Applying GIS, Data Vis. and Deep Learning to environmental science and nuclear emergency responses
Public
Edited
May 15, 2024
formatter = d3.format(".2e")
formatter(0.0000234)
timeValueAgg = Plot.plot({
color: {
scheme: "orrd",
type: "log"
},
y: {
type: "linear",
// tickFormat: "~s",
tickFormat: ".2e",
label: "Concentration (Sv/s) ↑"
},
x: { tickFormat: Plot.formatAuto },
marks: [
Plot.rect(
dataSelected.filter((d) => d.StartTime > fromDateTime),
Plot.bin(
{ fill: "Count" },
{ x: "StartTime", y: "Value", thresholds: 40 }
)
)
],
marginLeft: 80,
height: 400,
width: 600
})
Plot.plot({
// x: {
// type: "utc"
// },
y: {
grid: true,
inset: 6
},
marks: [
Plot.boxY(
dataSelected.filter((d) => d.StartTime > fromDateTime),
{ x: "StartTime", y: "Value" }
)
]
})
hexTimeChart = Plot.plot({
// width: width,
x: {
type: "utc"
},
color: { scheme: "OrRd" },
y: {
type: yScaleType,
tickFormat: "~s",
label: "Dose rate ↑",
// grid: true,
inset: 6
},
marks: [
Plot.dot(
dataSelected.filter((d) => d.StartTime > fromDateTime),
Plot.hexbin(
// { fill: "count", r: "count" },
{ fill: "count" },
{
x: "StartTime",
y: "Value",
symbol: "hexagon",
fillOpacity: 0.9,
stroke: "black",
strokeWidth: 0.5,
strokeOpacity: 0.8,
binWidth: 7
}
)
)
]
})
timeChart = Plot.plot({
style: "overflow: visible;",
y: { grid: true, type: "linear", tickFormat: "~s", label: "Dose rate ↑" },
marks: [
Plot.ruleY([0]),
Plot.lineY(
dataSelected.filter((d) => d.StartTime > fromDateTime),
{
x: "StartTime",
y: "Value",
stroke: "Name",
sort: "StartTime",
tip: "x"
}
),
Plot.dot(
dataSelected.filter((d) => d.StartTime > fromDateTime),
{
x: "StartTime",
y: "Value",
stroke: "Name",
sort: "StartTime",
fill: "Name",
r: 2,
tip: "x"
}
)
// Plot.text(
// dataSelected,
// Plot.selectLast({
// x: "StartTime",
// y: "Value",
// z: "Name",
// text: "Name",
// textAnchor: "start",
// dx: 5
// })
// )
]
})
Plot.plot({
width: width,
tip: true,
y: { grid: true, type: yScaleType, tickFormat: "~s", label: "Dose rate ↑" },
// x: { type: "time" },
// color: { legend: true },
fx: {
interval: d3.utcHour.every(2),
// tickFormat: (d) => `${d.getUTCFullYear()}’s`,
label: null
},
marks: [
Plot.dot(
dataSelected.filter((d) => d.StartTime > fromDateTime),
Plot.dodgeX("middle", {
fx: "StartTime",
y: "Value",
r: 1,
opacity: 1,
tip: "x"
})
)
]
})
// histogram = Plot.plot({
// x: {
// type: "linear",
// tickFormat: "~s",
// label: "Dose rate"
// },
// marks: [
// Plot.rectY(
// dataSelected,
// Plot.binX({ y: "count" }, { x: "Value", tip: true, thresholds: 20 })
// ),
// Plot.ruleY([0])
// ]
// })
Plot.plot({
y: {
type: "linear",
tickFormat: "~s",
label: "Dose rate"
},
fx: {
interval: d3.utcDay.every(1),
// tickFormat: (d) => `${d.getUTCFullYear()}’s`,
label: null
},
// fx: {
// transform: (d) => timeScale(d),
// label: "Time →",
// reverse: false,
// domain: rangeTime
// },
// facet: {
// data: dataSelected,
// // y: "depth",
// x: "StartTime",
// marginLeft: 100
// },
marks: [
Plot.rectX(
dataSelected,
Plot.binY(
{ x: "count" },
