James McCool
Refactor app.py to streamline database connections and enhance seed frame initialization. Removed hardcoded credentials and improved function signatures to include a 'split' parameter for limiting data retrieval. Updated user interface logic for selecting sports and contest types, ensuring better data handling and export functionality.
cc5cc89
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import gspread | |
| import pymongo | |
| import time | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| return client | |
| client = init_conn() | |
| percentages_format = {'Exposure': '{:.2%}'} | |
| freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} | |
| dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| def init_DK_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"DK_{sport}_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_DK_secondary_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_second_seed = raw_display.to_numpy() | |
| return DK_second_seed | |
| def init_DK_auxiliary_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"DK_{sport}_Auxiliary_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_auxiliary_seed = raw_display.to_numpy() | |
| return DK_auxiliary_seed | |
| def init_FD_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"FD_{sport}_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def init_FD_secondary_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| FD_second_seed = raw_display.to_numpy() | |
| return FD_second_seed | |
| def init_FD_auxiliary_seed_frames(sport, split): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db[f"FD_{sport}_Auxiliary_SD_seed_frame"] | |
| cursor = collection.find().limit(split) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| FD_auxiliary_seed = raw_display.to_numpy() | |
| return FD_auxiliary_seed | |
| def init_baselines(sport): | |
| if sport == 'NFL': | |
| db = client["NFL_Database"] | |
| collection = db['DK_SD_NFL_ROO'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display['Small_Field_Own'] = raw_display['Large_Field_Own'] | |
| raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100) | |
| small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum() | |
| raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var | |
| raw_display['cpt_Median'] = raw_display['Median'] * 1.25 | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| dk_raw = raw_display.dropna(subset=['Median']) | |
| collection = db['FD_SD_NFL_ROO'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', | |
| 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']] | |
| raw_display['Small_Field_Own'] = raw_display['Large_Field_Own'] | |
| raw_display['small_CPT_Own'] = raw_display['CPT_Own'] | |
| raw_display['cpt_Median'] = raw_display['Median'] | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| fd_raw = raw_display.dropna(subset=['Median']) | |
| elif sport == 'NBA': | |
| db = client["NBA_DFS"] | |
| collection = db['Player_SD_Range_Of_Outcomes'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] | |
| raw_display = raw_display[raw_display['site'] == 'Draftkings'] | |
| raw_display['Small_Field_Own'] = raw_display['Small_Own'] | |
| raw_display['small_CPT_Own_raw'] = (raw_display['Small_Field_Own'] / 2) * ((100 - (100-raw_display['Small_Field_Own']))/100) | |
| small_cpt_own_var = 300 / raw_display['small_CPT_Own_raw'].sum() | |
| raw_display['small_CPT_Own'] = raw_display['small_CPT_Own_raw'] * small_cpt_own_var | |
| raw_display['cpt_Median'] = raw_display['Median'] * 1.25 | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| dk_raw = raw_display.dropna(subset=['Median']) | |
| collection = db['Player_SD_Range_Of_Outcomes'] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] | |
| raw_display = raw_display[raw_display['site'] == 'Fanduel'] | |
| raw_display['Small_Field_Own'] = raw_display['Large_Own'] | |
| raw_display['small_CPT_Own'] = raw_display['CPT_Own'] | |
| raw_display['cpt_Median'] = raw_display['Median'] | |
| raw_display['STDev'] = raw_display['Median'] / 4 | |
| raw_display['CPT_STDev'] = raw_display['cpt_Median'] / 4 | |
| fd_raw = raw_display.dropna(subset=['Median']) | |
| return dk_raw, fd_raw | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| def calculate_DK_value_frequencies(np_array): | |
| unique, counts = np.unique(np_array[:, :6], return_counts=True) | |
| frequencies = counts / len(np_array) # Normalize by the number of rows | |
| combined_array = np.column_stack((unique, frequencies)) | |
| return combined_array | |
| def calculate_FD_value_frequencies(np_array): | |
| unique, counts = np.unique(np_array[:, :5], return_counts=True) | |
| frequencies = counts / len(np_array) # Normalize by the number of rows | |
| combined_array = np.column_stack((unique, frequencies)) | |
| return combined_array | |
| def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size): | |
| SimVar = 1 | |
| Sim_Winners = [] | |
| # Pre-vectorize functions | |
| vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) | |
| vec_cpt_projection_map = np.vectorize(maps_dict['cpt_projection_map'].__getitem__) | |
| vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) | |
| vec_cpt_stdev_map = np.vectorize(maps_dict['cpt_STDev_map'].__getitem__) | |
