var state = {
  dataset_size: 15000,
  threshold: .8,
  label: 8
}

var sel = d3.select('.accuracy-v-privacy-class').html('')
  .at({role: 'graphics-document', 'aria-label': `Line chart showing that high accuracy models can still perform poorly on some digit classes.`})

async function loadData(){
  var rawData = await util.getFile(`cns-cache/grid_${state.dataset_size}trainpoints_test_labels.csv`)

  rawData.forEach(d => {
    delete d['']
    d.i = +d.i
    d.label = +d.label
  })

  var aVal2Meta = {}
  var metadata = await util.getFile('cns-cache/model_grid_test_accuracy.json')
  metadata
    .filter(d => d.dataset_size == state.dataset_size)
    .forEach(d => aVal2Meta['aVal_' + d.aVal] = d)

  var allCols = d3.keys(rawData[0])
      .filter(d => d.includes('aVal'))
      .map(key => {
        var {epsilon, aVal} = aVal2Meta[key]
        return {key, epsilon, aVal}
      })
  
  var byDigit = d3.nestBy(rawData, d => d.label)
  byDigit.forEach(d => {
    d.label = +d.key
  })
  byDigit.forEach(digitClass => {
    digitClass.cols = allCols.map(({key, epsilon}, colIndex) => {
      return {
        key,
        colIndex,
        epsilon,
        digitClass,
        label: digitClass.label,
        accuracy: d3.mean(digitClass, d => d[key] > state.threshold)
      }
    })
  })  

  var data = _.flatten(byDigit.map(d => d.cols))
    .filter(d => util.epsilonExtent[1] <= d.epsilon && d.epsilon <= util.epsilonExtent[0])
  var byLabel = d3.nestBy(data, d => d.label)
  byLabel.forEach((d, i) => {
    d.label = d.key
  })

  return {data, byLabel}
}


async function initChart(){
  var {data, byLabel} = await loadData()

  var c = d3.conventions({
    sel: sel.append('div'),
    height: 400,
    margin: {bottom: 75, top: 5},
    layers: 'ds',
  })

  c.x = d3.scaleLog().domain(util.epsilonExtent).range(c.x.range())
  c.xAxis = d3.axisBottom(c.x).tickFormat(d => {
    var rv = d + ''
    if (rv.split('').filter(d => d !=0 && d != '.')[0] == 1) return rv
  })

  c.yAxis.tickFormat(d => d3.format('.0%')(d))//.ticks(8)
  d3.drawAxis(c)
  util.addAxisLabel(c, 'Higher Privacy →', '')
  util.ggPlotBg(c, false)
  c.layers[0].append('div')
    .st({fontSize: 12, color: '#555', width: 120*2, textAlign: 'center', lineHeight: '1.3em', verticalAlign: 'top'})
    .translate([c.width/2 - 120, c.height + 45])
    .html('in ε')

  var line = d3.line().x(d => c.x(d.epsilon)).y(d => c.y(d.accuracy))

  var lineSel = c.svg.append('g').appendMany('path.accuracy-line', byLabel)
    .at({
      d: line,
      fill: 'none',
      stroke: '#000',
      // opacity: 0,
    })
    .on('mousemove', setActiveLabel)

  var circleSel = c.svg.append('g')
    .appendMany('g.accuracy-circle', data)
    .translate(d => [c.x(d.epsilon), c.y(d.accuracy)])
    .on('mousemove', setActiveLabel)
    // .call(d3.attachTooltip)

  circleSel.append('circle')
    .at({r: 7, stroke: '#fff'})

  circleSel.append('text')
    .text(d => d.label)
    .at({textAnchor: 'middle', fontSize: 10, fill: '#fff', dy: '.33em'})

  setActiveLabel(state)
  function setActiveLabel({label}){
    lineSel
      .classed('active', 0)
      .filter(d => d.label == label)
      .classed('active', 1)
      .raise()

    circleSel
      .classed('active', 0)
      .filter(d => d.label == label)
      .classed('active', 1)
      .raise()

    state.label = label
  }


  async function updateDatasetSize(){
    var newData = await loadData()
    data = newData.data
    byLabel = newData.byLabel

    lineSel.data(byLabel)
      .transition()
      .at({d: line})

