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gka/.block

Last active September 13, 2019 10:56
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Revisions

  1. gka revised this gist Mar 1, 2019. No changes.
  2. gka revised this gist Mar 1, 2019. No changes.
  3. gka revised this gist Mar 1, 2019. No changes.
  4. gka revised this gist Mar 1, 2019. 1 changed file with 3 additions and 1 deletion.
    4 changes: 3 additions & 1 deletion README.md
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    @@ -1,3 +1,5 @@
    https://blog.datawrapper.de/weekly39/

    forked from <a href='http://bl.ocks.org/1wheel/'>1wheel</a>'s block: <a href='http://bl.ocks.org/1wheel/89ec11ab72a542b2a009d4fcd7b78b4c'>summer-heat</a>
    forked from <a href='http://bl.ocks.org/1wheel/'>1wheel</a>'s block: <a href='http://bl.ocks.org/1wheel/89ec11ab72a542b2a009d4fcd7b78b4c'>summer-heat</a>

    (wanted to see how Loess regression looks like, instead of linear regression)
  5. gka revised this gist Mar 1, 2019. 3 changed files with 240 additions and 14 deletions.
    34 changes: 20 additions & 14 deletions _script.js
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    // console.clear()
    console.clear()
    d3.select('body').selectAppend('div.tooltip.tooltip-hidden')

    var loess = LoessJS()()
    .bandwidth(0.43424)
    .robustnessIterations(1);

    var months = []
    years.forEach(year => {
    @@ -12,9 +15,17 @@ byMonth = d3.nestBy(months, d => d.month)

    byMonth.forEach(month => {
    month.forEach((d, i) => {

    d.rolling = d3.mean(month.slice(i - 10, i + 1), d => d.tmp)
    })

    const sorted = month.sort((a,b) => a.year - b.year);
    var xvals = sorted.map(d => d.year);
    var yvals = sorted.map(d => d.tmp);
    loess(xvals, yvals).forEach((tmpLoess, i) => {
    sorted[i].tmpLoess = tmpLoess;
    });

    var monthReg = month.map(d => [d.year, d.tmp])
    month.l = ss.linearRegressionLine(ss.linearRegression(monthReg))

    @@ -65,31 +76,26 @@ var yearScale = d3.scaleLinear()

    var line = d3.line()
    .x(d => yearScale(d.year))
    .y(d => c.y(d.rolling))
    .y(d => c.y(d.tmpLoess))
    .curve(d3.curveStep)
    .defined(d => !isNaN(d.rolling))

    var monthSel = c.svg.appendMany('g', byMonth)
    .translate(d => c.x(d.key), 0)

    monthSel
    .append('path')
    .at({
    d: line,
    stroke: '#000',
    fill: 'none',
    strokeWidth: .0,
    })


    var y0 = years[0].year
    var y1 = 2017

    monthSel.append('path')
    .at({
    d: d => `M ${yearScale(y0)} ${c.y(d.l(y0))} L ${yearScale(y1)} ${c.y(d.l(y1))}`,
    d: line,
    stroke: '#f0f',
    strokeWidth: 3,
    fill: '#f0f'
    strokeWidth: 2,
    }).st({
    fill: 'none',
    fillOpacity: 0
    })
    .attr('marker-end', 'url(#arrow)')

    @@ -149,7 +155,7 @@ c.svg.append('marker')
    .attr('markerHeight', 17)
    .attr('orient', 'auto')
    .append('path')
    .attr('d', 'M4,0 L0,-2 L 0,2Z')
    .attr('d', 'M4,0 L0,-3 L 0,3Z')
    .at({
    fill: '#f0f'
    })
    1 change: 1 addition & 0 deletions index.html
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    @@ -11,6 +11,7 @@
    <div id='graph'></div>

    <script src='data.js'></script>
    <script src='loess.js'></script>
    <script src='d3_.js'></script>


