three.cad/extlib/cubicBezier.js

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/**
* https://github.com/gre/bezier-easing
* BezierEasing - use bezier curve for transition easing function
* by Gaëtan Renaudeau 2014 - 2015 MIT License
*/
// 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;
}
function LinearEasing(x) {
return x;
}
export const Bezier = function (mX1, mY1, mX2, mY2) {
if (!(0 <= mX1 && mX1 <= 1 && 0 <= mX2 && mX2 <= 1)) {
throw new Error('bezier x values must be in [0, 1] range');
}
if (mX1 === mY1 && mX2 === mY2) {
return LinearEasing;
}
// Precompute samples table
var sampleValues = float32ArraySupported ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);
for (var i = 0; i < kSplineTableSize; ++i) {
sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);
}
function getTForX(aX) {
var intervalStart = 0.0;
var currentSample = 1;
var lastSample = kSplineTableSize - 1;
for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) {
intervalStart += kSampleStepSize;
}
--currentSample;
// Interpolate to provide an initial guess for t
var dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[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 function BezierEasing(x) {
// Because JavaScript number are imprecise, we should guarantee the extremes are right.
if (x === 0 || x === 1) {
return x;
}
return calcBezier(getTForX(x), mY1, mY2);
};
};