Published unlisted
Edited
May 20, 2021
Plot.plot({
padding: 0,
x: XAxisFormat,
y: YAxisFormat,
color: PlotColor,
marks: [
Plot.cell(
AggregatedData,
{
x: (d) => d3.utcDay(d.date),
y: (d) => d3.utcMinute.count(d3.utcDay(d.date), d.date) / TimeResolution,
fill: (d) => d.count,
//inset: 0.5
}
)
],
style: {
//background: (PlotColor.type === "diverging" ? "lightgrey" : undefined)
}
})
StartDate = new Date(Date.UTC(2017, 0, 1))
Url = "https://herringrunner.s3.us-east-1.amazonaws.com/detections_summary.csv.gz"
Data = d3.csvParseRows(await fetch(Url).then((r) => r.text()), (d) => {
d = d.map((x) => +(x || 0)) // coerce everything to numeric
const date = new Date(d[0] * 1000) // convert Unix timestamp to Date
let counts = d.slice(1).concat(Array(10).fill(0)).slice(0, 10) // counts per confidence level
counts = d3.cumsum(counts.reverse()).reverse()
return [date, ...counts]
})
AggregatedData = {
const thresh = Math.trunc((ConfidenceThreshold - 50) / 5)
return Array.from(d3.rollup(
// Filter data to exclude frames with too few detections
d3.filter(Data, (d) => d[thresh + 1] >= DetectionThreshold),
// Aggregate all frames within the group
(v) => ({
date: TimeInterval.floor(v[0][0]), // start of time slice
count: v.length, // number of frames
// Note: for number of _fish_, sum across v[i][thresh+1]
}),
// Form groups based on the timestamp
(d) => TimeInterval.floor(d[0])
), ([key, value]) => value /* discard grouping keys */)
}
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