    219 changes: 219 additions & 0 deletions loess.js
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    function LoessJS() {
    // Based on org.apache.commons.math.analysis.interpolation.LoessInterpolator
    // from http://commons.apache.org/math/
    function loess() {
    var bandwidth = 0.3;
    var robustnessIters = 2;
    var accuracy = 1e-12;

    function smooth(xval, yval, weights) {
    var n = xval.length;

    var i;

    if (n !== yval.length) throw Error('Mismatched array lengths');
    if (n === 0) throw Error('At least one point required.');

    if (arguments.length < 3) {
    weights = [];
    i = -1;
    while (++i < n) weights[i] = 1;
    }

    science_stats_loessFiniteReal(xval);
    science_stats_loessFiniteReal(yval);
    science_stats_loessFiniteReal(weights);
    science_stats_loessStrictlyIncreasing(xval);

    if (n === 1) return [yval[0]];
    if (n === 2) return [yval[0], yval[1]];

    var bandwidthInPoints = Math.floor(bandwidth * n);

    if (bandwidthInPoints < 2) throw Error('Bandwidth too small.');

    var res = [];
    var residuals = [];
    var robustnessWeights = [];

    // Do an initial fit and 'robustnessIters' robustness iterations.
    // This is equivalent to doing 'robustnessIters+1' robustness iterations
    // starting with all robustness weights set to 1.
    i = -1;
    while (++i < n) {
    res[i] = 0;
    residuals[i] = 0;
    robustnessWeights[i] = 1;
    }

    var iter = -1;
    while (++iter <= robustnessIters) {
    var bandwidthInterval = [0, bandwidthInPoints - 1];
    // At each x, compute a local weighted linear regression
    var x;
    i = -1;
    while (++i < n) {
    x = xval[i];

    // Find out the interval of source points on which
    // a regression is to be made.
    if (i > 0) {
    science_stats_loessUpdateBandwidthInterval(
    xval,
    weights,
    i,
    bandwidthInterval
    );
    }

    var ileft = bandwidthInterval[0];
    var iright = bandwidthInterval[1];

    // Compute the point of the bandwidth interval that is
    // farthest from x
    var edge = xval[i] - xval[ileft] > xval[iright] - xval[i] ? ileft : iright;

    // Compute a least-squares linear fit weighted by
    // the product of robustness weights and the tricube
    // weight function.
    // See http://en.wikipedia.org/wiki/Linear_regression
    // (section "Univariate linear case")
    // and http://en.wikipedia.org/wiki/Weighted_least_squares
    // (section "Weighted least squares")
    var sumWeights = 0;
    var sumX = 0;
    var sumXSquared = 0;
    var sumY = 0;
    var sumXY = 0;
    var denom = Math.abs(1 / (xval[edge] - x));

    for (var k = ileft; k <= iright; ++k) {
    var xk = xval[k];

    var yk = yval[k];

    var dist = k < i ? x - xk : xk - x;

    var w =
    science_stats_loessTricube(dist * denom) *
    robustnessWeights[k] *
    weights[k];

    var xkw = xk * w;
    sumWeights += w;
    sumX += xkw;
    sumXSquared += xk * xkw;
    sumY += yk * w;
    sumXY += yk * xkw;
    }

    var meanX = sumX / sumWeights;
    var meanY = sumY / sumWeights;
    var meanXY = sumXY / sumWeights;
    var meanXSquared = sumXSquared / sumWeights;
    var beta =
    Math.sqrt(Math.abs(meanXSquared - meanX * meanX)) < accuracy
    ? 0
    : (meanXY - meanX * meanY) / (meanXSquared - meanX * meanX);
    var alpha = meanY - beta * meanX;

    res[i] = beta * x + alpha;
    residuals[i] = Math.abs(yval[i] - res[i]);
    }

    // No need to recompute the robustness weights at the last
    // iteration, they won't be needed anymore
    if (iter === robustnessIters) {
    break;
    }

    // Recompute the robustness weights.

