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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
- *
- * https://github.com/manubb/Leaflet.PixiOverlay
- */
- (function(f) {
- if (typeof exports === 'object' && typeof module !== 'undefined') {
- module.exports = f();
- } else if (typeof define === 'function' && define.amd) {
- define([], f);
- } else {
- var g;
- if (typeof window !== 'undefined') {
- g = window;
- } else if (typeof global !== 'undefined') {
- g = global;
- } else if (typeof self !== 'undefined') {
- g = self;
- } else {
- g = this;
- }
- g.BezierEasing = f();
- }
- })(function() {
- var define, module, exports;
- return (function() {
- function r(e, n, t) {
- function o(i, f) {
- if (!n[i]) {
- if (!e[i]) {
- var c = 'function' == typeof require && require;
- if (!f && c) return c(i, !0);
- if (u) return u(i, !0);
- var a = new Error("Cannot find module '" + i + "'");
- throw ((a.code = 'MODULE_NOT_FOUND'), a);
- }
- var p = (n[i] = { exports: {} });
- e[i][0].call(
- p.exports,
- function(r) {
- var n = e[i][1][r];
- return o(n || r);
- },
- p,
- p.exports,
- r,
- e,
- n,
- t
- );
- }
- return n[i].exports;
- }
- for (var u = 'function' == typeof require && require, i = 0; i < t.length; i++) o(t[i]);
- return o;
- }
- return r;
- })()(
- {
- 1: [
- function(require, module, exports) {
- /**
- * 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;
- }
- module.exports = function bezier(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) {
- return 0;
- }
- if (x === 1) {
- return 1;
- }
- return calcBezier(getTForX(x), mY1, mY2);
- };
- };
- },
- {}
- ]
- },
- {},
- [1]
- )(1);
- });
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