import pandas as pd
from fuzzy import *


def zapisz_do_csv(nazwa_pliku, dataframe):
    dataframe.to_csv(nazwa_pliku, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)

def podziel_na_partie(dataframe, rozmiar_partii):
    for i in range(0, len(dataframe), rozmiar_partii):
        yield dataframe.iloc[i:i + rozmiar_partii]

def przetwarzaj_co_50_rekordow(plik_wejsciowy, plik_wyjsciowy):
    dataframe_wejsciowe = pd.read_csv(plik_wejsciowy)


def generateTrainingData(dataframe):
    columns = ['season','date','home_team','away_team','result_full','home_passes','away_passes',
               'home_possession','away_possession','home_shots','away_shots']
    return dataframe[columns]


def generateFuzzyLogicData(dataframe):
    columns = ['season','date','home_team','away_team','result_full','c_home_form_5m','c_away_form_5m',#,'c_home_passes','c_away_passes',
              # 'c_home_possession','c_away_possession','c_home_shots','c_away_shots',
               'c_home_diff_5m', 'c_away_diff_5m',"c_home_form_5s",
               'c_away_form_5s','c_home_diff_5s','c_away_diff_5s'
               , 'c_home_aggression_5m',
               'c_away_aggression_5m', 'c_away_shots_5m','c_away_shots_5m',
               'c_away_shots_5btw', 'c_away_shots_5btw', 'c_away_defence_5m',
               'c_away_defence_5m', 'c_away_defence_5btw', 'c_away_defence_5btw',
               'c_home_passing_5m', 'c_away_passing_5m', 'c_home_passing_5btw',
               'c_away_passing_5btw', 'c_away_aggression_5btw', 'c_away_aggression_5btw'


               #'c_home_aggression_season', 'c_away_aggression_season',
             #  'c_home_form_season','c_away_form_season',
            #   'c_home_diff_season', 'c_away_diff_season'
               ]
    return dataframe[columns]


def last5Matches(season, teamA, data, df):
    # Wybierz rekordy dla danej pary drużyn i sezonu
    subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]

    # Filtruj dane, aby zawierały te przed daną datą
    before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]

    # Posortuj wg daty w odwrotnej kolejności
    before_given_date = before_given_date.sort_values(by='date', ascending=False)

    # Wybierz 5 ostatnich przed daną datą
    last_before_date = before_given_date.head(5)

    return last_before_date, "_5m"

def last5MatchesBtwTeams(teamA, teamB, data, df):
    subset = df[(((df['home_team'] == teamA) | (df['away_team'] == teamA)) & ((df['home_team'] == teamB) | (df['away_team'] == teamB)))]
    before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
    before_given_date = before_given_date.sort_values(by='date', ascending=False)
    last_before_date = before_given_date.head(5)

    return last_before_date, "_5btw"

def seasonMatches(season, teamA, data, df):
    # Wybierz rekordy dla danej pary drużyn i sezonu
    subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]

    # Filtruj dane, aby zawierały te przed daną datą
    before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]

    # Posortuj wg daty w odwrotnej kolejności
    before_given_date = before_given_date.sort_values(by='date', ascending=False)

    return before_given_date, "_s"

def getResult(score,teamHome):
    x,y = score.split('-')
    x = int(x)
    y = int(y)

    if (x > y and teamHome == True) or (x < y and teamHome == False):
        return "win"
    elif x == y:
        return "draw"
    else:
        return "loss"

# def calculateAggression(matches, team):
#     aggression = 0
#     for index, row in matches.iterrows():
#         if team == row['home_team']:
#             yellow_cards = row['home_yellow_cards']
#             red_cards = row['home_red_cards']
#         else:
#             yellow_cards = row['away_yellow_cards']
#             red_cards = row['away_red_cards']
#         aggression_result = calculateFuzzyAggression(yellow_cards, red_cards)
#         #print(aggression_result['aggression'])
#         aggression = aggression + aggression_result['aggression']
#     if matches.shape[0] != 0:
#         aggression_avg = aggression / matches.shape[0]
#     else:
#         aggression_avg = 0
#     return aggression_avg

def calculatePoints(matches, team):
    points = 0
    for index, row in matches.iterrows():
        if team == row['home_team']:
            teamHome = True
        else:
            teamHome = False
        x = getResult(row['result_full'], teamHome)
        #print(x)
        if x == "win":
            points = points + 3
        elif x == "draw":
            points = points + 1
    if matches.shape[0] != 0:
        points_avg = points / matches.shape[0] 
    else:
        points_avg = 0
    return points_avg


def calculateGoalDifference(matches, team):
    goal_diff = 0
    for index, row in matches.iterrows():
        if team == row['home_team']:
            teamHome = True
        else:
            teamHome = False
        x,y = row['result_full'].split('-')
        x = int(x)
        y = int(y)
        if teamHome:
            goal_diff = goal_diff + (x-y)
        else:
            goal_diff = goal_diff + (y-x)
    return goal_diff

def calculateColumn(matches, team, column_name):
    result = 0
    for index, row in matches.iterrows():
        if team == row['home_team']:
            column = row[column_name]
        else:
            column = row[column_name]
        result = result + column
    if matches.shape[0] != 0:
        result_avg = result / matches.shape[0]
    else:
        result_avg = 0
    return result_avg