| st.write('Simulating contest on frames') | |
| while SimVar <= Sim_size: | |
| fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)] | |
| sample_arrays1 = np.c_[ | |
| fp_random, | |
| np.sum(np.random.normal( | |
| loc=np.concatenate([ | |
| vec_cpt_projection_map(fp_random[:, 0:1]), # Apply cpt_projection_map to first column | |
| vec_projection_map(fp_random[:, 1:-7]) # Apply original projection to remaining columns | |
| ], axis=1), | |
| scale=np.concatenate([ | |
| vec_cpt_stdev_map(fp_random[:, 0:1]), # Apply cpt_projection_map to first column | |
| vec_stdev_map(fp_random[:, 1:-7]) # Apply original projection to remaining columns | |
| ], axis=1)), | |
| axis=1) | |
| ] | |
| sample_arrays = sample_arrays1 | |
| final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]] | |
| best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] | |
| Sim_Winners.append(best_lineup) | |
| SimVar += 1 | |
| return Sim_Winners | |
| dk_raw, fd_raw = init_baselines('NFL') | |
| tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) | |
| with tab2: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| dk_raw, fd_raw = init_baselines('NFL') | |
| sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sport_var1') | |
| slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1') | |
| sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| if site_var1 == 'Draftkings': | |
| team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') | |
| if team_var1 == 'Specific Teams': | |
| team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique()) | |
| elif team_var1 == 'Full Slate': | |
| team_var2 = dk_raw.Team.values.tolist() | |
| stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') | |
| if stack_var1 == 'Specific Stack Sizes': | |
| stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0]) | |
| elif stack_var1 == 'Full Slate': | |
| stack_var2 = [5, 4, 3, 2, 1, 0] | |
| elif site_var1 == 'Fanduel': | |
| team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1') | |
| if team_var1 == 'Specific Teams': | |
| team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique()) | |
| elif team_var1 == 'Full Slate': | |
| team_var2 = fd_raw.Team.values.tolist() | |
| stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1') | |
| if stack_var1 == 'Specific Stack Sizes': | |
| stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0]) | |
| elif stack_var1 == 'Full Slate': | |
| stack_var2 = [4, 3, 2, 1, 0] | |
| if st.button("Prepare data export", key='data_export'): | |
| if 'working_seed' in st.session_state: | |
| data_export = st.session_state.working_seed.copy() | |
| elif 'working_seed' not in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| elif site_var1 == 'Fanduel': | |
| if slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| data_export = st.session_state.working_seed.copy() | |
| for col in range(6): | |
| data_export[:, col] = np.array([export_id_dict.get(x, x) for x in data_export[:, col]]) | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='NFL_SD_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| with col2: | |
| if st.button("Load Data", key='load_data'): | |
| if site_var1 == 'Draftkings': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| if slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| elif site_var1 == 'Fanduel': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| if slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)] | |
| st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)] | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names) | |
| with st.container(): | |
| if 'data_export_display' in st.session_state: | |
| st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True) | |
| with tab1: | |
| col1, col2 = st.columns([1, 7]) | |
| with col1: | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| dk_raw, fd_raw = init_baselines('NFL') | |
| sim_sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sim_sport_var1') | |
| dk_raw, fd_raw = init_baselines(sim_sport_var1) | |
| sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='sim_slate_var1') | |
| sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1') | |
| if sim_site_var1 == 'Draftkings': | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| elif sim_site_var1 == 'Fanduel': | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom')) | |
| if contest_var1 == 'Small': | |
| Contest_Size = 1000 | |
| st.write("Small field size is 1,000 entrants.") | |
| raw_baselines['Own'] = raw_baselines['Small_Field_Own'] | |
| raw_baselines['CPT_Own'] = raw_baselines['small_CPT_Own'] | |
| elif contest_var1 == 'Medium': | |
| Contest_Size = 5000 | |
| st.write("Medium field size is 5,000 entrants.") | |
| elif contest_var1 == 'Large': | |
| Contest_Size = 10000 | |
| st.write("Large field size is 10,000 entrants.") | |
| elif contest_var1 == 'Custom': | |
| Contest_Size = st.number_input("Insert contest size", value=100, min_value=1, max_value=100000) | |
| strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very')) | |
| if strength_var1 == 'Not Very': | |
| sharp_split = 500000 | |
| elif strength_var1 == 'Below Average': | |
| sharp_split = 400000 | |
| elif strength_var1 == 'Average': | |