{ y: "Value", tip: true, thresholds: 20, facet: "include" }
)
),
Plot.ruleX([0])
]
})
time = dataSelected.map((d) => d.StartTime)
// binsTime = d3.bin()(time)
// formatTime = d3.timeFormat("%Y-%m-%dT%H:%M:%S.%LZ")
formatTime = d3.timeFormat("%dT%H:%M:%S.%LZ")
// rangeTime = binsTime.map(
// (d) => `${formatTime(d3.min(d))} - ${formatTime(d3.max(d))}`
// )
binSize = 72 * 60 * 60 * 1000 // 1 hour in milliseconds
minTime = time.reduce((min, time) => Math.min(min, time), Infinity)
maxTime = time.reduce((max, time) => Math.max(max, time), -Infinity)
// Generate bins using d3.bin
binsTime = d3
.bin()
.domain([minTime, maxTime])
.thresholds(d3.range(minTime, maxTime + binSize, binSize))(time)
rangeTime = binsTime.map(
(d) => `${formatTime(d3.min(d))} - ${formatTime(d3.max(d))}`
)
domainTime = binsTime.map((d) => d3.min(d)).slice(1)
timeScale = d3.scaleThreshold().domain(domainTime).range(rangeTime)
Plot.plot({
height: 4000,
axis: null,
y: { domain: [0, step] },
color: { scheme: "YlGnBu" },
facet: { data: dataSelected, y: "Name" },
marks: [
d3.range(bands).map((i) =>
Plot.areaY(dataSelected, {
x: "StartTime",
y: (d) => d.Value - i * step,
fill: i,
clip: true,
sort: "StartTime"
})
),
Plot.text(
dataSelectedScaled,
Plot.selectFirst({
text: "Name",
frameAnchor: "top-left",
dx: 6,
dy: 6
})
),
Plot.frame()
]
})
dataSelectedScaled = dataSelected.map((d) => ({
...d,
ValueScaled: d.Value * Math.pow(10, 12)
}))
bands = 7
step = d3.max(dataSelected, (d) => d.Value) / bands
dataSelected = data.filter((d) =>
turf.booleanWithin(turf.point([d.Longitude, d.Latitude]), circle)
)
dataSelected.map((d) => d.EndTime)
features = turf.featureCollection(data.map((d) => turf.point([d.Longitude, d.Latitude])))
bbox = turf.envelope(features)
circle = d3
.geoCircle()
.center(center)
.radius((kms / (6371 * Math.PI * 2)) * 360)
.precision(2)()
// locations = getLocations(parsed)
// data = getData(parsed)
data
.map((d) => String(d.EndTime))
.filter((value, index, self) => self.indexOf(value) === index)
parsed = parse(fr_irsnfr_202307032200_doserates)
metadata = parsed.Report.Identification
import { parse, getData, getLocations } from "@francobollo/irix-parser"
turf = require("@turf/turf@6")
world = (
await fetch("https://cdn.jsdelivr.net/npm/world-atlas@2/land-50m.json")
).json()
data = _.flatten(
[
fr_irsnfr_202307040800_doserates,
fr_irsnfr_202307040700_doserates,
fr_irsnfr_202307040600_doserates,
fr_irsnfr_202307040500_doserates,
fr_irsnfr_202307040400_doserates,
fr_irsnfr_202307040300_doserates,
fr_irsnfr_202307040200_doserates1,
fr_irsnfr_202307040100_doserates
].map((d) => getData(parse(d)))
)
locations = _.flatten(
[
fr_irsnfr_202307040800_doserates,
fr_irsnfr_202307040700_doserates,
fr_irsnfr_202307040600_doserates,
fr_irsnfr_202307040500_doserates,
fr_irsnfr_202307040400_doserates,
fr_irsnfr_202307040300_doserates,
fr_irsnfr_202307040200_doserates1,
fr_irsnfr_202307040100_doserates
].map((d) => getLocations(parse(d)))
)
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.
