[New feature] CCT - Correlated Color Temperature

[heinemannj]

Correlated Color Temperature (CCT) (in K)
Returns a correlated color temperature.

Option A:
Based on Bruce Justin Lindbloom's implementation
http://www.brucelindbloom.com/Eqn_XYZ_to_T.html

Option B:
based on McCamy cubic approximation
https://en.wikipedia.org/wiki/Color_temperature

var xyz65_Sum = xyz65.x + xyz65.y + xyz65.z;

if (xyz65_Sum !== 0) {
	xyY65.x = xyz65.x / xyz65_Sum;
	xyY65.y = xyz65.y / xyz65_Sum;
	xyY65.Y = xyz65.y;
} else {
	xyY65.x = 0;
	xyY65.y = 0;
	xyY65.Y = 0;
}

var n = (xyY65.x - 0.3320) / (0.1858 - xyY65.y);
var cct = (449.0 * Math.pow(n, 3)) + (3525.0 * Math.pow(n, 2)) + (6823.3 * n) + 5520.33;

Benefit of option B:

Caculation of xyY coordinates which are actually not covered by culori

[danburzo]

Thanks for opening this issue, CCT is one of the features I've been considering. In general, I would prefer a concise solution, where available, that doesn't rely on large lookup tables. I'll have to research the topic a bit.

[heinemannj]

With the help of below 2 functions I'm able to calculate the CCT in very flexible way:
(working for all White references (eg. D50, D65, ...)

function cct({mode, x, y, z}) {

	let xyY = convertXYZToxyY({mode, x, y, z});

	var n = (xyY.x - 0.3320) / (0.1858 - xyY.y);
	var res = (449.0 * Math.pow(n, 3)) + (3525.0 * Math.pow(n, 2)) + (6823.3 * n) + 5520.33;
	return res;
}

function convertXYZToxyY({mode, x, y, z}) {
	var sum = x + y + z;

	if (sum !== 0) {
		Y = y;
		x = x / sum;
		y = y / sum;
	} else {
		Y = 0;
		x = 0;
		y = 0;
	}

	let res = {
		mode: mode.replace("xyz", "xyY"),
		x: x,
		y: y,
		Y: Y
	}
	return res;
}

For CCT calculation:
SensorData[4]['MeasuredValue']=parseFloat(cct(xyz65)).toFixed(0);

For transformation into CIE-xyY coordinates:
let xyY =convertXYZToxyY(xyz65));

[danburzo]

This should also be an interesting read on the topic: https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-24-13-14066&id=344803

[heinemannj]

It's up to you to choose from available solutions.

McCamy cubic approximation is very easy to implement - but for sure not the best solution in terms of accuracy

[heinemannj]

Option A:
Based on Bruce Justin Lindbloom's implementation
http://www.brucelindbloom.com/Eqn_XYZ_to_T.html

Bruce Justin Lindbloom has created Javascript code based on Robertson method:
http://www.brucelindbloom.com/javascript/ColorConv.js

The deviation of the Robertson method (+/-12) compared with McCamy (+/- 284) is much more better.

Actually the most accurate solution seems to be the Y. Ohno's CCT (combined solution) calculation methods (+/-1)
http://www.bipublication.com/files/IJABR-V7I2-2016-29Zheleznikova.pdf

Phython script available on Colour's Git:
https://github.com/colour-science/colour/blob/develop/colour/temperature/ohno2013.py