Remove the entire tier system and anything that has to do with tiers while maintaining functionality for everything else.

import asyncio
import aiohttp
from aiolimiter import AsyncLimiter
import time
from bisect import bisect_left
from tqdm.asyncio import tqdm
import backoff
import logging
import numpy as np
from scipy import stats
from scipy.optimize import brentq, minimize_scalar
from google.oauth2 import service_account
from googleapiclient.discovery import build
import discord
from discord import app_commands
from discord.ext import commands
import gspread
from oauth2client.service_account import ServiceAccountCredentials

# Import all configuration variables
from config import *

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
logger = logging.getLogger(__name__)

# --- Discord Bot Setup ---
intents = discord.Intents.default()
intents.message_content = True
bot = commands.Bot(command_prefix='!', intents=intents)

# --- Google Sheets Setup ---
scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name(SERVICE_ACCOUNT_FILE, scope)
client = gspread.authorize(creds)
sheet = client.open('Global Rankings - Polytrack').worksheet('0.4')

# --- Cooldown Implementation for Application Commands ---
_command_cooldowns = {}


# --- Helper Functions ---

def calculate_rank_distribution(n_ranks, std_dev, skew_factor):
    x = np.linspace(0, 1, n_ranks + 1)
    percentiles = 1 - (1 - x) ** skew_factor
    z_scores = np.clip(stats.norm.ppf(percentiles), -8, 8)
    probs = np.diff(stats.norm.cdf(z_scores))
    return probs / np.sum(probs)


def skew_normal_pdf(x, alpha):
    return 2 / np.sqrt(2 * np.pi) * np.exp(-x ** 2 / 2) * stats.norm.cdf(alpha * x)


def find_cutoff(cdf_value, alpha, std_dev):
    return brentq(lambda x: stats.norm.cdf(x) - stats.norm.cdf(0) + cdf_value - stats.norm.cdf(alpha * x),
                  -10 * std_dev, 10 * std_dev)


def calculate_percentile_cutoffs(n_ranks, skew_factor):
    cdf_values = np.cumsum(calculate_rank_distribution(n_ranks, 1, skew_factor))
    return [find_cutoff(cdf_value, skew_factor, 1) for cdf_value in cdf_values]


def get_tier(rank, total_players):
    percentile = rank / total_players
    tier_cutoffs = calculate_percentile_cutoffs(N_TIERS, SKEW_FACTOR)
    return np.digitize([percentile], tier_cutoffs, right=True)[0] + 1


def format_average_rank(rank):
    if rank < 10:
        return f"{rank:.2f}"
    elif rank < 100:
        return f"{rank:.1f}"
    else:
        return f"{rank:.0f}"


def get_tier_distribution(n_tiers, highest_tier_percentage, skew_factor):
    def target_function(sigma):
        dist = calculate_rank_distribution(n_tiers, sigma, skew_factor)
        return abs(dist[0] - highest_tier_percentage)

    result = minimize_scalar(target_function, bounds=(0.01, 100), method='bounded')
    sigma_optimized = result.x

    return calculate_rank_distribution(n_tiers, sigma_optimized, skew_factor)


tier_distribution = get_tier_distribution(N_TIERS, HIGHEST_TIER_PERCENTAGE, SKEW_FACTOR)


def get_tier(percentile):
    cumulative_percentage = 0
    for i, threshold in enumerate(tier_distribution):
        cumulative_percentage += threshold
        if percentile <= cumulative_percentage * 100:
            return i + 1
    return N_TIERS


def get_tier_name(tier_number):
    """Returns the name of the tier given its number."""
    return TIER_NAMES[tier_number - 1]


