Develop Reference

Costumize indicators from stock tools

I'm trying to implement some technical indicators series and add them to the indicators popop from stock tools. If I import highcharts/indicators/indicators-all I end up getting dozens of indicators, so I figured to import only the ones I need, so far I wasn't able to achieve that, if I import highcharts/indicators/indicators I end up getting only SMA, I tried to import other technical indicators via highcharts/indicators/indicators-INDICATOR-NAME but it didn't work.
Besides that I'd like to create a technical indicator/function such as Linear Regression (from this example) and attach them to the indicators popup as well.
function getLinearRegression(xData, yData) {
var sumX = 0,
sumY = 0,
sumXY = 0,
sumX2 = 0,
linearData = [],
linearXData = [],
linearYData = [],
n = xData.length,
alpha,
beta,
i,
x,
y;
// Get sums:
for (i = 0; i < n; i++) {
x = xData[i];
y = yData[i];
sumX += x;
sumY += y;
sumXY += x * y;
sumX2 += x * x;
}
// Get slope and offset:
alpha = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
if (isNaN(alpha)) {
alpha = 0;
}
beta = (sumY - alpha * sumX) / n;
// Calculate linear regression:
for (i = 0; i < n; i++) {
x = xData[i];
y = alpha * x + beta;
// Prepare arrays required for getValues() method
linearData[i] = [x, y];
linearXData[i] = x;
linearYData[i] = y;
}
return {
xData: linearXData,
yData: linearYData,
values: linearData
};
}
Is that even possible?
Live Demo
EDIT
To add a specific technical indicator you should add as an import highcharts/indicators/NAME (highcharts/indicators/ema,
highcharts/indicators/rsi e.g.)

That feature is not implemented in stock tools, but it could be very useful so you can create a new feature request here: https://github.com/highcharts/highcharts/issues/new/choose
Workaround:
All indicator series from plot options are added to stock tools, so you can customize chart.options.plotOptions, for example in load event:
chart: {
events: {
load: function() {
var plotOptions = this.options.plotOptions,
filteredSeries = {};
Highcharts.objectEach(plotOptions, function(option, key) {
if (!option.params || key === 'dema' || key === 'customlinearregression') {
filteredSeries[key] = option;
}
});
this.options.plotOptions = filteredSeries;
}
}
}
Live demo: https://jsfiddle.net/BlackLabel/xwec9hr7/2/
Useful example: https://www.highcharts.com/stock/demo/stock-tools-custom-gui
Code reference: https://github.com/highcharts/highcharts/blob/371424be0b168de96aa6a58b81ce0b2b7f40d5c5/ts/annotations/popup.ts#L783

Print target labels on xAxis with labels.formatter

I have some plotLines and I just need to print labels corresponding to those plotLines on my xAxis.
My data is a random value around 100 (yAxis) and dates that increments by 10 days (xAxis).
var getDaysArray = function(start, end) {
for (var arr = [], dt = start; dt <= end; dt.setDate(dt.getDate() + 10)) {
arr.push(new Date(dt));
}
return arr;
};
function generateDataPoints(noOfDps) {
var yVal = 100;
var dps = [];
var xDate = getDaysArray(new Date("2006-01-01"), new Date("2019-12-30"));
for (var i = 0; i < xDate.length; i++) {
yVal = yVal + Math.round(5 + Math.random() * (-5 - 5));
dps.push({ x: xDate[i], y: yVal });
}
return dps;
}
let dataPoints = generateDataPoints(100);
There is also a function that gets the min, max, first and last values of y and returns an array of markers. Those are the points where I have plotLines.
function setHighestLowest(dtPoints) {
let highestIndex = -1;
let minimumIndex = -1;
let highestValue = 0;
let lowestValue = 0;
for (let i = 0; i < dtPoints.length; i++) {
let obj = dtPoints[i];
if (obj.y > highestValue) {
highestIndex = i;
highestValue = obj.y;
}
if (obj.y < lowestValue || i === 0) {
minimumIndex = i;
lowestValue = obj.y;
}
}
dtPoints[0].indexLabel = dtPoints[0].y.toString();
dtPoints[dtPoints.length - 1].indexLabel = dtPoints[
dtPoints.length - 1
].y.toString();
if (highestIndex > -1) {
dtPoints[highestIndex].indexLabel = dtPoints[highestIndex].y.toString();
}
if (minimumIndex > -1) {
dtPoints[minimumIndex].indexLabel = dtPoints[minimumIndex].y.toString();
}
//returns -> [max[0], min[1], first[2], last[3]]
let dateMarker = [
dtPoints[highestIndex].x,
dtPoints[minimumIndex].x,
dtPoints[1].x,
dtPoints[dtPoints.length - 1].x
];
//console.log(dateMarker);
return dateMarker;
}
My approach is to define labels.formatter functions for the xAxis.
First issue I found was the increment steps, I tried xAxis.labels.step but it didn't work, than I found xAxis.tickIntervals it seemed to work at first, the only problem was that to prevent labels from being printed outside of xAxis area the formatter function value started from a previous point from my dataset, there was some kind of offset, formatter function increment would never fit my data (offset problem). To solve this offset problem I tried the xAxis.tickPositions and called my markers array, from that point I was able to acces the points on formatter function, the only problem is that it returns nothing, I get inside the if loop that does the checking but nothing is printed on xAxis.labels.
The final result I want in my plot is something like that:
This is my code:

