# import requests

import xlsxwriter

from gql import Client

from gql.transport.aiohttp import AIOHTTPTransport

import json

from types import SimpleNamespace

import pandas as pd

import time

from config.const import NBA_TEAMS_SLUGS, NBA_PLAYERS_NAME_OWNED

from models.NbaPlayer import NbaPlayer

import numpy as np

from config.queris import TeamPlayersQuery, PlayerLastStatsQuery

def calculateAverageScore(lastScoresList : list) -> tuple :

#print("analyzing num of games: ", len(lastScoresList))

filterdScoresList = list(filter(lambda lastScore: (lastScore["fixture"]["fixtureState"] != "opened" and lastScore["score"] > 0), lastScoresList))

totalLastThreeScores = 0

totalLastFiveScores = 0

fixtureStatsCounter = 0

for fixtureStats in filterdScoresList:

if fixtureStatsCounter >= 5 : break

score = fixtureStats["score"]

if fixtureStatsCounter < 3:

totalLastThreeScores += score

totalLastFiveScores += score

fixtureStatsCounter += 1

#print("total games analyzed: ", len(filterdScoresList), " - last 3 scores: ", totalLastThreeScores, " - last 5 scores: ", totalLastFiveScores)

lastThreeFixtureAnalyzed = fixtureStatsCounter if fixtureStatsCounter < 3 else 3

lastFiveFixtureAnalyzed = fixtureStatsCounter if fixtureStatsCounter < 5 else 5

lastThreeScoresAverage = 0

if lastThreeFixtureAnalyzed > 0 :

lastThreeScoresAverage = round(totalLastThreeScores / lastThreeFixtureAnalyzed)

lastFiveScoresAverage = 0

if lastFiveFixtureAnalyzed > 0 :

lastFiveScoresAverage = round(totalLastFiveScores / lastFiveFixtureAnalyzed)

return (lastThreeScoresAverage, lastFiveScoresAverage)

###################################

# Script start

###################################

# api_sorare_football_base_url = "https://api.sorare.com/graphql"

api_sorare_sports_base_url = "https://api.sorare.com/sports/graphql"

transport = AIOHTTPTransport(url=api_sorare_sports_base_url)

client = Client(transport=transport, fetch_schema_from_transport=True)

print(f"\n\n Elaborating statistics for {len(NBA_PLAYERS_NAME_OWNED)} players...\n\n")

players_slugs = []

for team in NBA_TEAMS_SLUGS:

# delay due to API calls limit

time.sleep(3)

print("Getting info for team...", team)

result = client.execute(TeamPlayersQuery, variable_values={

"team": team}, get_execution_result=True)

# Getting team player's slug by player's name owned

team_players = result.data["nbaTeam"]["players"]

for player in team_players:

if player["displayName"] in NBA_PLAYERS_NAME_OWNED:

player_slug = player["slug"]

players_slugs.append(player_slug)

print(f"\n\n Found {len(players_slugs)} player's slugs!\n\n")

#splitting list in many lists of 19 elements max due to limit into 'nbaPlayers' graphql api

chunked_players_slugs_array = np.array_split(players_slugs, (int(len(players_slugs) / 19)+1))

splitted_players_slugs_list = [list(array) for array in chunked_players_slugs_array]

nbaPlayersStatsList = []

for players_slugs_list in splitted_players_slugs_list:

result = client.execute(PlayerLastStatsQuery, variable_values={"players": players_slugs_list}, get_execution_result=True)

ownedPlayers = result.data["nbaPlayers"]

for player in ownedPlayers:

lastScores = calculateAverageScore(player["latestFinalFixtureStats"])

nbaPlayerStats = NbaPlayer(player["slug"], player["team"]["name"], player["isActive"], lastScores[0], lastScores[1], player["tenGameAverage"])

nbaPlayersStatsList.append(nbaPlayerStats)

active_nba_players_stats_list = filter(lambda nbaPlayer: nbaPlayer.isActive, nbaPlayersStatsList)

df_players_stats = pd.DataFrame(sorted(active_nba_players_stats_list, reverse=True))

pd.set_option('display.max_rows', None)

print(df_players_stats.rename(columns={'slug': 'Player', 'team': 'Team', 'latestThreeFixtureStats': 'L3', 'latestFiveFixtureStats': 'L5', 'tenGameAverage': 'L10', 'latestThreeFixtureStatsDiff': 'L3 Diff', 'latestFiveFixtureStatsDiff': 'L5 Diff'}))

print(f"\n\n...done!\n\n")