# Create a limiter that allows 10 requests per second
limiter = AsyncLimiter(20, 1)  # 10 requests per 1 second

@backoff.on_exception(backoff.expo,
                      (aiohttp.ClientError, asyncio.TimeoutError),
                      max_tries=RETRIES,
                      jitter=backoff.full_jitter,
                      max_time=60)  # Set a maximum backoff time of 60 seconds
async def fetch_data(session, url_template, skip, track_index):
    async with limiter:  # Use the limiter here
        url = url_template.format(skip=skip)
        try:
            async with session.get(url, timeout=30) as response:
                if response.status == 200:
                    data = await response.json()
                    return data.get("entries", [])
                elif response.status == 503:
                    logger.warning(f"Server overloaded (503). Retrying URL: {url}")
                    raise aiohttp.ClientError
                else:
                    logger.error(f"Error fetching data from {url}. Status code: {response.status}. Retrying...")
                    raise aiohttp.ClientError
        except (aiohttp.ClientError, asyncio.TimeoutError) as e:
            logger.error(f"Exception for URL {url}: {e}")
            raise

async def process_track(session, url_template, track_index, pbar):
    skip = 0
    max_entries = MAX_SKIP

    while skip < max_entries:
        try:
            entries = await fetch_data(session, url_template, skip, track_index)
            if entries:
                for entry in entries:
                    player_name = entry["name"]
                    frames = entry["frames"]
                    player_times.setdefault(player_name, [None] * len(TRACK_URLS))
                    if player_times[player_name][track_index] is None or frames < player_times[player_name][track_index]:
                        player_times[player_name][track_index] = frames
                sorted_track_times[url_template] = sorted([t for t in player_times.values() if t[track_index] is not None],
                                                          key=lambda x: x[track_index])
                skip += len(entries)
            else:
                logger.info(f"No more entries found for track {track_index} at skip {skip}.")
                break  # Exit only if no more entries are found
        except aiohttp.ClientError as e:
            logger.error(f"Failed to fetch data for {url_template.format(skip=skip)} after retries. Error: {e}.")
            # DO NOT BREAK HERE. Let backoff retry.
        finally:  # Ensure pbar update even on error
            pbar.update(len(entries) if entries else 0) # Update with 0 if entries is None due to an error



def clear_google_sheet_columns():
    SCOPES = ['https://www.googleapis.com/auth/spreadsheets']

    credentials = service_account.Credentials.from_service_account_file(
        SERVICE_ACCOUNT_FILE, scopes=SCOPES)

    service = build('sheets', 'v4', credentials=credentials)

    RANGE_NAME = '0.4!B1:Z'

    body = {}

    result = service.spreadsheets().values().clear(
        spreadsheetId=SPREADSHEET_ID, range=RANGE_NAME, body=body).execute()

    print(f"{result.get('clearedRange')} range cleared.")

    result = service.spreadsheets().values().update(
        spreadsheetId=SPREADSHEET_ID, range=RANGE_NAME,
        valueInputOption='USER_ENTERED', body=body).execute()

    print(f"{result.get('updatedCells')} cells updated.")


def update_google_sheet(data):
    SCOPES = ['https://www.googleapis.com/auth/spreadsheets']

    credentials = service_account.Credentials.from_service_account_file(
        SERVICE_ACCOUNT_FILE, scopes=SCOPES)

    service = build('sheets', 'v4', credentials=credentials)

    RANGE_NAME = '0.4!B1'

    clear_google_sheet_columns()

    body = {
        'values': data
    }

    result = service.spreadsheets().values().update(
        spreadsheetId=SPREADSHEET_ID, range=RANGE_NAME,
        valueInputOption='USER_ENTERED', body=body).execute()

    print(f"{result.get('updatedCells')} cells updated.")