What do you think about use the plotLines.label feature to render those labels? I think that it will be easier to implement this logic there.
Demo: https://codesandbox.io/s/highcharts-react-xaxis-label-formatter-8xdcg
API: https://api.highcharts.com/highcharts/xAxis.plotLines.label

How to reduce number of computations during d3.js transition?

So right now, I'm trying to implement a search bar function into my d3.js plot. Right now it doesn't do anything, but that's not the issue at the moment. The problem is that when I type/delete something from the bar, there's visible lag/choppiness in the characters appearing/disappearing. I believe the issue is stemming from my plot. I have 140+ dots moving around the screen, and their position is being interpolated. So from the beginning to the end of the transition, my code has to compute 140 positions thousands of times over.
I've looked into trying to reduce the cardinality of the d3.interpolateNumber function, but it appears that there isn't a third argument to change the number of terms like in a linspace command. Right now I have an array of 1000 numbers for my function to run through, but I don't know how to pass the array to my other functions.
Below are the pertinent functions for this issue. The commented line in tweenPatch is the original code I had that made my code run, but gave my plot computational issues. Variables arr, curr, and step were my attempt to fix the situation, but I haven't been able to figure out how to pass the array into displayPatch().
function tweenPatch() {
var patch = d3.interpolateNumber(1, 26);
var arr = [];
var curr = 1;
var step = (26 - 1) / (1000 - 1);
for (var i = 0; i < 1000; i++) {
arr.push(curr + (step * i));
}
return arr.forEach(function(d) {
console.log(arr[d]);
displayPatch(arr[d]);
});
//return function(t) { displayPatch(t); };
}
function displayPatch(patch) {
dots.data(interpolateData(patch), function(d) { return d.name; }).call(position).sort(order);
var inter = Math.floor(patch);
var seas = 8;
var patc = 1;
if (inter > 24) {
seas = 9;
patc = inter - 24;
} else {
patc = inter;
}
label.text("Patch " + seas + "." + patc);
}
function interpolateValues(values, number) {
old = Math.floor(number);
upd = Math.ceil(number);
var old_data = values.filter(function(d) {return d.internal == old;});
var new_data = values.filter(function(d) {return d.internal == upd;});
var oobj = old_data[0];
var nobj = new_data[0];
var onum = oobj[Object.keys(oobj)[4]];
var nnum = nobj[Object.keys(nobj)[4]];
var difint = number - old;
var difdis = 0;
var newnum = nnum;
if (nnum > onum) {
difdis = nnum - onum;
newnum = ((difint) * difdis) + onum;
} else if (onum > nnum) {
difdis = onum - nnum;
newnum = onum - ((difint) * difdis);
}
return newnum;
}
I believe switching my SVG to a canvas may help things, but since I have no knowledge of canvas I'd rather leave that as a last resort.

