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") roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '40+%': '{:.2%}','3x%': '{:.2%}','4x%': '{:.2%}','5x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}'} stat_format = {'Odds%': '{:.2%}', 'Boosts': '{:.2%}'} overall_table = 'LOL_Overall_Proj' wins_table = 'LOL_Win_Proj' losses_table = 'LOL_Loss_Proj' stacks_table = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0' bo1_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0' bo3_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0' bo5_player_stats = 'https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0' @st.cache_data def load_roo_model(outcome): sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/10MVGsAHJPUAdK9SJ28ZqjgBgV2xBJSXEka-s2pIxHHE/edit?pli=1#gid=0') worksheet = sh.worksheet('ROO') raw_display = pd.DataFrame(worksheet.get_all_records()) if outcome == 'Overall': raw_display = raw_display.loc[raw_display['type'] == 'Overall'] elif outcome == 'Wins': raw_display = raw_display.loc[raw_display['type'] == 'Wins'] elif outcome == 'Losses': raw_display = raw_display.loc[raw_display['type'] == 'Losses'] raw_display["Salary"] = raw_display["Salary"].replace("$", "", regex=True).astype(float) raw_display['Top_finish'] = raw_display['Top_finish'].astype(float)/100 raw_display['Top_5_finish'] = raw_display['Top_5_finish'].astype(float)/100 raw_display['Top_10_finish'] = raw_display['Top_10_finish'].astype(float)/100 raw_display['40+%'] = raw_display['40+%'].astype(float)/100 raw_display['3x%'] = raw_display['3x%'].astype(float)/100 raw_display['4x%'] = raw_display['4x%'].astype(float)/100 raw_display['5x%'] = raw_display['5x%'].astype(float)/100 raw_display['Own'] = raw_display['Own'].astype(float)/100 raw_display['LevX'] = raw_display['LevX'].astype(float)/100 return raw_display @st.cache_data def load_bo1_proj_model(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('Overall_BO1_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player"}, inplace = True) raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100 raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100 raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') raw_display = raw_display.sort_values(by='Kills', ascending=False) return raw_display @st.cache_data def load_bo3_proj_model(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('Overall_BO3_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player"}, inplace = True) raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100 raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100 raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') raw_display = raw_display.sort_values(by='Kills', ascending=False) return raw_display @st.cache_data def load_bo5_proj_model(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('Overall_BO5_Stats') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display.rename(columns={"Name": "Player"}, inplace = True) raw_display['Odds%'] = raw_display['Odds%'].astype(float)/100 raw_display['Boosts'] = raw_display['Kill Boost'].astype(float)/100 raw_display = raw_display.loc[raw_display['Kills'] != '#DIV/0!'] raw_display = raw_display.apply(pd.to_numeric, errors='ignore') raw_display = raw_display.sort_values(by='Kills', ascending=False) return raw_display @st.cache_data def load_stacks_table(URL): sh = gc.open_by_url(URL) worksheet = sh.worksheet('Overall_Stacks') raw_display = pd.DataFrame(worksheet.get_all_records()) raw_display = raw_display.sort_values(by='Stack+', ascending=False) return raw_display 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() hold_display = load_stacks_table(stacks_table) display = hold_display.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() model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table') 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') hold_display = load_roo_model(model_choice) 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(roo_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() 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 gametype_choice == 'Best of 1': hold_display = load_bo1_proj_model(bo1_player_stats) elif gametype_choice == 'Best of 3': hold_display = load_bo3_proj_model(bo3_player_stats) elif gametype_choice == 'Best of 5': hold_display = load_bo5_proj_model(bo5_player_stats) 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(stat_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', )