import pulp import numpy as np import pandas as pd import random import sys import openpyxl import re import time import streamlit as st import matplotlib from matplotlib.colors import LinearSegmentedColormap from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode import json import requests import gspread import plotly.figure_factory as ff scope = ['https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive"] credentials = { "type": "service_account", "project_id": "sheets-api-connect-378620", "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", "client_id": "106625872877651920064", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" } gc = gspread.service_account_from_dict(credentials) st.set_page_config(layout="wide") @st.cache_data def init_baselines(): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit#gid=1858245367') worksheet = sh.worksheet('ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.loc[raw_display['Salary'] > 0] raw_display = raw_display.loc[raw_display['Median'] > 0] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') roo_table = raw_display.sort_values(by='Median', ascending=False) # worksheet = sh.worksheet('Positional_Boosts') # raw_display = pd.DataFrame(worksheet.get_all_records()) # raw_display.replace("", 'Welp', inplace=True) # raw_display = raw_display.loc[raw_display['teamname'] != 'Welp'] # raw_display = raw_display.apply(pd.to_numeric, errors='ignore') # positional_boosts = raw_display.sort_values(by='Avg_Allowed', ascending=False) worksheet = sh.worksheet('Overall_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Win_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_win_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Loss_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Overall_BO1_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_bo1 = raw_display worksheet = sh.worksheet('Overall_BO3_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_bo3 = raw_display worksheet = sh.worksheet('Overall_BO5_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lck_bo5 = raw_display sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1W5gH-ef2K6gX3Dw86n-YbXxQhbpUiUosDOiH4-mzX8g/edit#gid=582025223') worksheet = sh.worksheet('Overall_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Win_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_win_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Loss_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Overall_BO1_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_bo1 = raw_display worksheet = sh.worksheet('Overall_BO3_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_bo3 = raw_display worksheet = sh.worksheet('Overall_BO5_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lcs_bo5 = raw_display sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1oOJD_QcBeDJ1f7e9FfgUHOQEPT6kvU0Sa9hQ_4B8gqc/edit#gid=0') worksheet = sh.worksheet('Overall_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_overall_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Win_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_win_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Loss_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Team'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_loss_stacks = raw_display.sort_values(by='Stack+', ascending=False) worksheet = sh.worksheet('Overall_BO1_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_bo1 = raw_display worksheet = sh.worksheet('Overall_BO3_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_bo3 = raw_display worksheet = sh.worksheet('Overall_BO5_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True) raw_display.replace("", 'Welp', inplace=True) raw_display = raw_display.loc[raw_display['Player'] != 'Welp'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') lec_bo5 = raw_display return roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines() tab1, tab2, tab3 = st.tabs(["LOL Stacks Table", "LOL Range of Outcomes", "LOL Player Base Stats"]) def convert_df_to_csv(df): return df.to_csv().encode('utf-8') with tab1: if st.button("Reset Data", key='reset1'): # Clear values from *all* all in-memory and on-disk data caches: # i.e. clear values from both square and cube st.cache_data.clear() roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines() league_choice1 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var1') if league_choice1 == 'LCK/LPL': league_hold = lck_overall_stacks elif league_choice1 == 'LCS': league_hold = lcs_overall_stacks elif league_choice1 == 'LEC': league_hold = lec_overall_stacks display = league_hold.set_index('Team') st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) st.download_button( label="Export Stacks", data=convert_df_to_csv(display), file_name='LOL_Stacks_export.csv', mime='text/csv', ) with tab2: if st.button("Reset Data", key='reset2'): # Clear values from *all* all in-memory and on-disk data caches: # i.e. clear values from both square and cube st.cache_data.clear() roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines() league_choice2 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var2') if league_choice2 == 'LCK/LPL': league_hold = roo_table[roo_table['league'] == 'LCK'] elif league_choice2 == 'LCS': league_hold = roo_table[roo_table['league'] == 'LCS'] elif league_choice2 == 'LEC': league_hold = roo_table[roo_table['league'] == 'LEC'] model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table') if model_choice == 'Overall': hold_display = league_hold[league_hold['type'] == 'Overall'] elif model_choice == 'Wins': hold_display = league_hold[league_hold['type'] == 'Wins'] elif model_choice == 'Losses': hold_display = league_hold[league_hold['type'] == 'Losses'] pos_var1 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'roo_posvar') team_var1 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'roo_teamvar') display = hold_display.set_index('Player') if team_var1: display = display[display['Team'].isin(team_var1)] if pos_var1 == 'All': display = display elif pos_var1 != 'All': display = display[display['Position'].str.contains(pos_var1)] st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) st.download_button( label="Export Range of Outcomes", data=convert_df_to_csv(display), file_name='LOL_ROO_export.csv', mime='text/csv', ) with tab3: if st.button("Reset Data", key='reset3'): # Clear values from *all* all in-memory and on-disk data caches: # i.e. clear values from both square and cube st.cache_data.clear() roo_table, lck_overall_stacks, lck_win_stacks, lck_loss_stacks, lcs_overall_stacks, lcs_win_stacks, lcs_loss_stacks, lec_overall_stacks, lec_win_stacks, lec_loss_stacks, lck_bo1, lck_bo3, lck_bo5, lcs_bo1, lcs_bo3, lcs_bo5, lec_bo1, lec_bo3, lec_bo5 = init_baselines() league_choice3 = st.radio("What table would you like to display?", ('LCK/LPL', 'LCS', 'LEC'), key='league_var3') gametype_choice = st.radio("What format are the games being played?", ('Best of 1', 'Best of 3', 'Best of 5'), key='player_stats') pos_var2 = st.selectbox('View specific position?', options = ['All', 'TOP', 'JNG', 'MID', 'ADC', 'SUP'], key = 'proj_posvar') team_var2 = st.multiselect('View specific team?', options = hold_display['Team'].unique(), key = 'proj_teamvar') if league_choice3 == 'LCK/LPL': if gametype_choice == 'Best of 1': hold_display = lck_bo1 elif gametype_choice == 'Best of 3': hold_display = lck_bo3 elif gametype_choice == 'Best of 5': hold_display = lck_bo5 display = hold_display.set_index('Player') elif league_choice3 == 'LCS': if gametype_choice == 'Best of 1': hold_display = lcs_bo1 elif gametype_choice == 'Best of 3': hold_display = lcs_bo3 elif gametype_choice == 'Best of 5': hold_display = lcs_bo5 display = hold_display.set_index('Player') elif league_choice3 == 'LEC': if gametype_choice == 'Best of 1': hold_display = lec_bo1 elif gametype_choice == 'Best of 3': hold_display = lec_bo3 elif gametype_choice == 'Best of 5': hold_display = lec_bo5 display = hold_display.set_index('Player') if team_var2: display = display[display['Team'].isin(team_var2)] if pos_var2 == 'All': display = display elif pos_var2 != 'All': display = display[display['Position'].str.contains(pos_var2)] st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) st.download_button( label="Export Baselines", data=convert_df_to_csv(display), file_name='LOL_Baselines_export.csv', mime='text/csv', )