    // Find the median residual.
    var sortedResiduals = residuals.slice();
    sortedResiduals.sort();
    var medianResidual = sortedResiduals[Math.floor(n / 2)];

    if (Math.abs(medianResidual) < accuracy) break;

    let arg;
    i = -1;
    while (++i < n) {
    arg = residuals[i] / (6 * medianResidual);
    robustnessWeights[i] = arg >= 1 ? 0 : (w = 1 - arg * arg) * w;
    }
    }

    return res;
    }

    smooth.bandwidth = function(x) {
    if (!arguments.length) return x;
    bandwidth = x;
    return smooth;
    };

    smooth.robustnessIterations = function(x) {
    if (!arguments.length) return x;
    robustnessIters = x;
    return smooth;
    };

    smooth.accuracy = function(x) {
    if (!arguments.length) return x;
    accuracy = x;
    return smooth;
    };

    return smooth;
    }

    function science_stats_loessFiniteReal(values) {
    var n = values.length;
    var i = -1;

    while (++i < n) if (!isFinite(values[i])) return false;

    return true;
    }

    function science_stats_loessStrictlyIncreasing(xval) {
    var n = xval.length;
    var i = 0;

    while (++i < n) if (xval[i - 1] >= xval[i]) return false;

    return true;
    }

    // Compute the tricube weight function.
    // http://en.wikipedia.org/wiki/Local_regression#Weight_function
    function science_stats_loessTricube(x) {
    return (x = 1 - x * x * x) * x * x;
    }

    // Given an index interval into xval that embraces a certain number of
    // points closest to xval[i-1], update the interval so that it embraces
    // the same number of points closest to xval[i], ignoring zero weights.
    function science_stats_loessUpdateBandwidthInterval(xval, weights, i, bandwidthInterval) {
    var left = bandwidthInterval[0];
    var right = bandwidthInterval[1];

    // The right edge should be adjusted if the next point to the right
    // is closer to xval[i] than the leftmost point of the current interval
    var nextRight = science_stats_loessNextNonzero(weights, right);
    if (nextRight < xval.length && xval[nextRight] - xval[i] < xval[i] - xval[left]) {
    var nextLeft = science_stats_loessNextNonzero(weights, left);
    bandwidthInterval[0] = nextLeft;
    bandwidthInterval[1] = nextRight;
    }
    }

    function science_stats_loessNextNonzero(weights, i) {
    var j = i + 1;
    while (j < weights.length && weights[j] === 0) j++;
    return j;
    }

    return loess;
    }
  6. gka created this gist Mar 1, 2019.
    1 change: 1 addition & 0 deletions .block
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    license: mit
    3 changes: 3 additions & 0 deletions README.md
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    https://blog.datawrapper.de/weekly39/

    forked from <a href='http://bl.ocks.org/1wheel/'>1wheel</a>'s block: <a href='http://bl.ocks.org/1wheel/89ec11ab72a542b2a009d4fcd7b78b4c'>summer-heat</a>
    157 changes: 157 additions & 0 deletions _script.js
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    @@ -0,0 +1,157 @@
    // console.clear()
    d3.select('body').selectAppend('div.tooltip.tooltip-hidden')


    var months = []
    years.forEach(year => {
    year.months
    .forEach((d, i) => months.push({year: year.year, tmp: d, month: i}))
    })

    byMonth = d3.nestBy(months, d => d.month)

    byMonth.forEach(month => {
    month.forEach((d, i) => {
    d.rolling = d3.mean(month.slice(i - 10, i + 1), d => d.tmp)
    })

    var monthReg = month.map(d => [d.year, d.tmp])
    month.l = ss.linearRegressionLine(ss.linearRegression(monthReg))

    month.min = d3.min(month, d => d.tmp)
    if (month.key == 1) month.min += 2
    if (month.key == 0) month.min += .6
    })


    var sel = d3.select('#graph').html('')
    var c = d3.conventions({sel, margin: {left: 40}})

    c.x.domain([0, 12])
    c.y.domain(d3.extent(months, d => d.tmp))


    c.svg.appendMany('rect', d3.range(12))
    .at({
    width: c.x(1),
    x: d => c.x(d),
    height: c.height,
    opacity: d => d % 2 ? .04 : .08
    })



