/**
 * 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)
const NEWTON_ITERATIONS = 4;
const NEWTON_MIN_SLOPE = 0.001;
const SUBDIVISION_PRECISION = 0.0000001;
const SUBDIVISION_MAX_ITERATIONS = 10;
const kSplineTableSize = 11;
const kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);
const 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) {
    let 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 (let i = 0; i < NEWTON_ITERATIONS; ++i) {
        let currentSlope = getSlope(aGuessT, mX1, mX2);
        if (currentSlope === 0.0) {
            return aGuessT;
        }
        let currentX = calcBezier(aGuessT, mX1, mX2) - aX;
        aGuessT -= currentX / currentSlope;
    }
    return aGuessT;
}
function LinearEasing(x) {
    return x;
}
export default 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
    let sampleValues = float32ArraySupported ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);
    for (let i = 0; i < kSplineTableSize; ++i) {
        sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);
    }
    function getTForX(aX) {
        let intervalStart = 0.0;
        let currentSample = 1;
        let lastSample = kSplineTableSize - 1;
        for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) {
            intervalStart += kSampleStepSize;
        }
        --currentSample;
        // Interpolate to provide an initial guess for t
        let dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]);
        let guessForT = intervalStart + dist * kSampleStepSize;
        let 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);
    };
}
;
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use bezier curve for transition easing function\r\n * by Gaëtan Renaudeau 2014 - 2015 – MIT License\r\n */\r\n\r\n// These values are established by empiricism with tests (tradeoff: performance VS precision)\r\nconst NEWTON_ITERATIONS = 4;\r\nconst NEWTON_MIN_SLOPE = 0.001;\r\nconst SUBDIVISION_PRECISION = 0.0000001;\r\nconst SUBDIVISION_MAX_ITERATIONS = 10;\r\n\r\nconst kSplineTableSize = 11;\r\nconst kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);\r\n\r\nconst float32ArraySupported = typeof Float32Array === 'function';\r\n\r\nfunction A(aA1, aA2) {\r\n  return 1.0 - 3.0 * aA2 + 3.0 * aA1;\r\n}\r\n\r\nfunction B(aA1, aA2) {\r\n  return 3.0 * aA2 - 6.0 * aA1;\r\n}\r\n\r\nfunction C(aA1) {\r\n  return 3.0 * aA1;\r\n}\r\n\r\n// Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2.\r\nfunction calcBezier(aT, aA1, aA2) {\r\n  return ((A(aA1, aA2) * aT + B(aA1, aA2)) * aT + C(aA1)) * aT;\r\n}\r\n\r\n// Returns dx/dt given t, x1, and x2, or dy/dt given t, y1, and y2.\r\nfunction getSlope(aT, aA1, aA2) {\r\n  return 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1);\r\n}\r\n\r\nfunction binarySubdivide(aX, aA, aB, mX1, mX2) {\r\n  let currentX, currentT, i = 0;\r\n  do {\r\n    currentT = aA + (aB - aA) / 2.0;\r\n    currentX = calcBezier(currentT, mX1, mX2) - aX;\r\n    if (currentX > 0.0) {\r\n      aB = currentT;\r\n    } else {\r\n      aA = currentT;\r\n    }\r\n  } while (Math.abs(currentX) > SUBDIVISION_PRECISION && ++i < SUBDIVISION_MAX_ITERATIONS);\r\n  return currentT;\r\n}\r\n\r\nfunction newtonRaphsonIterate(aX, aGuessT, mX1, mX2) {\r\n  for (let i = 0; i < NEWTON_ITERATIONS; ++i) {\r\n    let currentSlope = getSlope(aGuessT, mX1, mX2);\r\n    if (currentSlope === 0.0) {\r\n      return aGuessT;\r\n    }\r\n    let currentX = calcBezier(aGuessT, mX1, mX2) - aX;\r\n    aGuessT -= currentX / currentSlope;\r\n  }\r\n  return aGuessT;\r\n}\r\n\r\nfunction LinearEasing(x) {\r\n  return x;\r\n}\r\n\r\nexport default function bezier(mX1, mY1, mX2, mY2) {\r\n  if (!(0 <= mX1 && mX1 <= 1 && 0 <= mX2 && mX2 <= 1)) {\r\n    throw new Error('bezier x values must be in [0, 1] range');\r\n  }\r\n\r\n  if (mX1 === mY1 && mX2 === mY2) {\r\n    return LinearEasing;\r\n  }\r\n\r\n  // Precompute samples table\r\n  let sampleValues = float32ArraySupported ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);\r\n  for (let i = 0; i < kSplineTableSize; ++i) {\r\n    sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);\r\n  }\r\n\r\n  function getTForX(aX) {\r\n    let intervalStart = 0.0;\r\n    let currentSample = 1;\r\n    let lastSample = kSplineTableSize - 1;\r\n\r\n    for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) {\r\n      intervalStart += kSampleStepSize;\r\n    }\r\n    --currentSample;\r\n\r\n    // Interpolate to provide an initial guess for t\r\n    let dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]);\r\n    let guessForT = intervalStart + dist * kSampleStepSize;\r\n\r\n    let initialSlope = getSlope(guessForT, mX1, mX2);\r\n    if (initialSlope >= NEWTON_MIN_SLOPE) {\r\n      return newtonRaphsonIterate(aX, guessForT, mX1, mX2);\r\n    } else if (initialSlope === 0.0) {\r\n      return guessForT;\r\n    } else {\r\n      return binarySubdivide(aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2);\r\n    }\r\n  }\r\n\r\n  return function BezierEasing(x) {\r\n    // Because JavaScript number are imprecise, we should guarantee the extremes are right.\r\n    if (x === 0) {\r\n      return 0;\r\n    }\r\n    if (x === 1) {\r\n      return 1;\r\n    }\r\n    return calcBezier(getTForX(x), mY1, mY2);\r\n  };\r\n};\r\n"]}