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data-leak / source /anonymization /make-slides.js

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cd9051a over 4 years ago

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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()

	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()