summer-heat by gka

_script.js

console.clear()
d3.select('body').selectAppend('div.tooltip.tooltip-hidden')

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

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)
  })

  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))

  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.tmpLoess))
  .curve(d3.curveStep)
  .defined(d => !isNaN(d.rolling))

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



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

monthSel.append('path')
  .at({
    d: line,
    stroke: '#f0f',
    strokeWidth: 2,
  }).st({
  	fill: 'none',
  fillOpacity: 0
  })
  .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,-3 L 0,3Z')
  .at({
    fill: '#f0f'
  })

index.html

<!DOCTYPE html>
<meta charset='utf-8'>
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="style.css">

<script src='https://unpkg.com/simple-statistics@6.1.0/dist/simple-statistics.min.js' />
<script>
console.log('hi')
</script>

<div id='graph'></div>

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


<script src='_script.js'></script>

data-2.json

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data.js

var years = 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data.json

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4.3,-2],avg_temp:8.625,year:1950},{months:[1.5,1.9,2.2,7.3,11.5,15.3,17.1,17.2,14.5,7.3,6.5,2.5],avg_temp:8.73333333333333,year:1951},{months:[.2,-.1,2.6,10.3,12.3,15.2,18.3,17.8,10.5,7.2,1.7,-.5],avg_temp:7.95833333333333,year:1952},{months:[-.8,-.1,4.8,9,13.2,16.1,17.4,16.4,13.5,10.4,4.8,2.5],avg_temp:8.93333333333333,year:1953},{months:[-3.2,-3.5,4.3,5.8,12.2,16,14.4,15.8,13.5,9.9,4.1,3.2],avg_temp:7.70833333333333,year:1954},{months:[-1.7,-1.9,.4,6.9,10.3,14.6,17.4,16.8,13.4,8,3.8,2.6],avg_temp:7.55,year:1955},{months:[.2,-9.6,2.9,5.3,12.5,13.2,16.9,14.1,13.8,8.2,2.5,2],avg_temp:6.83333333333333,year:1956},{months:[.3,3.8,6.4,7.5,9.7,16.7,17.7,15.1,11.8,9.2,4.5,.5],avg_temp:8.6,year:1957},{months:[-.4,1.9,-.3,5.2,13.3,14.5,17,16.9,14.5,9.3,4.3,2.5],avg_temp:8.225,year:1958},{months:[-.1,-.1,6.2,9.4,12.7,16.1,19.3,17.3,13.5,8.5,3.3,2.2],avg_temp:9.025,year:1959},{months:[-.1,.4,4.3,7.4,12.8,16.2,15.4,15.8,12.5,9.1,6,1.2],avg_temp:8.41666666666667,year:1960},{months:[-.8,4.4,6,10.6,10.3,16,15.1,15.7,16.3,10.5,3.7,-.6],avg_temp:8.93333333333333,year:1961},{months:[1.7,.3,.2,8.2,9.8,14.2,15,15.8,12.3,9,2.7,-3.3],avg_temp:7.15833333333333,year:1962},{months:[-7.4,-5.7,2.5,8.4,12,15.8,17.7,15.9,13.7,8.1,7.4,-3.1],avg_temp:7.10833333333333,year:1963},{months:[-2.6,.6,.6,8.6,13.6,16.9,18.1,15.9,13.6,7.1,4.9,.4],avg_temp:8.14166666666667,year:1964},{months:[1.2,-1.8,2.3,6.7,11.2,15.5,15,14.9,12.7,8.2,1.4,2.6],avg_temp:7.49166666666667,year:1965},{months:[-2.7,3.5,3.3,8.6,13,16.9,15.6,15.5,13.2,11.1,2.2,2],avg_temp:8.51666666666