# --- Discord Bot Commands ---

@bot.event
async def on_ready():
    print(f'{bot.user} has connected to Discord!')
    await bot.tree.sync()


async def check_cooldown(interaction: discord.Interaction, command_name: str) -> bool:
    current_time = time.time()
    if command_name == "rankings":  # bypass cooldown for "rankings" command
        return True
    cooldown_duration = RELEASE_COOLDOWN_TIME if command_name == "updaterelease" else COOLDOWN_TIME

    if command_name in _command_cooldowns and current_time < _command_cooldowns[command_name]:
        remaining_time = _command_cooldowns[command_name] - current_time
        await interaction.response.send_message(
            f"This command is on cooldown. Try again in {remaining_time:.2f} seconds.",
            ephemeral=True,
        )
        return False
    else:
        _command_cooldowns[command_name] = current_time + cooldown_duration
        return True


@bot.tree.command(name="player", description="Get stats for a player")
@app_commands.describe(player_name="The name of the player to look up")
async def player(interaction: discord.Interaction, player_name: str):
    if await check_cooldown(interaction, "player"):
        await interaction.response.defer()
        try:
            await update_rankings_function()
            cells = sheet.findall(player_name, in_column=4, case_sensitive=True)

            if cells:
                cell = cells[0]
                row_number = cell.row
                stats = sheet.row_values(row_number)

                headers = sheet.row_values(1)
                tier_image_url = stats[-1]

                # Get tier name directly from the tier_images dictionary
                for tier_number, image_url in TIER_IMAGES.items():
                    if image_url == tier_image_url:
                        tier_name = get_tier_name(tier_number)
                        break  # Exit loop once tier name is found

                embed = discord.Embed(title=f"Stats for {player_name}")

                # Add Tier before Rank
                embed.add_field(name="Tier", value=tier_name, inline=True)
                embed.add_field(name=headers[1], value=stats[1], inline=True)  # Rank

                # Add the rest of the fields, skipping "Tier" header
                for i, (header, stat) in enumerate(zip(headers[2:-1], stats[2:-1])):
                    if i != 1:  # Skip the "Rank" header and value
                        embed.add_field(name=header, value=stat, inline=True)

                embed.set_image(url=tier_image_url)

                await interaction.followup.send(embed=embed)
            else:
                await interaction.followup.send(
                    f"{player_name} not found. Please complete every track!")
        except Exception as e:
            await interaction.followup.send(f"An error occurred: {e}")


@bot.tree.command(name="rankings", description="Get the link to the global rankings")
async def rankings(interaction: discord.Interaction):
    if await check_cooldown(interaction, "rankings"):
        await interaction.response.send_message(
            f"<https://docs.google.com/spreadsheets/d/1szNRW_nla-XuGTwgIdGHdmuyd2HhTQ8NqWoqn6l9uGw/edit?usp=sharing>")


@bot.tree.command(name="wr", description="Shows the world record for each track and players with the most WRs.")
async def world_records(interaction: discord.Interaction):
    if await check_cooldown(interaction, "wr"):
        await interaction.response.defer()
        embed = discord.Embed(title="Polytrack World Records", color=discord.Color.blue())
        wr_holders = {}  # Dictionary to store players and their WR count

        async with aiohttp.ClientSession() as session:
            for i, url in enumerate(TRACK_URLS[:13]):
                try:
                    async with session.get(url.format(skip=0), timeout=30) as response:
                        if response.status == 200:
                            data = await response.json()
                            entries = data.get("entries", [])
                            if entries:
                                world_record_frames = entries[0]["frames"]
                                world_record_seconds = world_record_frames / 1000
                                world_record_holder = entries[0]["name"]
                                embed.add_field(name=TRACK_NAMES[i],
                                                value=f"**{world_record_seconds:.3f}s** by **{world_record_holder}**",
                                                inline=False)

                                # Update WR count for the player
                                if world_record_holder in wr_holders:
                                    wr_holders[world_record_holder] += 1
                                else:
                                    wr_holders[world_record_holder] = 1
                        else:
                            embed.add_field(name=TRACK_NAMES[i],
                                            value=f"Error fetching data. Status code: {response.status}",
                                            inline=False)
                except Exception as e:
                    embed.add_field(name=TRACK_NAMES[i],
                                    value=f"An error occurred: {e}",
                                    inline=False)

        # Sort players by WR count in descending order
        sorted_wr_holders = sorted(wr_holders.items(), key=lambda item: item[1], reverse=True)

