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https://github.com/LCPQ/QUESTDB_website.git
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Improve statistics table
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c5208d13b5
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0714161047
@ -268,71 +268,67 @@ draft: false
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key.shift()
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Reflect.setPrototypeOf(key[0], method.prototype)
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//Reflect.setPrototypeOf(key[1], DOI.prototype)
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for (const [i, el] of key.entries()) {
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var td = $("<th/>",{scope:"column"})
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if (i === 1) {
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var publi = doiCache.get(el, true)
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const sets = await getSets()
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if (sets.has(el)) {
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var str = sets.get(el)
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$(td).append(str+ " ")
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}
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$("<a/>", {
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href: new DOI(el).url,
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target: "_blank"
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}).html(publi.format('citation', {
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format: 'html',
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lang: 'en-US'
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})).appendTo(td)
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}
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else {
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$(td).text(el)
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}
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$(row).append(td)
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th = $("<th/>", { scope: "column" })
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const [meth, doi] = key
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th.clone().text(meth.toString("\n").split('-').join('\u2011')).appendTo(row)
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var thdoi = th.clone()
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var publi = doiCache.get(doi, true)
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const sets = await getSets()
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if (sets.has(doi)) {
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var str = sets.get(doi)
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$(thdoi).append(str + " ")
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}
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const noNanVals = (vals.every((v) => Number.isNaN(v))) ? vals : (vals.filter((v) => !Number.isNaN(v)))
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const avals = noNanVals.map(v => Math.abs(v))
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for (const val of [ss.min(noNanVals), ss.max(noNanVals), ss.mean(noNanVals), ss.mean(avals), ss.median(noNanVals), ss.median(avals), ss.rootMeanSquare(noNanVals), ss.variance(noNanVals), ss.standardDeviation(noNanVals)]) {
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$("<td/>").text(noNanFixed(val, 2)).appendTo(row)
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$("<a/>", {
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href: new DOI(doi).url,
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target: "_blank"
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}).html(publi.format('citation', {
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format: 'html',
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lang: 'en-US'
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})).appendTo(thdoi)
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$(row).append(thdoi)
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const noNanVals = (vals.every((v) => Number.isNaN(v))) ? vals : (vals.filter((v) => !Number.isNaN(v)))
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const avals = noNanVals.map(v => Math.abs(v))
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for (const val of [ss.min(noNanVals), ss.max(noNanVals), ss.mean(noNanVals), ss.mean(avals), ss.median(noNanVals), ss.median(avals), ss.rootMeanSquare(noNanVals), ss.variance(noNanVals), ss.standardDeviation(noNanVals)]) {
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$("<td/>").text(noNanFixed(val, 2)).appendTo(row)
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}
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$(stb).append(row)
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var box = {
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x: noNanVals,
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amean: ss.mean(avals).toFixed(3),
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name: key[0].toString() + " MAD : " + ss.mean(avals).toPrecision(4),
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type: 'box',
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boxmean: 'sd'
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};
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graphdat.push(box)
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}
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var layout = {
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paper_bgcolor: 'rgba(0,0,0,0)',
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plot_bgcolor: 'rgba(0,0,0,0)',
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gap: 0,
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legend: {
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traceorder: 'reversed',
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},
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xaxis: {
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title: {
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text: 'Energy (eV)',
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}
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$(stb).append(row)
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var box = {
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x: noNanVals,
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amean: ss.mean(avals).toFixed(3),
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name: key[0].toString() + " MAD : " + ss.mean(avals).toPrecision(4),
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type: 'box',
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boxmean: 'sd'
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};
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graphdat.push(box)
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}
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var layout = {
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paper_bgcolor: 'rgba(0,0,0,0)',
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plot_bgcolor: 'rgba(0,0,0,0)',
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gap: 0,
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legend: {
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traceorder: 'reversed',
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},
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xaxis: {
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title: {
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text: 'Energy (eV)',
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}
