/**
 * https://github.com/gre/bezier-easing
 * BezierEasing - use bezier curve for transition easing function
 * by Gaëtan Renaudeau 2014 - 2015 – MIT License
 */

import type { EaseFn } from '../../types.js';

// These values are established by empiricism with tests (tradeoff: performance VS precision)
const NEWTON_ITERATIONS = 4,
  NEWTON_MIN_SLOPE = 0.001,
  SUBDIVISION_PRECISION = 0.0000001,
  SUBDIVISION_MAX_ITERATIONS = 10,
  kSplineTableSize = 11,
  kSampleStepSize = 1.0 / (kSplineTableSize - 1.0);

const A = (aA1: number, aA2: number) => 1.0 - 3.0 * aA2 + 3.0 * aA1;
const B = (aA1: number, aA2: number) => 3.0 * aA2 - 6.0 * aA1;
const C = (aA1: number) => 3.0 * aA1;

// Returns x(t) given t, x1, and x2, or y(t) given t, y1, and y2.
const calcBezier = (aT: number, aA1: number, aA2: number) => ((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.
const getSlope = (aT: number, aA1: number, aA2: number) => 3.0 * A(aA1, aA2) * aT * aT + 2.0 * B(aA1, aA2) * aT + C(aA1);

function binarySubdivide(aX: number, aA: number, aB: number, mX1: number, mX2: number) {
  let currentX: number,
    currentT: number,
    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: number, aGuessT: number, mX1: number, mX2: number) {
  for (let i = 0; i < NEWTON_ITERATIONS; ++i) {
    const currentSlope = getSlope(aGuessT, mX1, mX2);

    if (currentSlope === 0.0) return aGuessT;

    const currentX = calcBezier(aGuessT, mX1, mX2) - aX;
    aGuessT -= currentX / currentSlope;
  }
  return aGuessT;
}

const LinearEasing = (x: number) => x;

export default function cubicBezier(mX1: number, mY1: number, mX2: number, mY2: number): EaseFn {
  if (!(0 <= mX1 && mX1 <= 1 && 0 <= mX2 && mX2 <= 1))
    throw new Error('/n/n⛔ [animare] ➡️ [ease] ➡️ [cubicBezier] : bezier x values must be in [0, 1] range. !!\n\n');

  if (mX1 === mY1 && mX2 === mY2) return LinearEasing;

  // Precompute samples table
  const sampleValues = typeof Float32Array === 'function' ? new Float32Array(kSplineTableSize) : new Array(kSplineTableSize);

  for (let i = 0; i < kSplineTableSize; ++i) sampleValues[i] = calcBezier(i * kSampleStepSize, mX1, mX2);

  function getTForX(aX: number) {
    let intervalStart = 0.0,
      currentSample = 1;

    const lastSample = kSplineTableSize - 1;

    for (; currentSample !== lastSample && sampleValues[currentSample] <= aX; ++currentSample) intervalStart += kSampleStepSize;

    --currentSample;

    // Interpolate to provide an initial guess for t
    const dist = (aX - sampleValues[currentSample]) / (sampleValues[currentSample + 1] - sampleValues[currentSample]),
      guessForT = intervalStart + dist * kSampleStepSize,
      initialSlope = getSlope(guessForT, mX1, mX2);

    if (initialSlope >= NEWTON_MIN_SLOPE) return newtonRaphsonIterate(aX, guessForT, mX1, mX2);

    if (initialSlope === 0.0) return guessForT;

    return binarySubdivide(aX, intervalStart, intervalStart + kSampleStepSize, mX1, mX2);
  }

  return (t: number) => {
    // Because JavaScript number are imprecise, we should guarantee the extremes are right.
    if (t === 0 || t === 1) return t;

    return calcBezier(getTForX(t), mY1, mY2);
  };
}