How to reduce a data graph but keeping the extremes

I have a database that has got a month full of datasets in 10min intervals. (So a dataset for every 10min)
Now I want to show that data on three graphs: last 24 hours, last 7 days and last 30 days.
The data looks like this:
{ "data" : 278, "date" : ISODate("2016-08-31T01:51:05.315Z") }
{ "data" : 627, "date" : ISODate("2016-08-31T01:51:06.361Z") }
{ "data" : 146, "date" : ISODate("2016-08-31T01:51:07.938Z") }
// etc
For the 24h graph I simply output the data for the last 24h, that's easy.
For the other graphs I thin the data:
const data = {}; //data from database
let newData = [];
const interval = 7; //for 7 days the interval is 7, for 30 days it's 30
for( let i = 0; i < data.length; i += interval ) {
newData.push( data[ i ] );
};
This works fine but extreme events where data is 0 or differs greatly from the other values average, can be lost depending on what time you search the data. Not thinning out the data however will result in a large sum of data points that are sent over the pipe and have to be processed on the front end. I'd like to avoid that.
Now to my question
How can I reduce the data for a 7 day period while keeping extremes in it? What's the most efficient way here?
Additions:
In essence I think I'm trying to simplify a graph to reduce points but keep the overall shape. (If you look at it from a pure image perspective)
Something like an implementation of Douglas–Peucker algorithm in node?

As you mention in the comments, the Ramer-Douglas-Peucker (RDP) algorithm is used to process data points in 2D figures but you want to use it for graph data where X values are fixed. I modified this Javascript implementation of the algorithm provided by M Oehm to consider only the vertical (Y) distance in the calculations.
On the other hand, data smoothing is often suggested to reduce the number of data points in a graph (see this post by csgillespie).
In order to compare the two methods, I made a small test program. The Reset button creates new test data. An algorithm can be selected and applied to obtain a reduced number of points, separated by the specified interval. In the case of the RDP algorithm however, the resulting points are not evenly spaced. To get the same number of points as for the specified interval, I run the calculations iteratively, adjusting the espilon value each time until the correct number of points is reached.
From my tests, the RDP algorithm gives much better results. The only downside is that the spacing between points varies. I don't think that this can be avoided, given that we want to keep the extreme points which are not evenly distributed in the original data.
Here is the code snippet, which is better seen in Full Page mode:
var svgns = 'http://www.w3.org/2000/svg';
var graph = document.getElementById('graph1');
var grpRawData = document.getElementById('grpRawData');
var grpCalculatedData = document.getElementById('grpCalculatedData');
var btnReset = document.getElementById('btnReset');
var cmbMethod = document.getElementById('cmbMethod');
var btnAddCalculated = document.getElementById('btnAddCalculated');
var btnClearCalculated = document.getElementById('btnClearCalculated');
var data = [];
var calculatedCount = 0;
var colors = ['black', 'red', 'green', 'blue', 'orange', 'purple'];
var getPeriod = function () {
return parseInt(document.getElementById('txtPeriod').value, 10);
};
var clearGroup = function (grp) {
while (grp.lastChild) {
grp.removeChild(grp.lastChild);
}
};
var showPoints = function (grp, pts, markerSize, color) {
var i, point;
for (i = 0; i < pts.length; i++) {
point = pts[i];
var marker = document.createElementNS(svgns, 'circle');
marker.setAttributeNS(null, 'cx', point.x);
marker.setAttributeNS(null, 'cy', point.y);
