// Get the text that the user has selected
const getSelectedText = () => window.getSelection().toString();
getSelectedText();

const quickSort = arr => {
  const a = [...arr];
  if (a.length < 2) return a;
  const pivotIndex = Math.floor(arr.length / 2);
  const pivot = a[pivotIndex];
  const [lo, hi] = a.reduce(
    (acc, val, i) => {
      if (val < pivot || (val === pivot && i != pivotIndex)) {
        acc[0].push(val);
      } else if (val > pivot) {
        acc[1].push(val);
      }
      return acc;
    },
    [[], []]
  );
  return [...quickSort(lo), pivot, ...quickSort(hi)];
};

console.log(quickSort([1, 6, 1, 5, 3, 2, 1, 4])) // [1, 1, 1, 2, 3, 4, 5, 6]

const jwt = require("jsonwebtoken");

const authToken = (req, res, next) => {
	const token = req.headers["x-auth-token"];

	try {
	req.user = jwt.verify(token, process.env.ACCESS_TOKEN_SECRET);

	next();
	} catch (err) {
	console.log(err.message);
	res.status(401).json({ msg: "Error authenticating token" });
	}
};

module.exports = authToken;

//Disclaimer: I do not condone mass web scraping of ESPN websites, and this is just theoretical code that hasn't been used.

//Scrape player stats off of https://www.espn.com/nfl/team/roster/_/name/phi/philadelphia-eagles

//Example player JS object
/*
{
	"shortName": "S. Opeta",
	"name": "Sua Opeta",
	"href": "http://www.espn.com/nfl/player/_/id/3121009/sua-opeta",
	"uid": "s:20~l:28~a:3121009",
	"guid": "dec19157-e984-f981-3724-498281328c97",
	"id": "3121009",
	"height": "6' 4\"",
	"weight": "305 lbs",
	"age": 27,
	"position": "G",
	"jersey": "78",
	"birthDate": "08/15/96",
	"headshot": "https://a.espncdn.com/i/headshots/nfl/players/full/3121009.png",
	"lastName": "Sua Opeta",
	"experience": 4,
	"college": "Weber State"
}
*/

//The page needs to be focused, so you have to put this code in a bookmarklet and click the page before clicking it. Allegedly.
navigator.clipboard.writeText(JSON.stringify(
    window["__espnfitt__"].page.content.roster.groups.flatMap((group)=>{
        return group.athletes.map((athlete)=>{
            //We can assume all football players are above 100 lbs and below 1000 lbs.
            athlete.weight = parseInt(athlete.weight.substring(0,3));
            
            if(athlete.experience == "R") athlete.experience = 0;
            else athlete.experience = parseInt(athlete.experience);

            //We can assume players are at least 1 foot, or under 10 feet tall.
            athlete.inches = 12 * parseInt(athlete.height.substring(0, 1)); //Add feet in inches
            athlete.inches += parseInt(athlete.height.substring(2).replaceAll("\"", "").trim()); //Add remaining inches

            const monthDayYear = athlete.birthDate.split("/");
            athlete.birthMonth = parseInt(monthDayYear[0]);
            athlete.birthDay = parseInt(monthDayYear[1]);
            athlete.birthYear = parseInt(monthDayYear[2]);
            
            //The only really useful stuff we get from this is Height, weight, left handedness, age, position, and birthday
            return athlete;
        });
    })
, null, "\t")).then(null, ()=>{alert("That failed.")});

//Changes all the team links on this page https://www.espn.com/nfl/stats/team to the team's roster for easier scraping
$$("table > tbody > tr > td > div > div > a").forEach((elm)=>{
	elm.setAttribute("href", elm.getAttribute("href").replace("team/", "team/roster/"));
});

var arr = [{
    "success": true,
    "data": [
        {
            "id": "ipi_1KrrbvDOiB2klwsKhuqUWqt1",
            "object": "issuing.transaction",
            "amount": -2743,
            "amount_details": {
                "atm_fee": null
            },
            "authorization": "iauth_1KrrbuDOiB2klwsKoFjQZhhd",
            "balance_transaction": "txn_1KrrbwDOiB2klwsK1YkjJJRi",
            "card": "ic_1Krqe5DOiB2klwsK44a35eiE",
            "cardholder": "ich_1KrqL8DOiB2klwsKtBnZhzYr",
            "created": 1650753567,
            "currency": "usd",
            "dispute": null,
            "livemode": false,
            "merchant_amount": -2743,
            "merchant_currency": "usd",
            "merchant_data": {
                "category": "advertising_services",
                "category_code": "7311",
                "city": "San Francisco",
                "country": "US",
                "name": "Aeros Marketing, LLC",
                "network_id": "1234567890",
                "postal_code": "94103",
                "state": "CA"
            },
            "metadata": {},
            "type": "capture",
            "wallet": null
        },
                {
            "id": "ipi_1Krrbvc62B2klwsKhuqUWqt1",
            "object": "issuing.transaction",
            "amount": -9999,
            "amount_details": {
                "atm_fee": null
            },
            "authorization": "iauth_1KrrbuDOiB2klwsKoFjQZhhd",
            "balance_transaction": "txn_1KrrbwDOiB2klwsK1YkjJJRi",
            "card": "ic_1Krqe5DOiB2klwsK44a35eiE",
            "cardholder": "ich_1KrqL8DOiB2klwsKtBnZhzYr",
            "created": 1650753567,
            "currency": "USD",
            "dispute": null,
            "livemode": false,
            "merchant_amount": -9999,
            "merchant_currency": "usd",
            "merchant_data": {
                "category": "fast_food",
                "category_code": "7311",
                "city": "San Francisco",
                "country": "US",
                "name": "Aeros Marketing, LLC",
                "network_id": "1234567890",
                "postal_code": "94103",
                "state": "CA"
            },
            "metadata": {},
            "type": "capture",
            "wallet": null
        }
    ]
}];
    

const reduced = arr.reduce((prev, curr) => {
    prev.push({
        amount: curr.data[0].merchant_amount,
        id: curr.data[0].id,
        currency: curr.data[0].currency,
        category: curr.data[0].merchant_data.category
    });
    console.log(prev)

    return prev;
    
}, []);

const kNearestNeighbors = (data, labels, point, k = 3) => {
  const kNearest = data
    .map((el, i) => ({
      dist: Math.hypot(...Object.keys(el).map(key => point[key] - el[key])),
      label: labels[i]
    }))
    .sort((a, b) => a.dist - b.dist)
    .slice(0, k);

  return kNearest.reduce(
    (acc, { label }, i) => {
      acc.classCounts[label] =
        Object.keys(acc.classCounts).indexOf(label) !== -1
          ? acc.classCounts[label] + 1
          : 1;
      if (acc.classCounts[label] > acc.topClassCount) {
        acc.topClassCount = acc.classCounts[label];
        acc.topClass = label;
      }
      return acc;
    },
    {
      classCounts: {},
      topClass: kNearest[0].label,
      topClassCount: 0
    }
  ).topClass;
};
const data = [[0, 0], [0, 1], [1, 3], [2, 0]];
const labels = [0, 1, 1, 0];

kNearestNeighbors(data, labels, [1, 2], 2); // 1
kNearestNeighbors(data, labels, [1, 0], 2); // 0