The avatar for @spattana
Sumant Pattanaik
Published
Edited
Sep 13, 2021
1 star
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CAP 6737
Plotly slider use IPlotly slider usePlotly InteractionD3 Quick OverviewColor Solids: RGB and HSLHCL color modelCone Spectral Sensitivity PlotsColor Blind SimulatorColorBlindLibHSV and HSL Color ModelsColor from Wavelength, Correlated Temperature and HTML color pickerPlotly Color UsagePlotlyJS GeoVisualizationGeoVisualizationVisual Cluster AnalysisAssignment 4 Data and related computationWeather ForecastFetch Covid-19 dataWeb Scraping Table data from HTML file.
1D Dataviz in Plotly
US Baby NamesPlotly.Js: Getting Started with Old FaithfulData Fetch and Meta dataUseful Javascript Array functions.Useful D3 Functions for Data handlingIntroduction to Observable and PlotlyJSCAP 6737 Class Practice Notes
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Data Visualization Using D3 and Ploty.js
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md `## Strip/Jitter Plot.`
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new Array(oneDdata.length).fill(1)
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viewof jitterScale = slider()
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stripPlotData = {
const data = new Array(oneDdata.length).fill(0);
if ( jitter=="true") return data.map(d=>jitterScale*(Math.random()-0.5));
else return data;
}
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binSize = {
const xExtent = dataExtent;
return (xExtent[1]-xExtent[0])/nBins
}
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xAxisLabel = dim==='eruptions'?'Eruption Duration':'Waiting Time'
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dataExtent = d3.extent(oneDdata);
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oneDdata = extract(dim)
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densityDataLength = 100
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delta = (dataExtent[1]-dataExtent[0])/densityDataLength
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densityX = new Array(densityDataLength+1).fill(0).map((d,i)=>dataExtent[0]+i*delta)
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kernel = kernels[kernelname](bandWidth)
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densityY = kde(kernel,densityX,oneDdata)
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function kde(kernel, Xs, data) {
return Xs.map(x => d3.mean(data, d => kernel(Math.abs(x - d)/bandWidth)/bandWidth));
}
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oldFaithful = d3.csvParse(await FileAttachment("faithful.csv").text(), d3.autoType);
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extract = attribute=>oldFaithful.map(d=>d[attribute]);
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//import {epanechnikov} from "@d3/kernel-density-estimation"
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kernels = ({
cosine: () => (x => x <= 1 ? (Math.PI/4)*Math.cos(x*Math.PI/2): 0),
epanechnikov: () => (x => x <= 1 ? (3/4) * (1 - x * x) : 0),
quartic: () => (x => x <= 1 ? (15/16)*((1 - x * x)**2) : 0),
triweight: () => (x => x <= 1 ? (35/32)*((1 - x * x)**3) : 0),
tricube: () => (x => x <= 1 ? (35/32)*((1 - x**3)**3) : 0),
uniform:() => (x => x <= 1 ? 0.5 : 0),
triangle: () => (x => x <= 1 ? (1 - x): 0)
})
// Chosen Kernels with finite extent from https://en.wikipedia.org/wiki/Kernel_(statistics)
// Note all the kernels are symmetric and are zero outside the range -1 to + 1.
// So input to these functions are values that are translated and scaled so that it is zero at the peak value of the function.
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Plotly = require("plotly.js-dist") //require("https://cdn.plot.ly/plotly-latest.min.js")
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import {radio, slider} from "@jashkenas/inputs"
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md`## D3 Based visualization`
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