        # Add WR holders and their count to the embed without the header
        embed.add_field(name="-" * 20, value="", inline=False)  # Add a separator line without space
        for player, count in sorted_wr_holders:
            embed.add_field(name=player, value=f"{count} WRs", inline=False)

        await interaction.followup.send(embed=embed)


import discord
from discord import app_commands
import numpy as np

# ... (Your other imports, TRACK_URLS, sorted_track_times, player_times, etc.)

@bot.tree.command(name="improvement", description="Get a player's improvement potential for each track.")
@app_commands.describe(player_name="The name of the player to analyze.")
async def improvement(interaction: discord.Interaction, player_name: str):
    if await check_cooldown(interaction, "improvement"):
        await interaction.response.defer()
        try:
            await update_rankings_function()

            player_data = player_times.get(player_name)
            if player_data is None:
                await interaction.followup.send(f"Player '{player_name}' not found in the rankings.")
                return

            table_data = []
            total_pb_seconds = 0
            total_wr_seconds = 0
            for i, track_url in enumerate(TRACK_URLS):
                pb_frames = player_data[i]
                if pb_frames is None:
                    table_data.append(
                        [f"T{i+1}", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A"])
                    continue

                wr_frames = sorted_track_times[track_url][0][i]
                pb_seconds = pb_frames / 1000
                wr_seconds = wr_frames / 1000

                total_pb_seconds += pb_seconds
                total_wr_seconds += wr_seconds

                # Calculate metrics with significant figures
                difference = pb_seconds - wr_seconds
                sig_figs = 3  # Example: using 3 significant figures

                # Function to format a number with significant figures
                def format_sigfigs(value, sig_figs):
                    if value == 0:
                        return "0"
                    return f"{float(f'{value:.{sig_figs}g}')}"

                difference_str = format_sigfigs(difference, sig_figs)
                inverse_performance_ratio_str = format_sigfigs(wr_seconds / pb_seconds if pb_seconds else float("inf"),
                                                               sig_figs)
                relative_difference_str = format_sigfigs(difference / wr_seconds if wr_seconds else float("inf"),
                                                         sig_figs)
                logarithmic_difference_str = format_sigfigs(
                    np.log(pb_seconds / wr_seconds) if wr_seconds and pb_seconds > 0 else float("inf"), sig_figs)
                harmonic_mean_str = format_sigfigs(
                    2 * (pb_seconds * wr_seconds) / (pb_seconds + wr_seconds) if pb_seconds + wr_seconds else float(
                        "inf"), sig_figs)
                normalized_difference_str = format_sigfigs(
                    difference / (pb_seconds + wr_seconds) if pb_seconds + wr_seconds else float("inf"), sig_figs)
                exponential_difference_str = format_sigfigs(
                    np.exp(difference / wr_seconds) if wr_seconds else float("inf"), sig_figs)
                inverse_relative_difference_str = format_sigfigs(
                    wr_seconds / difference if difference else float("inf"), sig_figs)
                quadratic_difference_str = format_sigfigs(
                    (difference / wr_seconds) ** 2 if wr_seconds else float("inf"), sig_figs)

                table_data.append([
                    f"T{i+1}",  # Abbreviated Track column
                    f"{pb_seconds:.3f}",
                    f"{wr_seconds:.3f}",
                    difference_str,
                    inverse_performance_ratio_str,
                    relative_difference_str,
                    logarithmic_difference_str,
                    harmonic_mean_str,
                    normalized_difference_str,
                    exponential_difference_str,
                    inverse_relative_difference_str,
                    quadratic_difference_str
                ])

