Public
Edited
Nov 25, 2022
Importers
1 star
spectrum = [{
"Heinrich Zollinger":{v:[400,450],b:[450,500],g:[500,540],y:[540,590],o:[590,650],r:[650,700]},
"Daniel Malacara":{v:[380,430],b:[430,500],c:[500,520],g:[520,565],y:[565,580],o:[580,625],r:[625,740]},
"Anne Marie Helmenstine":{v:[380,450],b:[450,495],g:[495,570],y:[570,590],o:[590,620],r:[620,750]},
"Britannica":{v:[400,450],b:[450,500],c:[500,550],g:[550,580],y:[580,600],o:[600,655],r:[650,700]},
"CRC Handbook":{v:[380,450],b:[450,485],c:[485,500],g:[500,565],y:[565,590],o:[590,625],r:[625,700]}
}]
ramp(d3.interpolateHslLong("violet", "red"))
ramp(d3.piecewise(d3.interpolateRgb, ["#7F00FF","#0000FF","#00FFFF"," #00FF00","#FFFF00","#FF8000","#FF0000"]))
ramp(d3.piecewise(d3.interpolateRgb, ["violet","blue","cyan","green","yellow","orange","red"]))
colors = d3.scaleLinear([380, 430, 500, 520, 565, 580, 625, 740], ["violet","blue","cyan","green","yellow","orange","red"]);
fullScale = d3.scaleLinear([356, 1050],[0, 100])
uvcScale = d3.scaleLinear([100,280],["black","indigo"])
makeDumbell("Satellite [Sensor]",900,1950,2021)
Inputs.table(data)
Type Markdown, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
dataResolution = {
let res = []
data.forEach(function(d,i){
let yearStart = ''
let yearEnd = 2012
if (d['Acquisition period']){
if (d['Acquisition period'].split('–')[1].trim() == 'Today'){
yearStart = d['Acquisition period'].split('–')[0]
yearEnd = 2012
} else {
yearStart = d['Acquisition period'].split('–')[0]
yearEnd = d['Acquisition period'].split('–')[1]
}
}
let text = ''
if (d['Sensor']){
text = " ["+d['Sensor']+"]"
}
res.push(
{
'name' : d['Satellite']+text,
'start': +d['Spectral Resolution(nm)(only VIS-VNIR are listed)'].split('–')[0],
'end' : +d['Spectral Resolution(nm)(only VIS-VNIR are listed)'].split('–')[1],
'respan':d['Pan'],
'resvis':d['VIS-NIR'],
'yearStart': yearStart,
'yearEnd': yearEnd
});
})
return res
}
range5 = makeRange("Ultraviolet & Visible & Infrared",900,[100,280],[280,315],[315,380],[380,430],[430,500],[500,520],[520,565],[565,580],[580,625],[625,740],[740,1400],[1400,10000])
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.