667,year:1966},{months:[.7,2.8,5.4,6.5,12.5,14.7,18.6,16.4,13.8,11.1,3.9,.4],avg_temp:8.9,year:1967},{months:[-1.1,.7,4.4,9,10.9,16,16.2,16.4,13.4,10.2,3.7,-2],avg_temp:8.15,year:1968},{months:[.5,-2.1,.9,7,12.9,14.8,18.4,16.5,13.8,10,5.1,-4.7],avg_temp:7.75833333333333,year:1969},{months:[-2.6,-1.1,1.2,5.3,11.4,17.2,16.1,16.8,13.2,8.9,5.8,.5],avg_temp:7.725,year:1970},{months:[-1.1,1.4,1,8.2,14,13.9,18,17.8,12,8.8,3.5,3.6],avg_temp:8.425,year:1971},{months:[-2.3,2,5.3,7.2,11.3,14.5,17.7,15.7,10.7,6.8,4.3,.8],avg_temp:7.83333333333333,year:1972},{months:[0,1,3.9,5.1,12.5,16.1,17.2,17.7,14.1,7.3,3.3,.2],avg_temp:8.2,year:1973},{months:[3.3,3.2,5.7,8,11,14.1,15.3,17.1,13.2,5.5,4.9,4.8],avg_temp:8.84166666666667,year:1974},{months:[4.5,1.4,3.6,6.8,11.8,14.7,18.2,18.8,15.5,7.5,3.1,1.2],avg_temp:8.925,year:1975},{months:[1.2,.5,1.3,6.9,12.8,17.4,19.3,16.2,12.7,9.5,4.8,-1],avg_temp:8.46666666666667,year:1976},{months:[.4,3,6.1,5.7,11.7,15.5,16.5,15.9,11.9,10.2,5.1,2.1],avg_temp:8.675,year:1977},{months:[.8,-1.4,4.8,6.5,11.6,14.8,15.5,15.1,12.2,9.1,4.2,.3],avg_temp:7.79166666666667,year:1978},{months:[-4.5,-1.8,3.7,6.3,12.4,16.5,15.3,15.5,13.1,8.6,3.7,3.8],avg_temp:7.71666666666667,year:1979},{months:[-2.7,2.1,3.3,6,10.7,14.7,15.1,16.6,13.9,8,3,.7],avg_temp:7.61666666666667,year:1980},{months:[-1.5,-.4,6.8,7.8,13.2,15.3,16.4,16.4,14,8.1,4.6,-2.2],avg_temp:8.20833333333333,year:1981},{months:[-2.3,-.1,4.2,6.5,12.4,16.3,18.9,17.3,15.7,9.7,6,2.2],avg_temp:8.9,year:1982},{months:[4,-1.7,4.6,8.6,11.3,16.3,20.4,18.1,13.7,9.2,3.5,.7],avg_temp:9.05833333333333,year:1983},{months:[1.1,-.3,2,6.8,10.8,13.7,15.9,17,12.3,10.2,4.9,1.2],avg_temp:7.96666666666667,year:1984},{months:[-5.5,-3.1,2.8,7.6,13.4,13.5,17.4,16.2,13.5,8.8,.8,3.7],avg_temp:7.425,year:1985},{months:[.1,-6.4,3,6.3,14.1,15.9,17,16.3,11.3,9.6,5.9,2.1],avg_temp:7.93333333333333,year:1986},{months:[-5.9,-.3,-.4,9.1,9.7,13.9,16.9,15.5,14.5,9.4,4.9,2.1],avg_temp:7.45,year:1987},{months:[3.5,2.1,2.7,8,14.2,15,17,17,13.2,9.7,3,3.4],avg_temp:9.06666666666667,year:1988},{months:[2.5,3.4,7,7.2,13.8,15.3,17.7,17,14.4,10.4,2.7,2.4],avg_temp:9.48333333333333,year:1989},{months:[2.5,5.7,6.9,7.4,13.9,15.2,16.7,18.3,11.8,10.2,4.5,.8],avg_temp:9.49166666666667,year:1990},{months:[1.5,-2.4,6.4,7.1,9.5,13.5,19.2,17.9,14.8,8.2,4,.7],avg_temp:8.36666666666667,year:1991},{months:[1,2.8,4.8,7.8,14.5,17.3,18.6,19.1,13.4,6.5,5.4,1.3],avg_temp:9.375,year:1992},{months:[2.5,-.7,3.6,10.4,14.6,15.7,16.1,15.8,12.2,7.9,.4,3.2],avg_temp:8.475,year:1993},{months:[3,-.2,6.3,7.9,12.6,15.9,21.3,17.8,13.2,7.6,7.2,3.6],avg_temp:9.68333333333333,year:1994},{months:[.2,4.6,3.1,8.5,12.2,14.3,20.1,18.3,12.6,11.8,3,-1.8],avg_temp:8.90833333333333,year:1995},{months:[-2.8,-2.2,1,8.4,11.2,15.7,15.9,17.2,10.9,9.2,4.5,-2.4],avg_temp:7.21666666666667,year:1996},{months:[-2.6,4.1,5.8,6.4,12.7,15.9,17.2,19.8,13.7,7.7,4,2