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francobollo
Applying GIS, Data Vis. and Deep Learning to environmental science and nuclear emergency responses
Public
Edited
Jan 10, 2024
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4 stars
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value = data.filter(aq.escape((d) => d.radionuclide === rdn)).array("concentration")
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depth = data.filter(aq.escape((d) => d.radionuclide === rdn)).array("depth")
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time = data.filter(aq.escape((d) => d.radionuclide === rdn)).array("time")
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formatTime = d3.timeFormat("%B %Y")
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binsTime = d3.bin().thresholds(nBins)(time)
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rangeTime = binsTime.map(
(d) => `${formatTime(d3.min(d))} - ${formatTime(d3.max(d))}`
)
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domainTime = binsTime.map((d) => d3.min(d)).slice(1)
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timeScale = d3.scaleThreshold().domain(domainTime).range(rangeTime)
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timeScale(new Date("1989-08-01"))
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binsDepth = d3.bin().thresholds(nBins)(depth)
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domainDepth = binsDepth.map((d) => d.x0).slice(1)
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rangeDepth = binsDepth.map((d) => `${d.x0}-${d.x1}`)
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depthScale = d3.scaleThreshold().domain(domainDepth).range(rangeDepth)
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formatDate = d3.timeFormat("%Y-%m-%d")
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world = (
await fetch("https://cdn.jsdelivr.net/npm/world-atlas@2/land-50m.json")
).json()
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land = topojson.feature(world, world.objects.land)
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// source: https://observablehq.com/@mkfreeman/plot-tooltip
import { Plot } from "@mkfreeman/plot-tooltip"
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