TERP is a minimal interpolation library which is AMD compatible for normalizing, scaling and mapping values.
##scale
scale is the core TERP method. It accepts 3-6 inputs
With 3 or 4 arguments, the input is assumed to be between 0 and 1.
/**
* @param {number} input
* @param {number} outputMin
* @param {number} outputMax
* @param {number=} exponent (optional exponent which will change the interpolation curve)
*
* @return {number}
*/examples:
TERP.scale(.5, 100, 200);
//returns 150
TERP.scale(.5, 100, 200, 2)
// returns 125With 5 or 6 arguments, the input the second and third arguments define the input range
/**
* @param {number} input
* @param {number} inputMin
* @param {number} inputMax
* @param {number} outputMin
* @param {number} outputMax
* @param {number=} exponent (optional exponent which will change the interpolation curve)
*
* @return {number}
*/examples:
TERP.scale(50, 0, 100, 100, 1000, 2);
//returns 325
TERP.scale(-1, -2, 0, 0, 1000);
//returns 500##map
map is just like scale, but will clip the output to the output range
##normalize
normalize normalize will scale an input within an input range to 0-1
/**
* @param {number} input
* @param {number} inputMin
* @param {number} inputMax
* @return {number}
*/#Arrays
There are array version of each of those functions.
normalizeArray, scaleArray, and mapArray are the same as the above functions, but take an array as their first argument and return an array of the same length as the first argument with each of the values interpolated.
normalizeArray can accept one argument, in which case it will set the inputMin and inputMax to the smallest and largest values in the array.
//with one argument:
TERP.normalizeArray([1, 2, 3, 4, 5]);
// returns [0, .4, .6, .8, 1];
//with three arguments:
TERP.normalizeArray([1, 2, 3, 4, 5], 0, 10);
// returns [0, .2, .3, .4, 5];