Published
Edited
Jul 11, 2022
import {table} from "@mjw/tables"
cycleTimes = {
const array = [];
for (var i = 1; i <= 30; i++) {
for (var c = paceRange[0]; c <= d3.randomInt(paceRange[0], paceRange[1]+1)(0); c++) {
array.push({day:i, cycletime: d3.randomInt(1,10)(0)});
}
// add the odd outlier in (we could just use a distribution for this...)
if (d3.randomInt(0,10)(0)>=7) {
array.push({day:i, cycletime: d3.randomInt(7,25)(0)});
}
}
return array;
}
cycleTimePercentiles = {
const percentiles = {};
const sortedCycleTimes = Array.from(cycleTimes, t => t.cycletime).sort((a,b)=>a-b);
percentiles[50] = sortedCycleTimes[Math.round(sortedCycleTimes.length * (50/100))];
percentiles[70] = sortedCycleTimes[Math.round(sortedCycleTimes.length * (70/100))];
percentiles[85] = sortedCycleTimes[Math.round(sortedCycleTimes.length * (85/100))];
percentiles[95] = sortedCycleTimes[Math.round(sortedCycleTimes.length * (95/100))];
return percentiles;
}
options = ({
start: 1,
bar_color: 'lightgrey',
chart_height: height,
chart_width: width,
margin: {top: 20, right: 20, bottom: 40, left: 40},
x_title: "Sprints to complete",
y_title: "Result frequency",
tickCount: 5
})
mcfeatureCutResults = Array.from(featureMonteCarloData, t => t.workCompleted).sort((a,b)=>a-b);
// create the features outside the MC sim so changing sim parameters doesnt trigger new features
randomFeatures = {
let fc = [];
for (var i = 1; i <= featureAsk; i++) {
let numStories = d3.randomInt(3,15)(0);
fc.push({
name:`Feature ${i} - ${featureNames[d3.randomInt(0,featureNames.length)(0)]}`,
stories: numStories,
});
}
return fc;
}
featureWork = featuresCutLine.reduce((r,c) => r + c.stories, 0)
doneByFeatureCutDeadline = mcfeatureCutResults[Math.ceil(mcfeatureCutResults.length * (1-0.85)) - 1]
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