marker.setAttributeNS(null, 'r', markerSize);
marker.setAttributeNS(null, 'fill', color);
grp.appendChild(marker);
}
};
// Create and display test data
var showRawData = function () {
var i, x, y;
var r = 0;
data = [];
for (i = 1; i < 500; i++) {
x = i;
r += 15.0 * (Math.random() * Math.random() - 0.25);
y = 150 + 30 * Math.sin(x / 200) * Math.sin((x - 37) / 61) + 2 * Math.sin((x - 7) / 11) + r;
data.push({ x: x, y: y });
}
showPoints(grpRawData, data, 1, '#888');
};
// Gaussian kernel smoother
var createGaussianKernelData = function () {
var i, x, y;
var r = 0;
var result = [];
var period = getPeriod();
for (i = Math.floor(period / 2) ; i < data.length; i += period) {
x = data[i].x;
y = gaussianKernel(i);
result.push({ x: x, y: y });
}
return result;
};
var gaussianKernel = function (index) {
var halfRange = Math.floor(getPeriod() / 2);
var distance, factor;
var totalValue = 0;
var totalFactor = 0;
for (i = index - halfRange; i <= index + halfRange; i++) {
if (0 <= i && i < data.length) {
distance = Math.abs(i - index);
factor = Math.exp(-Math.pow(distance, 2));
totalFactor += factor;
totalValue += data[i].y * factor;
}
}
return totalValue / totalFactor;
};
// Ramer-Douglas-Peucker algorithm
var ramerDouglasPeuckerRecursive = function (pts, first, last, eps) {
if (first >= last - 1) {
return [pts[first]];
}
var slope = (pts[last].y - pts[first].y) / (pts[last].x - pts[first].x);
var x0 = pts[first].x;
var y0 = pts[first].y;
var iMax = first;
var max = -1;
var p, dy;
// Calculate vertical distance
for (var i = first + 1; i < last; i++) {
p = pts[i];
y = y0 + slope * (p.x - x0);
dy = Math.abs(p.y - y);
if (dy > max) {
max = dy;
iMax = i;
}
}
if (max < eps) {
return [pts[first]];
}
var p1 = ramerDouglasPeuckerRecursive(pts, first, iMax, eps);
var p2 = ramerDouglasPeuckerRecursive(pts, iMax, last, eps);
return p1.concat(p2);
}
var internalRamerDouglasPeucker = function (pts, eps) {
var p = ramerDouglasPeuckerRecursive(data, 0, pts.length - 1, eps);
return p.concat([pts[pts.length - 1]]);
}
var createRamerDouglasPeuckerData = function () {
var finalPointCount = Math.round(data.length / getPeriod());
var epsilon = getPeriod();
var pts = internalRamerDouglasPeucker(data, epsilon);
var iteration = 0;
// Iterate until the correct number of points is obtained
while (pts.length != finalPointCount && iteration++ < 20) {
epsilon *= Math.sqrt(pts.length / finalPointCount);
pts = internalRamerDouglasPeucker(data, epsilon);
}
return pts;
};
// Event handlers
btnReset.addEventListener('click', function () {
calculatedCount = 0;
clearGroup(grpRawData);
clearGroup(grpCalculatedData);
showRawData();
});
btnClearCalculated.addEventListener('click', function () {
calculatedCount = 0;
clearGroup(grpCalculatedData);
});
btnAddCalculated.addEventListener('click', function () {
switch (cmbMethod.value) {
case "Gaussian":
showPoints(grpCalculatedData, createGaussianKernelData(), 2, colors[calculatedCount++]);
break;
case "RDP":
showPoints(grpCalculatedData, createRamerDouglasPeuckerData(), 2, colors[calculatedCount++]);
return;
}
});
showRawData();
div
{
margin-bottom: 6px;
}
<div>
<button id="btnReset">Reset</button>
<select id="cmbMethod">
<option value="RDP">Ramer-Douglas-Peucker</option>
<option value="Gaussian">Gaussian kernel</option>
</select>
<label for="txtPeriod">Interval: </label>
<input id="txtPeriod" type="text" style="width: 36px;" value="7" />
</div>
<div>
<button id="btnAddCalculated">Add calculated points</button>
<button id="btnClearCalculated">Clear calculated points</button>
</div>
<svg id="svg1" width="765" height="450" viewBox="0 0 510 300">
<g id="graph1" transform="translate(0,300) scale(1,-1)">
<rect width="500" height="300" stroke="black" fill="#eee"></rect>
<g id="grpRawData"></g>
<g id="grpCalculatedData"></g>
</g>
</svg>

