| /* eslint-disable */ |
| const 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); |
| } if (initialSlope === 0.0) { |
| return guessForT; |
| } |
| return binarySubdivide(aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2); |
| }, |
| }; |
| |
| return ob; |
| }()); |
| |
| export default BezierFactory; |