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},
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bgcolor: '#E2E2E2',
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bordercolor: '#FFFFFF',
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borderwidth: 2,
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// autosize: false,
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width: 850,
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height: 500,
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margin: {
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l: 0,
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r: 10,
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b: 15,
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t: 20,
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pad: 0,
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},
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}
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Plotly.newPlot('graph_div', graphdat, layout);
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processingIndicator.isActive = false
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},
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bgcolor: '#E2E2E2',
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bordercolor: '#FFFFFF',
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borderwidth: 2,
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// autosize: false,
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width: 850,
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height: 500,
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margin: {
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l: 0,
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r: 10,
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b: 15,
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t: 20,
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pad: 0,
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},
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}
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Plotly.newPlot('graph_div', graphdat, layout);
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processingIndicator.isActive = false
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}
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async function reloadContent() {
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window.filterParams = [$("#cb_unsafe").prop("checked"), Array.from(Array.from($("#cb_exTypeList>li>input[type=checkbox]:checked")).map(el => parseInt(el.value))).reduce((pv, cv) => pv + cv, 0), Array.from(Array.from($("#cb_exVertKindList>li>input[type=checkbox]:checked")).map(el => parseInt(el.value))).reduce((pv, cv) => pv + cv, 0)]
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@ -399,7 +395,7 @@ draft: false
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var head = $("<tr/>")
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$("<thead/>").addClass("sticky").append(head).appendTo(table)
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var tbody = $("<tbody/>").appendTo(table)
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var th=$("<th/>",{scope:"column"})
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var th = $("<th/>", { scope: "column" })
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head.append(["Molecule", "Transition"].map(x => th.clone().text(x)))
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head.append(sortedMethods.map(x => th.clone().text(x.toString("\n").split('-').join('\u2011'))))
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adjustSticky();
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@ -431,14 +427,14 @@ draft: false
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Reflect.setPrototypeOf(ex[3], VertExcitationKind.prototype)
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var tr = $("<tr/>")
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if (printmol) {
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$("<th/>",{ scope:"row", rowspan:moldat.size}).text(molecule).appendTo(tr)
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$("<th/>", { scope: "row", rowspan: moldat.size }).text(molecule).appendTo(tr)
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printmol = false
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}
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var Vertkindtext = ""
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if (ex[3].Value == VertExcitationKinds.Fluorescence.Value) {
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Vertkindtext = String.raw`[${VertExcitationKinds.Fluorescence.LaTeX}]`
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}
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$("<th/>",{scope:"row"}).text(String.raw`${LatexInline[0]}${ex[0].toLaTeX()} \rightarrow ${ex[1].toLaTeX()} ${Vertkindtext}(${ex[2].LaTeX}) ${LatexInline[1]}`).appendTo(tr)
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$("<th/>", { scope: "row" }).text(String.raw`${LatexInline[0]}${ex[0].toLaTeX()} \rightarrow ${ex[1].toLaTeX()} ${Vertkindtext}(${ex[2].LaTeX}) ${LatexInline[1]}`).appendTo(tr)
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var entries = Array.from(exdat.entries())
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for (const method of sortedMethods) {
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td = $("<td/>").addClass("NumberCell")
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@ -587,17 +583,17 @@ draft: false
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<section>
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<table id="stat_table" class="datatable">
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<thead class="sticky">
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<th>Method</th>
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<th>Set</th>
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<th>Min</th>
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<th>Max</th>
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<th>Mean</th>
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<th>Absolute Mean</th>
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<th>Median</th>
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<th>Absolute Median</th>
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<th>RMS</th>
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<th>Variance</th>
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<th>Standard deviation</th>
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<th scope="col">Method</th>
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<th scope="col">Set</th>
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<th scope="col">Min</th>
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<th scope="col">Max</th>
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<th scope="col">MSE</th>
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<th scope="col">MAE</th>
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<th scope="col">Median</th>
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<th scope="col">Absolute Median</th>
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<th scope="col">RMS</th>
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<th scope="col">Variance</th>
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<th scope="col">Standard deviation</th>
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</thead>
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<tbody>
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</tbody>
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