Sort Colour / Color Values

Im looking to align the following array of colours as precisely as possible.
After searching & trying many solutions suggested on Stackoverflow, the pusher.color library has the best solution, however, it too is far from perfect. I would like to hear solutions on how we can align them perfectly.
JSFIDDLE LINK : http://jsfiddle.net/dxux7y3e/
Code:
var coloursArray=['#FFE9E9','#B85958','#FFB1AE','#FFC2BF','#C55E58','#FFC7C4','#FF9A94','#FF9D96','#FA9790','#A78B88','#A78B88','#CE675B','#DB8073','#FF9D90','#FF7361','#FFD6D1','#F9A092','#FF7B67','#EBACA2','#FF806D','#DD6D5B','#D16654','#ED8673','#FFC4B8','#E2725B','#ED7A64','#8F3926','#BD492F','#9D3C27','#AD533E','#BF4024','#FFC9BC','#6B6766','#E1CDC8','#C2654C','#B3978F','#FFC7B8','#CE2B00','#C2654C','#A24D34','#FF926D','#E78667','#FFB198','#8C756D','#9E6D5B','#FFC7B0','#FFBEA4','#D2B9AF','#FFB193','#632710','#B26746','#976854','#F44900','#E79873','#EFA27F','#532510','#BC866B','#FDE5D9','#FF5B00','#D18C67','#FF5B00','#9E4312','#763713','#BB6B39','#B5622E','#CC7742','#6D4227','#B56B38','#FF7518','#F3B080','#995C30','#995C30','#FF6A00','#D89769','#71472A','#EDAC7B','#EEAB79','#EBCFB9','#FBE3D1','#E19255','#5E381B','#FFDCC1','#FFF0E4','#F68D39','#7B5B40','#FF8313','#FFCEA4','#AA8667','#975414','#CB9867','#8C5B2B','#FFCE9E','#7B4714','#FFF3E7','#FFA449','#CEAF90','#CDB69E','#EFD6BC','#DDA66B','#B27737','#B88A57','#CE9B61','#F4C38B','#543817','#BC9C78','#DBB07A','#FF8E04','#F6EADB','#DBC2A4','#C49B64','#CBA26B','#80551E','#FF9200','#FFECD3','#FFC87C','#FFB755','#DBB680','#D2D0CD','#EFDBBE','#E5C18B','#FFE5BC','#F2EADB','#885F12','#FFE7B6','#825A08','#906712','#F2D18E','#C8C6C2','#FFB000','#FFC243','#C6BEAD','#D0C3A4','#916800','#8C6700','#F4E9CA','#FFF0C5','#FFE080','#FFEBA8','#846600','#FFE692','#F5F0DB','#433F2F','#BBB394','#FFEFAA','#FFE76D','#FFFAE0','#3E3B28','#554900','#E1E0D8','#74725C','#605F54','#F8F7DD','#A5A467','#DDDDDA','#FFFFEE','#A3A39D','#E0E0D7','#BEBEB9','#E8E8E5','#454531','#ACACAA','#E9E9DF','#FFFFDC','#EBEBE7','#979831','#C5C6BE','#B9C866','#898D72','#F3FAD1','#616452','#CED5B0','#A1A787','#595C4E','#B0BB8C','#EEFFB6','#ACB78E','#8FA359','#858F6C','#86916E','#374912','#AEB0AA','#79904C','#627739','#747F60','#9FA98E','#E7F9CB','#E1F9BE','#495637','#8A9978','#4E5F39','#86996E','#C3CEB7','#78866B','#CEDDC1','#B5CEA2','#536149','#D6E6CC','#D6E6CC','#809873','#4F564C','#4F6C45','#555F52','#4F7942','#5F705B','#D0DFCD','#2B3929','#F0F7EF','#AAD5A4','#99BC95','#B6D4B4','#869E86','#618661','#006700','#E9EEE9','#739E73','#005B06','#EDF7EE','#D0E0D2','#809784','#ABCEB1','#C0E0C8','#3A5241','#435549','#E6ECE8','#E3EAE6','#3B604C','#00602F','#92B7A5','#2F5B49','#318061','#30745B','#316955','#00A275','#C2D1CE','#80A7A0','#00A082','#C2D1CF','#5C6E6C','#607473','#EDF7F7','#1E8285','#D5E7E8','#AADEE1','#188086','#107F87','#566364','#007B86','#66949A','#CAE2E5','#18656F','#004F61','#0C5B6C','#668E98','#BBD0DA','#91B4C5','#AFC3CD','#738A99','#3A5467','#476174','#244967','#556C80','#667A8C','#516D87','#1E4263','#7C8791','#849CB6','#738CAA','#1E3A5F','#1E3655','#9EB0CE','#B6BAC2','#67738D','#BEC1CD','#555559','#616180','#000049','#000031','#F8F8FC','#938BA4','#47375D','#F7F6F8','#3D0067','#514C53','#9566A2','#7F5482','#A279A4','#6D1261','#A06492','#925582','#945B80','#CE94BA','#ECCFE1','#A20058','#A6005B','#BC0061','#BB0061','#F3CEE1','#B3005B','#AB165F','#8A184D','#AA185B','#F3DAE4','#DB3779','#E71261','#E74F86','#FFD6E5','#BE9BA7','#D0396A','#DB1855','#F798B6','#9C294A','#D62B5B','#DE3969','#BC1641','#E7547A','#D52756','#9C7D85','#DB244F','#A1354F','#C22443','#FFBDCA','#8B6D73','#DC3D5B','#FF738C','#F13154','#BC4055','#FED4DB','#FFCFD6','#CB4E61','#ED455A','#F36C7B','#C94F5B','#F3959D','#A8444C','#FFCCD0','#735B5D','#D15D67','#B44B52','#FD868D','#FFD5D8','#C3767B','#FF8087','#C8242B','#FFEAEB','#F95A61','#E96D73','#E6656B','#FF6D73','#FF555B','#A35A5B','#FFD3D4','#B84B4D'];
