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