import * as maps from './maps/maps';
import * as metadata from '../data/2d73896c/metadata.json';

const POPULAR_THRESHOLD = 1.0; // percent
const PICKS_THRESHOLD = 200; // picks

const report = require('../data/2d73896c/report.json')
  .filter((entry) => entry.Actor != undefined); // bad data from API downtime

const reports = new Map();
const totalPicks = new Map();
const top10Relevancy = new Map();
const topPicks = new Map();
const topWins = new Map();
const topRareWins = new Map();
const topEpicWins = new Map();
const topLegendaryWins = new Map();
const topLowLevel = new Map();
const topScaling = new Map();
const topUnpopularWins = new Map();
let totalMatches = 0;
let actors = [];
const modes = metadata.config.api.modes;

for(let mode of modes) {
  const relevancy = (entry) => (entry.TotalPicks / totalPicks.get(mode)) * entry.TotalWinner;
  const playersPerMatch = maps.playersPerMatch(mode);

  const reportFilterMode = report.filter((entry) => entry.Mode == mode);

  const baseLevelForEntry = (entry) => reportFilterMode
    .filter((entry2) => entry2.Actor == entry.Actor &&
      entry2.Talent == entry.Talent)
    .map((entry2) => entry2.LevelBucket)
    .reduce((agg, cur) => cur < agg? cur : agg);

  const reportFilterModeBaseLevel = reportFilterMode.filter((entry) => entry.LevelBucket == baseLevelForEntry(entry));

  // using the "base level" (bucket 0) report, add information about diff to 'NoTalent' and scaling
  const reportFilterModeRegressed = reportFilterModeBaseLevel.map((entry) => {
    const sum = (agg, cur) => agg + cur;
    const range = (n) => [...Array(n).keys()];

    // perform a weighted linear regression. x: level bucket (scaled 0 - 1) y: win rate weights: picks
    const actorReport = reportFilterMode
      .filter((entry2) =>
        entry2.Actor == entry.Actor &&
        entry2.Talent == entry.Talent &&
        entry2.LevelBucket >= 0);
    const buckets = actorReport.map((entry) => entry.LevelBucket).sort();
    const findActorWithBucket = (bucket) => actorReport.find((entry) => entry.LevelBucket == bucket);
    const weights = buckets.map((bucket) => findActorWithBucket(bucket).Count);
    const sum_weights = weights.reduce(sum, 0);
    const xs = [].concat(...buckets.map((bucket, index) => range(weights[index]).map((_) => bucket / metadata.config.self.levelBuckets)));
    const ys = [].concat(...buckets.map((bucket, index) => range(weights[index]).map((_) => findActorWithBucket(bucket).Winner)));
    const n = xs.length;
    const sum_x = xs.reduce(sum, 0);
    const sum_y = ys.reduce(sum, 0);
    const sum_xy = xs.map((x, index) => x * ys[index]).reduce(sum, 0);
    const sum_xx = xs.map((x) => x * x).reduce(sum, 0);
    const sum_yy = ys.map((y) => y * y).reduce(sum, 0);

    const slope = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x * sum_x);
    const intercept = (sum_y - slope * sum_x) / n;

    return Object.assign({}, entry, {
      TalentWinrateBase: intercept,
      TalentWinrateScaling: slope, // to 1 = max level
      TalentWinrateLevelScaling: slope / maps.getMaxLevel(entry),
      TotalPicks: sum_weights || entry.Count, // NoTalent has no weights
      TotalWinner: sum_y / sum_weights || entry.Winner,
      SampleTooSmall: sum_weights < PICKS_THRESHOLD,
    });
  });
  reports.set(mode, reportFilterModeRegressed);

  totalPicks.set(mode, reports.get(mode)
    .map((entry) => entry.TotalPicks)
    .reduce((agg, cur) => agg + cur, 0));
  totalMatches += totalPicks.get(mode) / maps.playersPerMatch(mode);
  top10Relevancy.set(mode, reports.get(mode)
    .sort((entry1, entry2) => relevancy(entry2) - relevancy(entry1))
    .slice(0, 10));
  topPicks.set(mode, reports.get(mode).sort((entry1, entry2) => entry2.TotalPicks - entry1.TotalPicks)[0]);
  topWins.set(mode, reports.get(mode)
    .filter((entry) => 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode) > POPULAR_THRESHOLD)
    .sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
  topUnpopularWins.set(mode, reports.get(mode)
    .filter((entry) => 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode) <= POPULAR_THRESHOLD &&
      entry.TotalPicks > PICKS_THRESHOLD)
    .sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
  topRareWins.set(mode, reports.get(mode)
    .filter((entry) => maps.getTalentRarity(entry.Talent) == 'Rare' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
    .sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
  topEpicWins.set(mode, reports.get(mode)
    .filter((entry) => maps.getTalentRarity(entry.Talent) == 'Epic' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
    .sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
  topLegendaryWins.set(mode, reports.get(mode)
    .filter((entry) => maps.getTalentRarity(entry.Talent) == 'Legendary' && 100 * playersPerMatch * entry.TotalPicks / totalPicks.get(mode))
    .sort((entry1, entry2) => entry2.TotalWinner - entry1.TotalWinner)[0]);
  topLowLevel.set(mode, reports.get(mode)
    .filter((entry) => !entry.SampleTooSmall)
    .sort((entry1, entry2) => entry2.TalentWinrateBase - entry1.TalentWinrateBase)[0]);
  topScaling.set(mode, reports.get(mode)
    .filter((entry) => !entry.SampleTooSmall)
    .sort((entry1, entry2) => entry2.TalentWinrateLevelScaling - entry1.TalentWinrateLevelScaling)[0]);

  if (actors.length == 0) {
    actors = [...new Set(reports.get(mode).map((entry) => entry.Actor))];
  }
}

export default {
  getReport(mode) {
    return reports.get(mode);
  },

  getTotalPicks(mode) {
    return totalPicks.get(mode);
  },

  getTopPick(mode) {
    return topPicks.get(mode);
  },

  getTop10Picks(mode) {
    return top10Relevancy.get(mode);
  },

  getTopLowLevel(mode) {
    return topLowLevel.get(mode);
  },

  getTopScaling(mode) {
    return topScaling.get(mode);
  },

  getTopWin(mode) {
    return topWins.get(mode);
  },

  getTopUnpopularWin(mode) {
    return topUnpopularWins.get(mode);
  },

  getTopRareWins(mode) {
    return topRareWins.get(mode);
  },

  getTopEpicWins(mode) {
    return topEpicWins.get(mode);
  },

  getTopLegendaryWins(mode) {
    return topLegendaryWins.get(mode);
  },

  getBestUnpopular(mode) {
    return topUnpopular.get(mode);
  },

  getActors() {
    return actors;
  },

  getModes() {
    return modes;
  },

  getTotalMatches() {
    return totalMatches;
  },

  getLastUpdate() {
    return metadata.lastUpdate;
  },

  getLevelBuckets() {
    return metadata.config.self.levelBuckets;
  },
};