| sharp_split = 300000 | |
| elif strength_var1 == 'Above Average': | |
| sharp_split = 200000 | |
| elif strength_var1 == 'Very': | |
| sharp_split = 100000 | |
| with col2: | |
| if st.button("Run Contest Sim"): | |
| if 'working_seed' in st.session_state: | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), | |
| 'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| #st.table(Sim_Winner_Frame) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| else: | |
| if sim_site_var1 == 'Draftkings': | |
| if sim_slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sim_slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sim_slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id'])) | |
| raw_baselines = dk_raw | |
| column_names = dk_columns | |
| elif sim_site_var1 == 'Fanduel': | |
| if sim_slate_var1 == 'Showdown': | |
| st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sim_slate_var1 == 'Secondary Showdown': | |
| st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sim_slate_var1 == 'Auxiliary Showdown': | |
| st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var) | |
| if sport_var1 == 'NFL': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| elif sport_var1 == 'NBA': | |
| export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id'])) | |
| raw_baselines = fd_raw | |
| column_names = fd_columns | |
| maps_dict = { | |
| 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)), | |
| 'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)), | |
| 'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)), | |
| 'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)), | |
| 'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])), | |
| 'cpt_Own_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_Own'])), | |
| 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), | |
| 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)), | |
| 'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev'])) | |
| } | |
| Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size) | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners)) | |
| #st.table(Sim_Winner_Frame) | |
| # Initial setup | |
| Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy']) | |
| Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2 | |
| Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str) | |
| # Add percent rank columns for ownership at each roster position | |
| # Calculate Dupes column for Fanduel | |
| if sim_site_var1 == 'Fanduel': | |
| dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank'] | |
| own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own'] | |
| calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc'] | |
| Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100 | |
| # Calculate ownership product and convert to probability | |
| Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) + 0.0001 | |
| # Calculate average of ownership percent rank columns | |
| Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1) | |
| # Calculate dupes formula | |
| Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 59800) / 100) | |
| # Round and handle negative values | |
| Sim_Winner_Frame['Dupes'] = np.where( | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, | |
| 0, | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1 | |
| ) | |
| Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2 | |
| elif sim_site_var1 == 'Draftkings': | |
| dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank'] | |
| own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own'] | |
| calc_columns = ['own_product', 'avg_own_rank', 'dupes_calc'] | |
| Sim_Winner_Frame['CPT_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX1_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX2_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX3_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX4_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['FLEX5_Own_percent_rank'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']).rank(pct=True) | |
| Sim_Winner_Frame['CPT_Own'] = Sim_Winner_Frame.iloc[:,0].map(maps_dict['cpt_Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX1_Own'] = Sim_Winner_Frame.iloc[:,1].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX2_Own'] = Sim_Winner_Frame.iloc[:,2].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX3_Own'] = Sim_Winner_Frame.iloc[:,3].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX4_Own'] = Sim_Winner_Frame.iloc[:,4].map(maps_dict['Own_map']) / 100 | |
| Sim_Winner_Frame['FLEX5_Own'] = Sim_Winner_Frame.iloc[:,5].map(maps_dict['Own_map']) / 100 | |
| # Calculate ownership product and convert to probability | |
| Sim_Winner_Frame['own_product'] = (Sim_Winner_Frame[own_columns].product(axis=1)) | |
| # Calculate average of ownership percent rank columns | |
| Sim_Winner_Frame['avg_own_rank'] = Sim_Winner_Frame[dup_count_columns].mean(axis=1) | |
| # Calculate dupes formula | |
| Sim_Winner_Frame['dupes_calc'] = ((Sim_Winner_Frame['own_product'] * Sim_Winner_Frame['avg_own_rank']) * (Contest_Size * 1.5)) + ((Sim_Winner_Frame['salary'] - 49800) / 100) | |
| # Round and handle negative values | |
| Sim_Winner_Frame['Dupes'] = np.where( | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, | |
