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Running
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window.makeSlides = function(){
var slides = [
{
xKey: 'grid',
circleDelayFn: d => axii.ageScale(d.age),
showFlipRect: 0,
populationTarget: 144,
headsProbTarget: .5,
},
{
xKey: 'age',
showAgeAxis: 1,
},
{
xKey: 'ageState',
showStateAxis: 1,
},
{
showUniqueBox: 1
},
{
xKey: 'ageStateSeason',
showUniqueBox: 1,
showUniqueSeasonBox: 1,
showSeasonAxis: 1,
},
{
xKey: 'heads',
showUniqueBox: 0,
showUniqueSeasonBox: 0,
showSeasonAxis: 0,
showAgeAxis: 0,
showStateAxis: 0,
showHeadAxis: 1,
},
{
showFlipCircle: 1,
showHeadCaptionAxis: 1,
},
// Flip coin
{
xKey: 'plagerizedShifted',
showHeadAxis: 0,
showHeadCaptionAxis: 0,
showHistogramAxis: 1,
},
// Exactly how far off can these estimates be after adding noise? Flip more coins to see the distribution.
{
enterHistogram: 1,
showHistogram: 1,
// showPlagerizedAxis: 0,
showEstimate: 1,
},
// Reducing the random noise increases our point estimate, but risks leaking information about students.
{
animateHeadsProbSlider: 1,
animatePopulationSlider: 1,
enterHistogram: 0,
name: 'noise',
headsProbTarget: .35,
},
// If we collect information from lots of people, we can have high accuracy and protect everyone's privacy.
{
showEstimate: 0,
showAllStudents: 1,
name: 'population',
animateHeadsProbSlider: -1,
animatePopulationSlider: 1,
populationTarget: 400,
},
]
var keys = []
slides.forEach((d, i) => {
keys = keys.concat(d3.keys(d))
d.index = i
})
_.uniq(keys).forEach(str => {
var prev = null
slides.forEach(d => {
if (typeof(d[str]) === 'undefined'){
d[str] = prev
}
prev = d[str]
})
})
return slides
}
if (window.init) window.init()
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