            # Total Row Calculations (using the same significant figures)
            total_difference = total_pb_seconds - total_wr_seconds
            total_difference_str = format_sigfigs(total_pb_seconds - total_wr_seconds, sig_figs)
            total_inverse_performance_ratio_str = format_sigfigs(
                total_wr_seconds / total_pb_seconds if total_pb_seconds else float("inf"), sig_figs)
            total_relative_difference_str = format_sigfigs(
                total_difference / total_wr_seconds if total_wr_seconds else float("inf"), sig_figs)
            total_logarithmic_difference_str = format_sigfigs(
                np.log(total_pb_seconds / total_wr_seconds) if total_wr_seconds and total_pb_seconds > 0 else float(
                    "inf"), sig_figs)
            total_harmonic_mean_str = format_sigfigs(2 * (total_pb_seconds * total_wr_seconds) / (
                    total_pb_seconds + total_wr_seconds) if total_pb_seconds + total_wr_seconds else float("inf"),
                                                     sig_figs)
            total_normalized_difference_str = format_sigfigs(total_difference / (
                    total_pb_seconds + total_wr_seconds) if total_pb_seconds + total_wr_seconds else float("inf"),
                                                             sig_figs)
            total_exponential_difference_str = format_sigfigs(
                np.exp(total_difference / total_wr_seconds) if total_wr_seconds else float("inf"), sig_figs)
            total_inverse_relative_difference_str = format_sigfigs(
                total_wr_seconds / total_difference if total_difference else float("inf"), sig_figs)
            total_quadratic_difference_str = format_sigfigs(
                (total_difference / total_wr_seconds) ** 2 if total_wr_seconds else float("inf"), sig_figs)

            table_data.append([
                "All",
                f"{total_pb_seconds:.3f}",
                f"{total_wr_seconds:.3f}",
                total_difference_str,
                total_inverse_performance_ratio_str,
                total_relative_difference_str,
                total_logarithmic_difference_str,
                total_harmonic_mean_str,
                total_normalized_difference_str,
                total_exponential_difference_str,
                total_inverse_relative_difference_str,
                total_quadratic_difference_str
            ])

            # Compact column widths further
            table_str = "```\n"
            table_str += f"{'T':<3} {'PB':<7} {'WR':<7} {'Diff':<5} {'IPR':<5} {'RelD':<8} {'LogD':<8} {'Harm':<5} {'NormD':<8} {'ExpD':<6} {'InvRD':<7} {'QuadD':<6}\n"  # Shortened column names
            table_str += "-" * 94 + "\n"  # Adjusted separator length
            for row in table_data:
                table_str += f"{row[0]:<3} {row[1]:<7} {row[2]:<7} {row[3]:<5} {row[4]:<5} {row[5]:<8} {row[6]:<8} {row[7]:<5} {row[8]:<8} {row[9]:<6} {row[10]:<7} {row[11]:<6}\n"
            table_str += "```\n"

            await interaction.followup.send(f"Improvement potential for **{player_name}**:\n{table_str}")

        except Exception as e:
            await interaction.followup.send(f"An error occurred: {e}")


# --- Metric Explanation Commands ---

def create_metric_explanation_command(metric_name, explanation):
    """Creates a slash command function for a metric explanation."""

    @bot.tree.command(name=metric_name.lower(), description=f"Explanation of the {metric_name} metric.")
    async def metric_explanation_command(interaction: discord.Interaction):
        if await check_cooldown(interaction, metric_name.lower()):
            embed = discord.Embed(title=f"{metric_name} Metric Explanation", color=discord.Color.blue())
            embed.add_field(name="Explanation", value=explanation, inline=False)
            await interaction.response.send_message(embed=embed)

    return metric_explanation_command


# Create a command for each metric explanation from config.py
for metric_name, explanation in METRIC_EXPLANATIONS.items():
    create_metric_explanation_command(metric_name, explanation)


@bot.tree.command(name="help", description="Show available commands and their usage.")
async def help_command(interaction: discord.Interaction):
    if await check_cooldown(interaction, "help"):
        embed = discord.Embed(title="Available Commands", color=discord.Color.blue())
        embed.add_field(name="/player <player_name>", value="Get stats for a player.", inline=False)
        embed.add_field(name="/rankings", value="Get the link to the global rankings.", inline=False)
        embed.add_field(name="/wr", value="Shows the world record for each track.", inline=False)
        embed.add_field(name="/improvement <player_name>",
                        value="Shows the improvement potential for each track for the given player.", inline=False)
        embed.add_field(name="/help", value="Show this help message.", inline=False)

