{"version":3,"names":["NEWTON_ITERATIONS","NEWTON_MIN_SLOPE","SUBDIVISION_PRECISION","SUBDIVISION_MAX_ITERATIONS","kSplineTableSize","kSampleStepSize","A","aA1","aA2","B","C","calcBezier","aT","getSlope","binarySubdivide","aX","aA","aB","mX1","mX2","currentX","currentT","i","Math","abs","newtonRaphsonIterate","aGuessT","currentSlope","Bezier","mY1","mY2","LinearEasing","x","Error","sampleValues","Array","getTForX","intervalStart","currentSample","lastSample","dist","guessForT","initialSlope","BezierEasing"],"sources":["Bezier.ts"],"sourcesContent":["'use strict';\n/**\n * https://github.com/gre/bezier-easing\n * BezierEasing - use bezier curve for transition easing function\n * by Gaëtan Renaudeau 2014 - 2015 – MIT License\n */\n\n// These values are established by empiricism with tests (tradeoff: performance VS precision)\n\nconst NEWTON_ITERATIONS = 4;\nconst NEWTON_MIN_SLOPE = 0.001;\nconst SUBDIVISION_PRECISION = 0.0000001;\nconst SUBDIVISION_MAX_ITERATIONS = 10;\n\nconst kSplineTableSize = 11;\nconst kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);\n\nfunction A(aA1: number, aA2: number): number {\n  'worklet';\n  return 1.0 - 3.0 * aA2 + 3.0 * aA1;\n}\nfunction B(aA1: number, aA2: number): number {\n  'worklet';\n  return 3.0 * aA2 - 6.0 * aA1;\n}\nfunction C(aA1: number) {\n  'worklet';\n  return 3.0 * aA1;\n}\n\n// Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2.\nfunction calcBezier(aT: number, aA1: number, aA2: number): number {\n  'worklet';\n  return ((A(aA1, aA2) * aT + B(aA1, aA2)) * aT + C(aA1)) * aT;\n}\n\n// Returns dx/dt given t, x1, and x2, or dy/dt given t, y1, and y2.\nfunction getSlope(aT: number, aA1: number, aA2: number): number {\n  'worklet';\n  return 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1);\n}\n\nfunction binarySubdivide(\n  aX: number,\n  aA: number,\n  aB: number,\n  mX1: number,\n  mX2: number\n): number {\n  'worklet';\n  let currentX;\n  let currentT;\n  let i = 0;\n  do {\n    currentT = aA + (aB - aA) / 2.0;\n    currentX = calcBezier(currentT, mX1, mX2) - aX;\n    if (currentX > 0.0) {\n      aB = currentT;\n    } else {\n      aA = currentT;\n    }\n  } while (\n    Math.abs(currentX) > SUBDIVISION_PRECISION &&\n    ++i < SUBDIVISION_MAX_ITERATIONS\n  );\n  return currentT;\n}\n\nfunction newtonRaphsonIterate(\n  aX: number,\n  aGuessT: number,\n  mX1: number,\n  mX2: number\n): number {\n  'worklet';\n  for (let i = 0; i < NEWTON_ITERATIONS; ++i) {\n    const currentSlope = getSlope(aGuessT, mX1, mX2);\n    if (currentSlope === 0.0) {\n      return aGuessT;\n    }\n    const currentX = calcBezier(aGuessT, mX1, mX2) - aX;\n    aGuessT -= currentX / currentSlope;\n  }\n  return aGuessT;\n}\n\nexport function Bezier(\n  mX1: number,\n  mY1: number,\n  mX2: number,\n  mY2: number\n): (x: number) => number {\n  'worklet';\n\n  function LinearEasing(x: number): number {\n    'worklet';\n    return x;\n  }\n\n  if (!(mX1 >= 0 && mX1 <= 1 && mX2 >= 0 && mX2 <= 1)) {\n    throw new Error('[Reanimated] Bezier x values must be in [0, 1] range.');\n  }\n\n  if (mX1 === mY1 && mX2 === mY2) {\n    return LinearEasing;\n  }\n\n  // FIXME: Float32Array is not available in Hermes right now\n  //\n  // var float32ArraySupported = typeof Float32Array === 'function';\n  // const sampleValues = float32ArraySupported\n  // ? new Float32Array(kSplineTableSize)\n  // : new Array(kSplineTableSize);\n\n  // Precompute samples table\n  const sampleValues = new Array(kSplineTableSize);\n\n  for (let i = 0; i < kSplineTableSize; ++i) {\n    sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);\n  }\n\n  function getTForX(aX: number): number {\n    'worklet';\n    let intervalStart = 0.0;\n    let currentSample = 1;\n    const lastSample = kSplineTableSize - 1;\n\n    for (\n      ;\n      currentSample !== lastSample && sampleValues[currentSample] <= aX;\n      ++currentSample\n    ) {\n      intervalStart += kSampleStepSize;\n    }\n    --currentSample;\n\n    // Interpolate to provide an initial guess for t\n    const dist =\n      (aX - sampleValues[currentSample]) /\n      (sampleValues[currentSample + 1] - sampleValues[currentSample]);\n    const guessForT = intervalStart + dist * kSampleStepSize;\n\n    const initialSlope = getSlope(guessForT, mX1, mX2);\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(\n        aX,\n        intervalStart,\n        intervalStart + kSampleStepSize,\n        mX1,\n        mX2\n      );\n    }\n  }\n\n  return function BezierEasing(x) {\n    'worklet';\n    if (mX1 === mY1 && mX2 === mY2) {\n      return x; 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