue: 2859
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python_version: 3.13.11
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library_version: unknown
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preview_url: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/highcharts/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/highcharts/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/andrews-curves/highcharts/plot.html
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quality_score: 92
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review:
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strengths:
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- Excellent colorblind-safe palette choice (blue/yellow/purple)
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- Clear cluster separation visible - Setosa distinctly separated from Versicolor/Virginica
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- Proper use of transparency (0.5 opacity) to show overlapping curve density
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- Well-implemented Andrews curve transformation following Fourier series formula
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- Good canvas utilization with appropriate margins
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- Interactive HTML version also generated
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- Normalized data using StandardScaler as recommended in spec
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weaknesses:
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- Helper function andrews_curve() deviates from pure KISS structure (minor)
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- Y-axis label f(t) could be more descriptive for general audience

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