Public
Edited
Jun 21, 2023
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// radarLine = d3.lineRadial()
// .curve(d3["curveLinear"])
// .radius(d => rScale(d))
// .angle((d, i) => -angleSlice*i)
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// rScale = d3.scaleLinear()
// .domain([0, maxValue])
// .range([0, radius])
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// axisLabelFactor = 1.12
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// wrapWidth = 60
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// dotRadius = 4
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// radius = (height-(margin*2)) / 2
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// height = 600
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// margin = 30
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viewof table_Atlas = Inputs.table(recom_data_Atlas['numerical'], {
// sort: "TOTAL bias (selected)",
columns: [
"Bias Dimension",
"Value",
"Difference"
],
header: {
"Bias Dimension": "Bias Dimension",
"Value": "Value",
"Difference": "Difference"
},

format: {
"Bias Dimension": d => Object.keys(d),
}
})
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recom_data_Atlas['numerical']
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recom_data_Atlas = Object({
numerical: Object.values(data_fetched_numerical[0])[0],
canonical: data_fetched_canonical['RIPE Atlas']
})
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// data_fetched_numerical = await fetch("https://ai4netmon.csd.auth.gr/api/bias/cause/numerical").then((response) => response.json())
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// data_fetched_canonical = await fetch("https://ai4netmon.csd.auth.gr/api/bias/cause/categorical").then((response) => response.json())
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Purpose-built for displays of data

Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
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