    c.xAxis
    .tickFormat(i => 'Jan Feb March April May June July Aug Sept Oct Nov Dec'.split(' ')[i])
    .tickValues(d3.range(12))
    c.yAxis
    .ticks(6)
    .tickSize(c.width)
    .tickFormat(d => d + '° C')

    d3.drawAxis(c)
    c.svg.select('.x')
    .translate(0, 0)
    .selectAll('.tick')
    .translate(d => [c.x(d + .5), c.y(byMonth[d].min)])

    c.svg.selectAll('.y')
    .translate(c.width, 0)

    var pad = innerWidth < 500 ? 3 : 10
    var yearScale = d3.scaleLinear()
    .domain(d3.extent(years, d => d.year))
    .range([pad, c.x(1) - pad])

    var line = d3.line()
    .x(d => yearScale(d.year))
    .y(d => c.y(d.rolling))
    .curve(d3.curveStep)
    .defined(d => !isNaN(d.rolling))

    var monthSel = c.svg.appendMany('g', byMonth)
    .translate(d => c.x(d.key), 0)

    monthSel
    .append('path')
    .at({
    d: line,
    stroke: '#000',
    fill: 'none',
    strokeWidth: .0,
    })

    var y0 = years[0].year
    var y1 = 2017

    monthSel.append('path')
    .at({
    d: d => `M ${yearScale(y0)} ${c.y(d.l(y0))} L ${yearScale(y1)} ${c.y(d.l(y1))}`,
    stroke: '#f0f',
    strokeWidth: 3,
    fill: '#f0f'
    })
    .attr('marker-end', 'url(#arrow)')

    months.forEach(d => delete d.rolling)

    var circleSel = monthSel
    .appendMany('circle', d => d)
    .at({
    r: .7,
    strokeWidth: 5,
    stroke: d => d.year == 1881 ? 'steelblue' : d.year == 2017 ? 'orange' : '#f00',
    strokeOpacity: d => d.year == 1881 || d.year == 2017 ? .5 : .01,
    stroke: '#f00',
    strokeOpacity: .01,
    opacity: innerWidth < 500 ? .7 : 1,
    cx: d => yearScale(d.year),
    cy: d => c.y(d.tmp)
    })
    .call(d3.attachTooltip)
    .on('mouseover', d => {
    circleSel
    .classed('active', e => e.year == d.year)
    })
    .on('mouseout', d => {
    circleSel.classed('active', 0)
    })
    // .classed('active', d => d.year == 1881 || d.year == 2017)

    var octSel = monthSel.filter(d => d.key == 9)
    octSel.append('g')
    .translate(d => [pad - 27, c.y(d[0].tmp) + 2])
    .append('text.anno').text(1881)
    .at({textAnchor: 'end', dy: '-.3em', fontSize: 10, dx: 10, opacity: .6})
    .parent()
    .append('path')
    .at({
    d: 'M 0,0 A 13.09 13.09 0 1 0 26,1',
    stroke: '#ccc',
    fill: 'none',
    })
    octSel.append('g')
    .translate(d => [c.x(1) - pad, c.y(d[136].tmp) - 2])
    .append('text.anno').text(2017)
    .at({textAnchor: 'start', dy: '1em', fontSize: 10, dx: 14, opacity: .6})
    .parent()
    .append('path')
    .at({
    d: 'M 0,0 A 13.09 13.09 0 1 1 26,1',
    stroke: '#ccc',
    fill: 'none',
    })

    c.svg.append('marker')
    .attr('id', 'arrow')
    .attr('viewBox', '-10 -10 20 20')
    .attr('markerWidth', 17)
    .attr('markerHeight', 17)
    .attr('orient', 'auto')
    .append('path')
    .attr('d', 'M4,0 L0,-2 L 0,2Z')
    .at({
    fill: '#f0f'
    })


    143 changes: 143 additions & 0 deletions d3_.js
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    1 change: 1 addition & 0 deletions data-2.json
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    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