var body=document.getElementsByTagName('body')[0];
function hexToRgb(hex) {
hex = hex.substring(1, hex.length);
var r = parseInt((hex).substring(0, 2), 16);
var g = parseInt((hex).substring(2, 4), 16);
var b = parseInt((hex).substring(4, 6), 16);
return r + "," + g + "," + b;
}
function rgbToHex(r, g, b) {
return "#" + ((1 << 24) + (r << 16) + (g << 8) + b).toString(16).slice(1);
}
var rgbArr=new Array();
var div=document.createElement('div');
div.id='Original';
body.appendChild(div);
for(var color in coloursArray){
color=coloursArray[color];
displayColor(color,div);
rgbArr.push(hexToRgb(color));
}
var hslArr=new Array();
for(var i=0;i<rgbArr.length;i++){
//Transforming rgb to hsl
//`hslArr[i][1]` (`i`) is a reference to the rgb color, in order to retrieve it later
hslArr[i]=[rgbToHsl(rgbArr[i]),i];
}
var sortedHslArr=new Array();
//Sorting `hslArr` into `sortedHslArr`
outerloop:
for(var i=0;i<hslArr.length;i++){
for(var j=0;j<sortedHslArr.length;j++){
if(sortedHslArr[j][0][0]>hslArr[i][0][0]){
sortedHslArr.splice(j,0,hslArr[i]);
continue outerloop;
}
}
sortedHslArr.push(hslArr[i]);
}
var sortedRgbArr=new Array();
//Retrieving rgb colors
for(var i=0;i<sortedHslArr.length;i++){
sortedRgbArr[i]=rgbArr[sortedHslArr[i][1]];
}
function displayColor(color,parent){
var div;
div=document.createElement('div');
div.style.backgroundColor=color;
div.style.width='22px';
div.style.height='22px';
div.style.cssFloat='left';
div.style.position='relative';
parent.appendChild(div);
}
var finalArray=new Array();
var div=document.createElement('div');
div.id='Sorted';
body.appendChild(div);
for(var color in sortedRgbArr){
color=sortedRgbArr[color];
color=color.split(',');
color=rgbToHex(parseInt(color[0]),parseInt(color[1]),parseInt(color[2]));
displayColor(color,div);
finalArray.push(color);
}
function rgbToHsl(c){
var r = c[0]/255, g = c[1]/255, b = c[2]/255;
var max = Math.max(r, g, b), min = Math.min(r, g, b);
var h, s, l = (max + min) / 2;
if(max == min){
h = s = 0; // achromatic
}else{
var d = max - min;
s = l > 0.5 ? d / (2 - max - min) : d / (max + min);
switch(max){
case r: h = (g - b) / d + (g < b ? 6 : 0); break;
case g: h = (b - r) / d + 2; break;
case b: h = (r - g) / d + 4; break;
}
h /= 6;
}
return new Array(h * 360, s * 100, l * 100);
}
var sorted = coloursArray.sort(function(colorA, colorB) {
return pusher.color(colorA).hue() - pusher.color(colorB).hue();
});
// console.log(sorted);
var div=document.createElement('div');
div.id='Pusher';
body.appendChild(div);
for(var color in sorted){
color=sorted[color];
displayColor(color,div);
}
var div=document.createElement('div');
body.appendChild(div);
var str='';
for(var color in sorted){
color=sorted[color];
str+='\''+color+'\',';
}
div.innerHTML=str;
function sorthueColors (colors) {
for (var c = 0; c < colors.length; c++) {
/* Get the hex value without hash symbol. */
var hex = colors[c].substring(1);
//var hex = colors[c].hex.substring(1);
/* Get the RGB values to calculate the Hue. */
var r = parseInt(hex.substring(0,2),16)/255;
var g = parseInt(hex.substring(2,4),16)/255;
var b = parseInt(hex.substring(4,6),16)/255;
/* Getting the Max and Min values for Chroma. */
var max = Math.max.apply(Math, [r,g,b]);
var min = Math.min.apply(Math, [r,g,b]);
/* Variables for HSV value of hex color. */
var chr = max-min;
var hue = 0;
var val = max;
var sat = 0;
if (val > 0) {
/* Calculate Saturation only if Value isn't 0. */
sat = chr/val;
if (sat > 0) {
if (r == max) {
hue = 60*(((g-min)-(b-min))/chr);
if (hue < 0) {hue += 360;}
} else if (g == max) {
hue = 120+60*(((b-min)-(r-min))/chr);
} else if (b == max) {
hue = 240+60*(((r-min)-(g-min))/chr);
}
}
}
/* Modifies existing objects by adding HSV values. */
colors[c].hue = hue;
colors[c].sat = sat;
colors[c].val = val;
}
/* Sort by Hue. */
return colors.sort(function(a,b){return a.hue - b.hue;});
}

