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| 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' | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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', | |
| ) |