    circleSel.data(data)
      .transition()
      .translate(d => [c.x(d.epsilon), c.y(d.accuracy)])

    c.svg.select('text.annotation').remove()
  }

  function updateThreshold(){
    data.forEach(d => {
      d.accuracy = d3.mean(d.digitClass, e => e[d.key] > state.threshold)
    })

    lineSel.at({d: line})
    circleSel.translate(d => [c.x(d.epsilon), c.y(d.accuracy)])

    c.svg.select('.y .axis-label').text(`Test Points With More Than ${d3.format('.2%')(state.threshold)} Confidence In Label`)

    c.svg.select('text.annotation').remove()
  }
  updateThreshold()

  return {c, updateDatasetSize, updateThreshold}
}


async function init(){
  sel.append('div.chart-title').text('High accuracy models can still perform poorly on some digit classes')

  var chart = await initChart()

  var buttonRowSel = sel.append('div.button-row')
    .st({height: 50})

  var buttonSel = buttonRowSel.append('div')
    .st({width: 500})
    .append('span.chart-title').text('Training points')
    .parent()
    .append('div').st({display: 'inline-block', width: 300, marginLeft: 10})
    .append('div.digit-button-container.dataset_size')
    .appendMany('div.button', [2000, 3750, 7500, 15000, 30000, 60000])
    .text(d3.format(','))
    .classed('active', d => d == state.dataset_size)
    .on('click', d => {
      buttonSel.classed('active', e => e == d)
      state.dataset_size = d
      chart.updateDatasetSize()
    })

  buttonRowSel.append('div.conf-slider')
    .append('span.chart-title').text('Confidence threshold')
    .parent()
    .append('input.slider-native')
    .at({
      type: 'range',
      min: .0001,
      max: .9999,
      step: .0001,
      value: state.threshold,
    })
    .on('input', function(){
      state.threshold = this.value
      chart.updateThreshold()
    })


  function addSliders(){
    var width = 140
    var height = 30
    var color = '#000'

    var sliders = [
      {key: 'threshold', label: 'Confidence threshold', r: [.0001, .9999]},
    ]
    sliders.forEach(d => {
      d.value = state[d.key]
      d.xScale = d3.scaleLinear().range([0, width]).domain(d.r).clamp(1)
    })

    d3.select('.conf-slider .slider-container').remove()
    d3.select('.slider-native').remove()

    var svgSel = d3.select('.conf-slider').parent()
      // .st({marginTop: 5, marginBottom: 5})
      .appendMany('div.slider-container', sliders)
      .append('svg').at({width, height})
      .append('g').translate([10, 25])

    var sliderSel = svgSel
      .on('click', function(d){
        d.value = d.xScale.invert(d3.mouse(this)[0])
        renderSliders(d)
      })
      .classed('slider', true)
      .st({cursor: 'pointer'})

    var textSel = sliderSel.append('text.annotation')
      .at({y: -15, fontWeight: 300, textAnchor: 'middle', x: 180/2})

    sliderSel.append('rect')
      .at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'})

    sliderSel.append('path').at({
      d: `M 0 -.5 H ${width}`, 
      stroke: color,
      strokeWidth: 1
    })

    var leftPathSel = sliderSel.append('path').at({
      d: `M 0 -.5 H ${width}`, 
      stroke: color,
      strokeWidth: 3
    })

    var drag = d3.drag()
      .on('drag', function(d){
        var x = d3.mouse(this)[0]
        d.value = d.xScale.invert(x)
        
        renderSliders(d)
      })

    var circleSel = sliderSel.append('circle').call(drag)
      .at({r: 7, stroke: '#000'})

    function renderSliders(d){
      if (d) state[d.key] = d.value

      circleSel.at({cx: d => d.xScale(d.value)})
      leftPathSel.at({d: d => `M 0 -.5 H ${d.xScale(d.value)}`})
      textSel
        .at({x: d => d.xScale(d.value)})
        .text(d => d3.format('.2%')(d.value))
      chart.updateThreshold()
    }
    renderSliders()
  }
  addSliders()


  chart.c.svg.append('text.annotation')
    .translate([505, 212])
    .tspans(d3.wordwrap(`8s are correctly predicted with high confidence much more rarely than other digits`, 25), 12)
    .at({textAnchor: 'end'})

}
init()