Applying GIS, Data Vis. and Deep Learning to environmental science and nuclear emergency responses
Public
Edited
Feb 2, 2024
1 star
Plot.plot({
x: { label: "K added (cmolc/kg) " },
y: { label: "K extracted (cmolc/kg) " },
color: { legend: true },
marks: [
Plot.dot(results, {
x: "spiked",
y: "extracted",
stroke: "smp",
tip: true
}),
Plot.linearRegressionY(results, {
x: "spiked",
y: "extracted",
stroke: "smp"
})
]
})
spectra["mid-infrared"]
results = [
{ spiked: 0.1, extracted: 0.120278, smp: "LUI" },
{ spiked: 1, extracted: 0.557428, smp: "LUI" },
{ spiked: 10, extracted: 8.808574, smp: "LUI" },
{ spiked: 0.1, extracted: 1.752982, smp: "SPA" },
{ spiked: 1, extracted: 2.544179, smp: "SPA" },
{ spiked: 10, extracted: 9.720309, smp: "SPA" },
{ spiked: 0.1, extracted: 0.58839, smp: "TM" },
{ spiked: 1, extracted: 1.123894, smp: "TM" },
{ spiked: 10, extracted: 8.541135, smp: "TM" }
]
mir@3.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
mir-dwt@7.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
nir-dwt@8.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
nir@5.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
nirSelected = nir.filter((d) => d.smp_name.includes(smp))
mirSelected = mir.filter((d) => d.smp_name.includes(smp))
spectra = ({ "mid-infrared": mirSelected, "near-infrared": nirSelected })
nirDWTSelected = nirDWT.filter((d) => d.smp_name.includes(smp))
mirDWTSelected = mirDWT.filter((d) => d.smp_name.includes(smp))
dwt = ({ "mid-infrared": mirDWTSelected, "near-infrared": nirDWTSelected })
levels = new Set(mirDWTSelected.map((d) => d.level))
resolutions = Array.from(new Set(dwt[scanType].map((d) => d.resolution)))
resolutions[-1]
Array.from(new Set(spectra[scanType].map((d) => d.wavenumber)))
new Set(spectra["mid-infrared"].map((d) => d.wavenumber))
function getWavenumbers(data, scanType) {
const wavenumbers = Array.from(new Set(data.map((d) => d.wavenumber)));
return scanType == "mid-infrared" ? wavenumbers : wavenumbers.reverse();
}
getWavenumbers(spectra["mid-infrared"], "mid-infrared")
import { interval } from "@mootari/range-slider@1781"
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.
