/**
 * @license
 * Copyright 2017 Google LLC
 * SPDX-License-Identifier: Apache-2.0
 */

// The exact double values for the max and min scores possible in each range.
const MIN_PASSING_SCORE = 0.90000000000000002220446049250313080847263336181640625;
const MAX_AVERAGE_SCORE = 0.899999999999999911182158029987476766109466552734375;
const MIN_AVERAGE_SCORE = 0.5;
const MAX_FAILING_SCORE = 0.499999999999999944488848768742172978818416595458984375;

/**
 * Approximates the Gauss error function, the probability that a random variable
 * from the standard normal distribution lies within [-x, x]. Moved from
 * traceviewer.b.math.erf, based on Abramowitz and Stegun, formula 7.1.26.
 * @param {number} x
 * @return {number}
 */
function erf(x) {
  // erf(-x) = -erf(x);
  const sign = Math.sign(x);
  x = Math.abs(x);

  const a1 = 0.254829592;
  const a2 = -0.284496736;
  const a3 = 1.421413741;
  const a4 = -1.453152027;
  const a5 = 1.061405429;
  const p = 0.3275911;
  const t = 1 / (1 + p * x);
  const y = t * (a1 + t * (a2 + t * (a3 + t * (a4 + t * a5))));
  return sign * (1 - y * Math.exp(-x * x));
}

/**
 * Returns the score (1 - percentile) of `value` in a log-normal distribution
 * specified by the `median` value, at which the score will be 0.5, and a 10th
 * percentile value, at which the score will be 0.9. The score represents the
 * amount of the distribution greater than `value`. All values should be in the
 * same units (e.g. milliseconds). See
 *   https://www.desmos.com/calculator/o98tbeyt1t
 * for an interactive view of the relationship between these parameters and the
 * typical parameterization (location and shape) of the log-normal distribution.
 * @param {{median: number, p10: number}} parameters
 * @param {number} value
 * @return {number}
 */
function getLogNormalScore({median, p10}, value) {
  // Required for the log-normal distribution.
  if (median <= 0) throw new Error('median must be greater than zero');
  if (p10 <= 0) throw new Error('p10 must be greater than zero');
  // Not strictly required, but if p10 > median, it flips around and becomes the p90 point.
  if (p10 >= median) throw new Error('p10 must be less than the median');

  // Non-positive values aren't in the distribution, so always 1.
  if (value <= 0) return 1;

  // Closest double to `erfc-1(1/5)`.
  const INVERSE_ERFC_ONE_FIFTH = 0.9061938024368232;

  // Shape (σ) is `|log(p10/median) / (sqrt(2)*erfc^-1(1/5))|` and
  // standardizedX is `1/2 erfc(log(value/median) / (sqrt(2)*σ))`, so simplify a bit.
  const xRatio = Math.max(Number.MIN_VALUE, value / median); // value and median are > 0, so is ratio.
  const xLogRatio = Math.log(xRatio);
  const p10Ratio = Math.max(Number.MIN_VALUE, p10 / median); // p10 and median are > 0, so is ratio.
  const p10LogRatio = -Math.log(p10Ratio); // negate to keep σ positive.
  const standardizedX = xLogRatio * INVERSE_ERFC_ONE_FIFTH / p10LogRatio;
  const complementaryPercentile = (1 - erf(standardizedX)) / 2;

  // Clamp to avoid floating-point out-of-bounds issues and keep score in expected range.
  let score;
  if (value <= p10) {
    // Passing. Clamp to [0.9, 1].
    score = Math.max(MIN_PASSING_SCORE, Math.min(1, complementaryPercentile));
  } else if (value <= median) {
    // Average. Clamp to [0.5, 0.9).
    score = Math.max(MIN_AVERAGE_SCORE, Math.min(MAX_AVERAGE_SCORE, complementaryPercentile));
  } else {
    // Failing. Clamp to [0, 0.5).
    score = Math.max(0, Math.min(MAX_FAILING_SCORE, complementaryPercentile));
  }
  return score;
}

/**
 * Interpolates the y value at a point x on the line defined by (x0, y0) and (x1, y1)
 * @param {number} x0
 * @param {number} y0
 * @param {number} x1
 * @param {number} y1
 * @param {number} x
 * @return {number}
 */
function linearInterpolation(x0, y0, x1, y1, x) {
  const slope = (y1 - y0) / (x1 - x0);
  return y0 + (x - x0) * slope;
}

export {
  linearInterpolation,
  getLogNormalScore,
};