Public
Edited
Feb 11
HMTL = {
return htl.html`
<div>
<div style="display:flex">
<div>${viewof selected}</div>
<div>
<br/><br/> <br/>
<h3>Current TimeBox Coordinates:</h3>
${viewof selected.brushesCoordinates}
<br/>
<h3>Groups:</h3>
${viewof selected.groups}
</div>
</div>
</div>
`;
}
selected
selectedAll
comparisionCard(selectedAll, {
config: configImpact,
colorScale: selected.groupsColorScale,
width: width
})
classificationRaw = clasificationInput.json()
selectedAll = {
let newData = new Map();
Array.from(selected).forEach((g) => {
let Gid = selected.status.get(g[0]).id;
let selectedIds = rawData.filter((d) => g[1].has(d.Idenfinal));
newData.set(Gid, selectedIds);
});
return newData;
}
GenerateClasificatorInput = function (
independentVars,
objVar,
initDate,
finDate,
period
) {
if (!independentVars.includes(objVar)) {
independentVars.push(objVar);
}
let formatter = d3.timeFormat("%Y-%m-%d");
let dates = [];
let currentDate = new Date(initDate.getTime());
while (currentDate < finDate) {
let nextDate = new Date(currentDate.getTime());
nextDate.setFullYear(nextDate.getFullYear() + period);
dates.push([formatter(currentDate), formatter(nextDate)]);
currentDate = nextDate;
}
let content =
"base.csv\n" +
dates.join(";") +
"\n" +
independentVars.toString() +
"\n" +
objVar +
"\n" +
"out.json";
let file = new Blob([content], {
type: "text/plain"
});
return DOM.download(file, "clasifierIn.in", "Download Clasifier Input");
}
generateExportFile = function (Ids, name) {
let content = Ids.join("\n");
let file = new Blob([content], { type: "text/plain" });
return DOM.download(file, name + ".csv", name);
}
rawDataGrups = {
let set1 = new Set(G1.map((d) => d.Idenfinal));
let set2 = new Set(G2.map((d) => d.Idenfinal));
let aux = [];
rawData.forEach((d) => {
let id = d.Idenfinal;
if (set1.has(id)) {
d.__group = nameG1;
aux.push(d);
} else if (set2.has(id)) {
d.__group = nameG2;
aux.push(d);
}
});
return aux;
}
rawData2 = FileAttachment("baseRandom.zip")
.zip()
.then((res) => res.file("baseRandom.csv").csv({ typed: true }))
rawData = rawData2.slice(0, 10000)
classification = {
let parser = d3.timeParse("%Y-%m-%d");
let data = [];
let precision = [];
let samples = [];
classificationRaw.forEach((d) => {
let fecha = parser(d.Fecha_ini);
data.push(
Object.entries(d.importancia_atributo).map((d) => {
return { value: d[1], var: d[0], fecha: fecha };
})
);
precision.push({ value: d.F1_score, var: "Precision", fecha: fecha });
samples.push({ value: d["Total muestras"], var: "NSamples", fecha: fecha });
});
return { data: data.flat(), precision: precision, samples: samples };
}
stageData = transformData(rawData, "Idenfinal", stages, "__group")
Object.keys(stageData[0])
PosibleVariables = {
let aux = Object.keys(stageData[0]).slice(5);
aux.push("LecheMaternaExclusiva", "ConLecheMaterna");
return aux;
}
import { FacetedSearch } from "@john-guerra/faceted-search"
import { searchCheckbox } from "@john-guerra/search-checkbox"
import { comparisionCard } from "@ivelascog/comparison-cards"
import { StatisticalCard, config } from "@ivelascog/statistical-card"
import { transformData, stages } from "@ivelascog/to-stages"
import { interval } from "@mootari/range-slider"
import { Swatches } from "@d3/color-legend"
import { button } from "@jeremiak/download-data-button"
import { download } from "@mbostock/lazy-download"
TimeSearcher = {
try {
return await require(`http://localhost:8080/dist/TimeWidget.js?${Date.now()}`);
} catch (e) {
try {
return await require(await FileAttachment("TimeSearcher@2.js").url());
} catch (e) {
return await require("time-searcher-plus@0.0.24");
}
}
}
curvaspc = FileAttachment("curvasPC.json").json()
curvaspeso = FileAttachment("curvasPeso@4.json").json()
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