blob: 82c426871f810c94493e266e0b39e455bd7d52bc [file] [log] [blame]
var BezierFactory = (function(){
/**
* BezierEasing - use bezier curve for transition easing function
* by Gaëtan Renaudeau 2014 - 2015 – MIT License
*
* Credits: is based on Firefox's nsSMILKeySpline.cpp
* Usage:
* var spline = BezierEasing([ 0.25, 0.1, 0.25, 1.0 ])
* spline.get(x) => returns the easing value | x must be in [0, 1] range
*
*/
var ob = {};
ob.getBezierEasing = getBezierEasing;
var beziers = {};
function getBezierEasing(a,b,c,d,nm){
var str = nm || ('bez_' + a+'_'+b+'_'+c+'_'+d).replace(/\./g, 'p');
if(beziers[str]){
return beziers[str];
}
var bezEasing = new BezierEasing([a,b,c,d]);
beziers[str] = bezEasing;
return bezEasing;
}
// These values are established by empiricism with tests (tradeoff: performance VS precision)
var NEWTON_ITERATIONS = 4;
var NEWTON_MIN_SLOPE = 0.001;
var SUBDIVISION_PRECISION = 0.0000001;
var SUBDIVISION_MAX_ITERATIONS = 10;
var kSplineTableSize = 11;
var kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);
var float32ArraySupported = typeof Float32Array === 'function';
function A (aA1, aA2) { return 1.0 - 3.0 * aA2 + 3.0 * aA1; }
function B (aA1, aA2) { return 3.0 * aA2 - 6.0 * aA1; }
function C (aA1) { return 3.0 * aA1; }
// Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2.
function calcBezier (aT, aA1, aA2) {
return ((A(aA1, aA2)*aT + B(aA1, aA2))*aT + C(aA1))*aT;
}
// Returns dx/dt given t, x1, and x2, or dy/dt given t, y1, and y2.
function getSlope (aT, aA1, aA2) {
return 3.0 * A(aA1, aA2)*aT*aT + 2.0 * B(aA1, aA2) * aT + C(aA1);
}
function binarySubdivide (aX, aA, aB, mX1, mX2) {
var currentX, currentT, i = 0;
do {
currentT = aA + (aB - aA) / 2.0;
currentX = calcBezier(currentT, mX1, mX2) - aX;
if (currentX > 0.0) {
aB = currentT;
} else {
aA = currentT;
}
} while (Math.abs(currentX) > SUBDIVISION_PRECISION && ++i < SUBDIVISION_MAX_ITERATIONS);
return currentT;
}
function newtonRaphsonIterate (aX, aGuessT, mX1, mX2) {
for (var i = 0; i < NEWTON_ITERATIONS; ++i) {
var currentSlope = getSlope(aGuessT, mX1, mX2);
if (currentSlope === 0.0) return aGuessT;
var currentX = calcBezier(aGuessT, mX1, mX2) - aX;
aGuessT -= currentX / currentSlope;
}
return aGuessT;
}
/**
* points is an array of [ mX1, mY1, mX2, mY2 ]
*/
function BezierEasing (points) {
this._p = points;
this._mSampleValues = float32ArraySupported ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);
this._precomputed = false;
this.get = this.get.bind(this);
}
BezierEasing.prototype = {
get: function (x) {
var mX1 = this._p[0],
mY1 = this._p[1],
mX2 = this._p[2],
mY2 = this._p[3];
if (!this._precomputed) this._precompute();
if (mX1 === mY1 && mX2 === mY2) return x; // linear
// Because JavaScript number are imprecise, we should guarantee the extremes are right.
if (x === 0) return 0;
if (x === 1) return 1;
return calcBezier(this._getTForX(x), mY1, mY2);
},
// Private part
_precompute: function () {
var mX1 = this._p[0],
mY1 = this._p[1],
mX2 = this._p[2],
mY2 = this._p[3];
this._precomputed = true;
if (mX1 !== mY1 || mX2 !== mY2)
this._calcSampleValues();
},
_calcSampleValues: function () {
var mX1 = this._p[0],
mX2 = this._p[2];
for (var i = 0; i < kSplineTableSize; ++i) {
this._mSampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);
}
},
/**
* getTForX chose the fastest heuristic to determine the percentage value precisely from a given X projection.
*/
_getTForX: function (aX) {
var mX1 = this._p[0],
mX2 = this._p[2],
mSampleValues = this._mSampleValues;
var intervalStart = 0.0;
var currentSample = 1;
var lastSample = kSplineTableSize - 1;
for (; currentSample !== lastSample && mSampleValues[currentSample] <= aX; ++currentSample) {
intervalStart += kSampleStepSize;
}
--currentSample;
// Interpolate to provide an initial guess for t
var dist = (aX - mSampleValues[currentSample]) / (mSampleValues[currentSample+1] - mSampleValues[currentSample]);
var guessForT = intervalStart + dist * kSampleStepSize;
var initialSlope = getSlope(guessForT, mX1, mX2);
if (initialSlope >= NEWTON_MIN_SLOPE) {
return newtonRaphsonIterate(aX, guessForT, mX1, mX2);
} else if (initialSlope === 0.0) {
return guessForT;
} else {
return binarySubdivide(aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2);
}
}
};
return ob;
}());