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feat(highcharts): implement cat-strip (#2769)
## Implementation: `cat-strip` - highcharts Implements the **highcharts** version of `cat-strip`. **File:** `plots/cat-strip/implementations/highcharts.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20601060796)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/cat-strip/implementations/highcharts.py‎

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""" pyplots.ai
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cat-strip: Categorical Strip Plot
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Library: highcharts unknown | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-30
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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.scatter import ScatterSeries
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from selenium import webdriver
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from selenium.webdriver.chrome.options import Options
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# Data - Plant growth measurements across different fertilizer types
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np.random.seed(42)
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categories = ["Control", "Fertilizer A", "Fertilizer B", "Fertilizer C"]
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n_per_category = 25
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data_by_category = {}
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# Control: lower growth, moderate spread
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data_by_category["Control"] = np.random.normal(loc=15, scale=3, size=n_per_category)
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# Fertilizer A: moderate growth, tight spread
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data_by_category["Fertilizer A"] = np.random.normal(loc=22, scale=2, size=n_per_category)
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# Fertilizer B: high growth, wider spread with some outliers
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data_by_category["Fertilizer B"] = np.concatenate(
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[
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np.random.normal(loc=28, scale=4, size=n_per_category - 3),
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np.array([38, 40, 12]), # outliers
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]
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)
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# Fertilizer C: moderate-high growth, bimodal distribution
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data_by_category["Fertilizer C"] = np.concatenate(
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[
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np.random.normal(loc=20, scale=2, size=n_per_category // 2),
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np.random.normal(loc=30, scale=2, size=n_per_category - n_per_category // 2),
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]
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)
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# Colors for each category
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colors = ["#306998", "#FFD43B", "#9467BD", "#17BECF"]
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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 configuration
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chart.options.chart = {
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"type": "scatter",
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"width": 4800,
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"height": 2700,
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"backgroundColor": "#ffffff",
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"marginBottom": 250,
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"spacingBottom": 50,
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}
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# Title
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chart.options.title = {
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"text": "cat-strip · highcharts · pyplots.ai",
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"style": {"fontSize": "48px", "fontWeight": "bold"},
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}
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# X-axis (categories)
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chart.options.x_axis = {
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"categories": categories,
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"title": {"text": "Treatment Group", "style": {"fontSize": "36px"}},
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"labels": {"style": {"fontSize": "28px"}},
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0, 0, 0, 0.1)",
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}
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# Y-axis (values)
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chart.options.y_axis = {
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"title": {"text": "Plant Height (cm)", "style": {"fontSize": "36px"}},
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"labels": {"style": {"fontSize": "28px"}},
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"gridLineWidth": 1,
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"gridLineColor": "rgba(0, 0, 0, 0.1)",
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}
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# Legend - position at top right for better visibility
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chart.options.legend = {
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"enabled": True,
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"itemStyle": {"fontSize": "32px"},
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"symbolHeight": 20,
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"symbolWidth": 20,
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"symbolRadius": 10,
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"layout": "vertical",
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"align": "right",
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"verticalAlign": "top",
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"x": -50,
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"y": 80,
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"borderWidth": 1,
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"borderColor": "#cccccc",
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"backgroundColor": "#ffffff",
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"padding": 15,
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}
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# Plot options
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chart.options.plot_options = {
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"scatter": {
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"marker": {"radius": 14, "symbol": "circle"},
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"jitter": {"x": 0.2, "y": 0}, # Highcharts built-in jitter
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}
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}
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# Add series for each category
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for i, (cat, values) in enumerate(data_by_category.items()):
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series = ScatterSeries()
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# For jitter to work with categories, we use category index as x
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series.data = [[i, float(v)] for v in values]
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series.name = cat
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series.color = colors[i]
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series.marker = {"fillColor": colors[i], "lineWidth": 2, "lineColor": "#ffffff"}
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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 scripts
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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 take 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=4800,2700")
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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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driver.save_screenshot("plot.png")
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driver.quit()
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Path(temp_path).unlink()
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# 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/cat-strip/metadata/highcharts.yaml‎

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library: highcharts
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specification_id: cat-strip
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created: '2025-12-30T16:31:47Z'
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updated: '2025-12-30T16:44:06Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20601060796
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issue: 0
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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/cat-strip/highcharts/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/cat-strip/highcharts/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/cat-strip/highcharts/plot.html
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quality_score: 91
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review:
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strengths:
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- Excellent use of Highcharts built-in jitter feature for proper strip plot behavior
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- 'Thoughtful data design showing different distribution characteristics: tight
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vs wide spread, outliers, bimodal patterns'
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- Colorblind-safe palette following library guidelines
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- Clean rendering with appropriate font sizes for high-resolution output
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- White marker borders add visual clarity and separation between overlapping points
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weaknesses:
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- Legend position in top-right corner encroaches on the data visualization area
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- Points lack alpha/transparency which would help visualize density in overlapping
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areas

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