.2],avg_temp:8.90833333333333,year:1997},{months:[2.4,4.3,5,8.9,14,16.5,16.4,16.7,13.4,8.7,1.6,1],avg_temp:9.075,year:1998},{months:[2.6,.4,5.4,8.9,13.8,15.2,19,17.2,16.8,9,3.5,2.2],avg_temp:9.5,year:1999},{months:[1.1,3.9,5.2,10.2,14.6,17,15.3,17.6,13.9,10.6,6.1,3.1],avg_temp:9.88333333333333,year:2e3},{months:[.9,2.3,4.2,7.1,14.4,14.3,18.6,18.6,11.8,12.5,3.8,-.2],avg_temp:9.025,year:2001},{months:[1.2,5.1,5.4,8.1,13.8,17.4,17.7,18.8,13.2,8.3,5.6,.3],avg_temp:9.575,year:2002},{months:[-.3,-1.9,5.3,8.5,14.1,19.4,19.1,20.6,13.9,5.9,6,1.8],avg_temp:9.36666666666667,year:2003},{months:[-.1,2.4,4.1,9.4,11.6,15.3,16.8,18.4,14,10.1,4.2,1.1],avg_temp:8.94166666666667,year:2004},{months:[2,-1,3.6,9.3,12.8,16.4,18,15.8,15,11,4.2,.9],avg_temp:9,year:2005},{months:[-2.6,-.4,1.5,8,13.1,16.8,22,15.5,16.9,12.1,7,4.4],avg_temp:9.525,year:2006},{months:[4.8,3.9,6.2,11.5,14.2,17.4,17.2,16.9,12.6,8.4,3.7,1.6],avg_temp:9.86666666666667,year:2007},{months:[3.7,3.7,4.2,7.6,14.5,16.9,18,17.4,12.4,9.1,5.1,1.1],avg_temp:9.475,year:2008},{months:[-2.2,.5,4.3,11.8,13.6,14.8,18,18.7,14.7,8.2,7.4,.3],avg_temp:9.175,year:2009},{months:[-3.7,-.5,4.2,8.7,10.4,16.3,20.3,16.7,12.4,8.1,4.8,-3.7],avg_temp:7.83333333333333,year:2010},{months:[1,.9,4.9,11.6,13.9,16.5,16.1,17.7,15.2,9.4,4.5,3.9],avg_temp:9.63333333333333,year:2011},{months:[1.9,-2.5,6.9,8.2,14.2,15.5,17.4,18.4,13.6,8.7,5.2,1.5],avg_temp:9.08333333333333,year:2012},{months:[.2,-.7,.1,8.1,11.8,15.7,19.5,17.9,13.3,10.6,4.6,3.6],avg_temp:8.725,year:2013},{months:[2.1,4.3,6.9,10.8,12.4,16.1,19.3,16,14.9,11.9,6.5,2.7],avg_temp:10.325,year:2014},{months:[2.2,.7,5.2,8.4,12.3,15.8,19.4,19.9,13,8.4,7.5,6.5],avg_temp:9.94166666666667,year:2015},{months:[1,3.3,4,7.9,13.7,17,18.6,17.7,16.9,8.5,3.8,2.2],avg_temp:9.55,year:2016},{months:[-2.2,2.9,7.2,7.4,14.1,17.8,18.1,17.9,12.8,11.1,5.1,2.7],avg_temp:9.575,year:2017},{months:[3.7,-1.9,2.4,12.3,16,17.7,20.3],avg_temp:null,year:2018}]}}

loess.js

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;
}

style.css

body{
  font-family: monaco, Consolas, 'Lucida Console', monospace; 
  margin: 0px;
}

.tooltip {
  top: -1000px;
  position: fixed;
  padding: 10px;
  background: rgba(255, 255, 255, .90);
  border: 1px solid lightgray;
  pointer-events: none;
}
.tooltip-hidden{
  opacity: 0;
  transition: all .3s;
  transition-delay: .1s;
}

@media (max-width: 590px){
  div.tooltip{
    bottom: -1px;
    width: calc(100%);
    left: -1px !important;
    right: -1px !important;
    top: auto !important;
    width: auto !important;
  }
}

svg{
  overflow: visible;
}

.domain{
  display: none;
}

text{
  /*pointer-events: none;*/
  /*text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;*/
}


.axis{
  opacity: .4;
}

.y line{
  stroke: #ccc;
}

.x line{
  opacity: 0;
}

.x{
  opacity: 1;
}

.x text{
  font-size: 14px
}

circle.active{
  stroke-opacity: .5;
}