        # Add a general note about metric explanation commands
        embed.add_field(name="Metric Explanations",
                        value="For detailed explanations of each metric, use commands like `/diff`, `/ipr`, `/reldiff`, etc.",
                        inline=False)

        await interaction.response.send_message(embed=embed)


# --- Main Ranking Update Function ---
async def update_rankings_function():
    start_time = time.time()

    connector = aiohttp.TCPConnector(limit_per_host=13)
    async with aiohttp.ClientSession(connector=connector) as session:
        with tqdm(total=MAX_SKIP * len(TRACK_URLS), desc="Processing tracks") as pbar:
            tasks = [process_track(session, url_template, i, pbar) for i, url_template in enumerate(TRACK_URLS)]
            await asyncio.gather(*tasks)

    player_data = {}
    for player, times in player_times.items():
        if all(t is not None for t in times):
            total_time = sum(times)
            ranks = [bisect_left(sorted_track_times[TRACK_URLS[i]], times[i], key=lambda x: x[i]) + 1 for i in
                     range(len(TRACK_URLS))]
            average_rank = sum(ranks) / len(ranks)
            best_rank = min(ranks)
            worst_rank = max(ranks)

            player_data[player] = {
                "total_time": total_time,
                "average_rank": average_rank,
                "best_rank": best_rank,
                "worst_rank": worst_rank,
                "track_times": times,
                "ranks": ranks
            }

    sorted_players = sorted(player_data.items(), key=lambda x: x[1]["total_time"])

    sheet_data = []

    header = ["Rank", "Percentile", "Player Name", "Total Time", "Average Rank", "Best Rank", "Worst Rank"]
    header.extend(TRACK_NAMES)
    header.append("Tier")
    sheet_data.append(header)

    for i, (player, data) in enumerate(sorted_players, start=1):
        overall_percentile = (len(sorted_players) - i + 1) / len(sorted_players) * 100
        tier = get_tier(overall_percentile)
        tier = max(1, min(tier, N_TIERS))
        tier_image = TIER_IMAGES.get(tier, "")

        total_seconds = data["total_time"] / 1000
        hours, remainder = divmod(total_seconds, 3600)
        minutes, seconds = divmod(remainder, 60)
        milliseconds = int((seconds % 1) * 1000)
        seconds = int(seconds)
        minutes = int(minutes)
        hours = int(hours)

        if hours > 0:
            formatted_total_time = f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}"
        elif minutes > 0:
            formatted_total_time = f"{minutes}:{seconds:02d}.{milliseconds:03d}"
        else:
            formatted_total_time = f"{seconds}.{milliseconds:03d}"

        formatted_average_rank = format_average_rank(data['average_rank'])

        row = [i, f"{overall_percentile:.2f}", f"'{player}", formatted_total_time, formatted_average_rank,
               data["best_rank"], data["worst_rank"]]
        for j in range(len(TRACK_URLS)):
            track_time = data["track_times"][j]
            rank = data["ranks"][j]
            formatted_track_time = f"{track_time / 1000:.3f}"
            row.append(f"{formatted_track_time}s (#{rank})")
        row.append(tier_image)
        sheet_data.append(row)

    update_google_sheet(sheet_data)

    end_time = time.time()
    total_time = end_time - start_time
    hours, remainder = divmod(total_time, 3600)
    minutes, seconds = divmod(remainder, 60)
    milliseconds = int((seconds % 1) * 1000)
    seconds = int(seconds)
    minutes = int(minutes)
    hours = int(hours)

    if hours > 0:
        formatted_time = f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}"
    elif minutes > 0:
        formatted_time = f"{minutes}:{seconds:02d}.{milliseconds:03d}"
    else:
        formatted_time = f"{seconds}.{milliseconds:03d}"

    print(f"Data updated in Google Sheets")
    print(f"Total execution time: {formatted_time}")


# --- Run the Discord Bot ---
bot.run(DISCORD_BOT_TOKEN)