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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| 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" | |
| } | |
| gspreadcon = 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%}', | |
| '120+%': '{:.2%}','10x%': '{:.2%}','11x%': '{:.2%}','12x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}', 'CPT_Own': '{.2%}'} | |
| odds_format = {'Odds': '{:.2%}'} | |
| stat_format = {'Odds%': '{:.2%}'} | |
| map_proj_format = {'Win%': '{:.2%}'} | |
| master_hold = 'https://docs.google.com/spreadsheets/d/1dOXsbeWbvWjRyohsEEDXOiWji4-1R1J6E-Lu2CSM9AM/edit#gid=928272897' | |
| def pull_baselines(): | |
| sh = gspreadcon.open_by_url(master_hold) | |
| worksheet = sh.worksheet('Overall_Vegas') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display = raw_display.loc[raw_display['Team'] != ""] | |
| odds_table = raw_display[['Team', 'Vegas', 'Odds', 'Games']] | |
| worksheet = sh.worksheet('Overall_ROO') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| overall_roo = raw_display.loc[raw_display['Player'] != ""] | |
| worksheet = sh.worksheet('Win_ROO') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| win_roo = raw_display.loc[raw_display['Player'] != ""] | |
| worksheet = sh.worksheet('Loss_ROO') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| loss_roo = raw_display.loc[raw_display['Player'] != ""] | |
| worksheet = sh.worksheet('3_map_Proj') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display = raw_display.loc[raw_display['Player'] != ""] | |
| map_proj_3 = raw_display[['Player', 'Team', 'Opponent', 'Odds', 'Win%', 'Avg Kills', 'Avg Deaths', 'Proj_Kills', 'Proj_Deaths']] | |
| data_cols = map_proj_3.columns.drop(['Player', 'Team', 'Opponent', 'Win%']) | |
| map_proj_3[data_cols] = map_proj_3[data_cols].apply(pd.to_numeric, errors='coerce') | |
| worksheet = sh.worksheet('Timestamp') | |
| timestamp = worksheet.acell('A1').value | |
| return odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2, tab3 = st.tabs(["COD Odds Tables", "COD Range of Outcomes", "COD 3-map projections"]) | |
| with tab1: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| odds_display = odds_table | |
| st.dataframe(odds_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(odds_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(odds_display), | |
| file_name='COD_Odds_Tables_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table') | |
| team_var1 = st.multiselect('View specific team?', options = overall_roo['Team'].unique(), key = 'roo_teamvar') | |
| if model_choice == 'Overall': | |
| hold_display = overall_roo | |
| elif model_choice == 'Wins': | |
| hold_display = win_roo | |
| elif model_choice == 'Losses': | |
| hold_display = loss_roo | |
| hold_display['Cpt_Own'] = (hold_display['Own']) * ((100 - (100-hold_display['Own']))) | |
| cpt_own_norm = 100 / hold_display['Cpt_Own'].sum() | |
| hold_display['Cpt_Own'] = (hold_display['Cpt_Own'] * cpt_own_norm) | |
| display = hold_display.set_index('Player') | |
| export_display = display | |
| export_display['Position'] = "FLEX" | |
| if team_var1: | |
| display = display[display['Team'].isin(team_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(export_display), | |
| file_name='CSGO_ROO_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| st.info(t_stamp) | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| team_var2 = st.multiselect('View specific team?', options = map_proj_3['Team'].unique(), key = 'stat_teamvar') | |
| map_stat_display = map_proj_3 | |
| if team_var2: | |
| map_stat_display = map_stat_display[display['Team'].isin(team_var2)] | |
| st.dataframe(map_stat_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(map_proj_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(map_stat_display), | |
| file_name='COD_Projections_export.csv', | |
| mime='text/csv', | |
| ) |