Problem is, sorting requires a well-defined order - in other words, you need to map all colors to one dimension. While there are some approaches to display the color space in two dimensions, I am not aware of any that would display it in one dimension and still make sense to the human eye.
However, if you don't insist on some universal ordering and you merely want to place a given list of colors in a way that looks nice, then some clustering approach might produce better results. I tried the naive approach, the idea here is to put similar colors into the same cluster and to merge these clusters until you only have one. Here is the code I've got:
function colorDistance(color1, color2) {
// This is actually the square of the distance but
// this doesn't matter for sorting.
var result = 0;
for (var i = 0; i < color1.length; i++)
result += (color1[i] - color2[i]) * (color1[i] - color2[i]);
return result;
}
function sortColors(colors) {
// Calculate distance between each color
var distances = [];
for (var i = 0; i < colors.length; i++) {
distances[i] = [];
for (var j = 0; j < i; j++)
distances.push([colors[i], colors[j], colorDistance(colors[i], colors[j])]);
}
distances.sort(function(a, b) {
return a[2] - b[2];
});
// Put each color into separate cluster initially
var colorToCluster = {};
for (var i = 0; i < colors.length; i++)
colorToCluster[colors[i]] = [colors[i]];
// Merge clusters, starting with lowest distances
var lastCluster;
for (var i = 0; i < distances.length; i++) {
var color1 = distances[i][0];
var color2 = distances[i][1];
var cluster1 = colorToCluster[color1];
var cluster2 = colorToCluster[color2];
if (!cluster1 || !cluster2 || cluster1 == cluster2)
continue;
// Make sure color1 is at the end of its cluster and
// color2 at the beginning.
if (color1 != cluster1[cluster1.length - 1])
cluster1.reverse();
if (color2 != cluster2[0])
cluster2.reverse();
// Merge cluster2 into cluster1
cluster1.push.apply(cluster1, cluster2);
delete colorToCluster[color1];
delete colorToCluster[color2];
colorToCluster[cluster1[0]] = cluster1;
colorToCluster[cluster1[cluster1.length - 1]] = cluster1;
lastCluster = cluster1;
}
// By now all colors should be in one cluster
return lastCluster;
}
Complete fiddle
The colorDistance() function works with RGB colors that are expressed as arrays with three elements. There are certainly much better approaches to do this and they might produce results that look better. Note also that this isn't the most efficient algorithm because it calculates the distance between each and every color (O(n2)) so if you have lots of colors you might want to optimize it.

Assuming there is no best solution. Here is how i would find what i like the most.
First i would use color distance function:
var balance = [10, 0, 0.01];
function colorDistance(color1, color2) {
var result = 0;
color1 = rgbToHsl(color1[0], color1[1], color1[2]);
color2 = rgbToHsl(color2[0], color2[1], color2[2]);
for (var i = 0; i < color1.length; i++)
result += (color1[i] - color2[i]) * (color1[i] - color2[i]) * balance[i];
return result;
}
I would use balance function :
hue distance var balance = [1, 0, 0];
saturation distance var balance = [0, 1, 0];
lightness distance var balance = [0, 0, 1];
After that you can just experiment and pick what you like. Note that values are not normalized to appear between 0 to 1 but other then that it should work well. You can also you another distance function this is just most trivial and well known one that gets decent results.
This approach does not give you the best solution, but describes a way to fiddle with it and get result you where looking for. Have fun.

Using the Hue of a color is usually a pleasant way to map the color on a one-dimensional scale. You could convert the rgb color to hsv using the answers here: RGB to HSV color in javascript?

This is an improved code based on pusher.color lib.
https://jsfiddle.net/kt1zv6g4/3/