2e:null 2f:["$","title",null,{"children":"One dimensional data comparison | Chathura Jayalath | Observable"}] 34:[] 32:{"id":"5be3d9e0c7b7cc85","slug":"my-first-script","title":"One dimensional data comparison","thumbnail":"ebc3a0c66d22a069f835c91745350a29f1f38283b507c42d2e6f5052687fcbbe","access_level":"public","create_time":"$D2020-08-27T14:38:32.532Z","update_time":"$D2020-09-08T01:39:11.496Z","view_time":null,"first_public_time":"$D2020-09-04T15:19:06.815Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"faa67bfd1359f951","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":"starter_2024"},"version":580,"publish_version":580,"role":null,"authored":false,"authors":[{"type":"individual","id":"5fe81dea2f61079a","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":null}],"members":[{"type":"individual","id":"5fe81dea2f61079a","login":"deamonpog","name":"Chathura Jayalath","avatar_url":"https://avatars-next.observableusercontent.com/1bc13a0724d43e94c26de301357a2897d22e5c4e01e7f160ea8fba232ef861f1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"One dimensional data comparison","cells":[{"id":0,"value":"md`# One dimensional data comparison`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"md`---\n## Importing Libraries`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"d3 = require(\"d3\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"md`---\n## Importing Test Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"oldFaithful = d3.tsv(\"https://gist.githubusercontent.com/mbostock/e3f4376d54e02d5d43ae32a7cf0e6aa9/raw/dcb23e8f6eefdbc4ada97d6eda22b2a4f256c263/faithful.tsv\", d3.autoType)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":56,"value":"oldFaithful.columns","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"extract = attribute=>oldFaithful.map(d=>d[attribute])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"eruptions = extract('eruptions')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"waiting = extract('waiting')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"maxWaiting = d3.max(waiting)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"minWaiting = d3.min(waiting)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"stripPlotY = stripLength => Array(stripLength).fill(0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"md`---\n## Plot Generation`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":500,"value":"md`### Histogram`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"{\n let numOfBins = 20\n let histData = waiting\n \n let trace1 = {\n x:histData,\n type:'histogram',\n xbins: {size: (d3.max(histData) - d3.min(histData)) / (numOfBins - 1)},\n marker: {\n color: 'green',\n line: {\n color: \"black\", \n width: 1\n }\n\t }\n }\n const div = DOM.element('div');\n Plotly.newPlot(div, [trace1])\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":505,"value":"md`### Strip plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"{\n let trace1 = {\n x:waiting,\n y:stripPlotY(waiting.length),\n type:'scatter',\n mode:'markers',\n marker: { color: 'rgba(17, 157, 255,0.5)', size: 5}\n }\n const div = DOM.element('div');\n Plotly.newPlot(div, [trace1])\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"md`### Jitter plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":519,"value":"jitterPlotY = (jitterLength, jitterMax, jitterMin) => Array(jitterLength).fill(1).map(x => Math.random() * (jitterMax - jitterMin) + jitterMin)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":104,"value":"{\n let trace1 = {\n x:waiting,\n y:jitterPlotY(waiting.length, 1,-1),\n type:'scatter',\n mode:'markers',\n marker: { color: 'rgba(17, 157, 255,0.5)', size: 5}\n }\n const div = DOM.element('div');\n Plotly.newPlot(div, [trace1])\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":524,"value":"md`### Kernal Density plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"md`#### KDE generation algorithm`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"function generateKernalDensity(inputArray, bandwith, amplitude, kernalFunc) {\n let xVals = Array.from(Array(d3.max(inputArray) - d3.min(inputArray) + 1).keys()).map(x => x + d3.min(inputArray))\n let yVals = Array.from(xVals)\n let kernalStepSize = 1 // must be greater than 0\n for (let i = 0; i < xVals.length; i++) {\n let theValue = 0\n for (let j = 0; j < inputArray.length; j++){\n let observedVal = inputArray[j]\n let shiftDist = xVals[i] - observedVal\n theValue += kernalFunc(shiftDist) * amplitude * Math.abs(shiftDist) / bandwith;\n }\n yVals[i] = theValue\n }\n return [xVals, yVals]\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"md`#### Different Kernals`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":442,"value":"function kernalUniformFactory(a, b){\n let H = x => (x > 0) ? 1 : 0;\n return u => H(u - a) - H(u - b);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"function kernalGaussianFactory(){\n return u => Math.exp(-0.5 * u * u) / Math.sqrt(2 * Math.PI)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":566,"value":"md `#### Kernal shapes plots`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":427,"value":"{\n let n = 100\n let stepSize = 0.1\n let xvals = Array.from(Array(n).keys()).map(x => x * stepSize - ((n-1) * stepSize/2))\n \n let trace_uniform = {\n x : xvals,\n y : Array.from(xvals).map( x => kernalUniformFactory(-2,2)(x) ),\n type : 'scatter'\n }\n \n let trace_gauss = {\n x : xvals,\n y : Array.from(xvals).map( x => kernalGaussianFactory()(x) ),\n type : 'scatter'\n }\n \n const div = DOM.element('div');\n Plotly.newPlot(div, [trace_uniform, trace_gauss])\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":571,"value":"md `### Final KDE output on Test data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":343,"value":"testArray = waiting //[2,1,4,4,5,1,0,9,6]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":530,"value":"viewof bandwidth = {\n const form = html``;\n form.i.oninput = () => form.o.value = `${(form.value = form.i.valueAsNumber).toFixed(1)} bandwidth`;\n form.i.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":536,"value":"viewof amplitude = {\n const form = html``;\n form.i.oninput = () => form.o.value = `${(form.value = form.i.valueAsNumber).toFixed(1)} amplitude`;\n form.i.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"{\n let numOfBins = 20\n let histData = testArray\n \n let trace1 = {\n x:histData,\n type:'histogram',\n xbins: {size: (d3.max(histData) - d3.min(histData)) / (numOfBins - 1)},\n marker: {\n color: 'white',\n line: {\n color: \"black\", \n width: 1\n }\n\t }\n };\n \n let kdeData = generateKernalDensity(testArray, bandwidth, amplitude, kernalGaussianFactory())\n let trace2 = {\n x: kdeData[0],\n y: kdeData[1],\n type: 'scatter'\n };\n \n const div = DOM.element('div');\n div.setAttribute(\"id\", \"Div1\");\n Plotly.newPlot(div, [trace1, trace2])\n \n //d3.select('Div1')\n return div\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 33:"$32:body"