text: /**
   * 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;
  }
  
  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 || x === 1) {
              return x;
          }
          return calcBezier(getTForX(x), mY1, mY2);
      };
  }
  