| 0, | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) - 1 | |
| ) | |
| Sim_Winner_Frame['Dupes'] = (Sim_Winner_Frame['Dupes'] * (500000 / sharp_split)) / 2 | |
| Sim_Winner_Frame['Dupes'] = np.round(Sim_Winner_Frame['Dupes'], 0) | |
| Sim_Winner_Frame['Dupes'] = np.where( | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) <= 0, | |
| 0, | |
| np.round(Sim_Winner_Frame['dupes_calc'], 0) | |
| ) | |
| Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=dup_count_columns) | |
| Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=own_columns) | |
| Sim_Winner_Frame = Sim_Winner_Frame.drop(columns=calc_columns) | |
| Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts())) | |
| # Type Casting | |
| type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32, 'Dupes': int} | |
| Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) | |
| # Sorting | |
| st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100) | |
| st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy() | |
| st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict)) | |
| # Data Copying | |
| st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy() | |
| freq_copy = st.session_state.Sim_Winner_Display | |
| if sim_site_var1 == 'Draftkings': | |
| freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| freq_working['Freq'] = freq_working['Freq'].astype(int) | |
| freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map']) | |
| if sim_site_var1 == 'Draftkings': | |
| if sim_sport_var1 == 'NFL': | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5 | |
| elif sim_sport_var1 == 'NBA': | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) | |
| elif sim_site_var1 == 'Fanduel': | |
| freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) | |
| freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100 | |
| freq_working['Exposure'] = freq_working['Freq']/(1000) | |
| freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own'] | |
| freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.player_freq = freq_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_working['Freq'] = cpt_working['Freq'].astype(int) | |
| cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map']) | |
| if sim_sport_var1 == 'NFL': | |
| cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) | |
| elif sim_sport_var1 == 'NBA': | |
| cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map']) * 1.5 | |
| cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100 | |
| cpt_working['Exposure'] = cpt_working['Freq']/(1000) | |
| cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own'] | |
| cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.sp_freq = cpt_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_own_div = 600 | |
| elif sim_site_var1 == 'Fanduel': | |
| flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| cpt_own_div = 500 | |
| flex_working['Freq'] = flex_working['Freq'].astype(int) | |
| flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map']) | |
| if sim_site_var1 == 'Draftkings': | |
| if sim_sport_var1 == 'NFL': | |
| flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5 | |
| elif sim_sport_var1 == 'NBA': | |
| flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) | |
| elif sim_site_var1 == 'Fanduel': | |
| flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) | |
| flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100) | |
| flex_working['Exposure'] = flex_working['Freq']/(1000) | |
| flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own'] | |
| flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map']) | |
| st.session_state.flex_freq = flex_working.copy() | |
| if sim_site_var1 == 'Draftkings': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| elif sim_site_var1 == 'Fanduel': | |
| team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].values, return_counts=True)), | |
| columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) | |
| team_working['Freq'] = team_working['Freq'].astype(int) | |
| team_working['Exposure'] = team_working['Freq']/(1000) | |
| st.session_state.team_freq = team_working.copy() | |
| with st.container(): | |
| if st.button("Reset Sim", key='reset_sim'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if 'player_freq' in st.session_state: | |
| player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2') | |
| if player_split_var2 == 'Specific Players': | |
| find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique()) | |
| elif player_split_var2 == 'Full Players': | |
| find_var2 = st.session_state.player_freq.Player.values.tolist() | |
| if player_split_var2 == 'Specific Players': | |
| st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)] | |
| if player_split_var2 == 'Full Players': | |
| st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame | |
| if 'Sim_Winner_Display' in st.session_state: | |
| st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| if 'Sim_Winner_Export' in st.session_state: | |
| st.download_button( | |
| label="Export Full Frame", | |
| data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'), | |
| file_name='NFL_SD_consim_export.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures']) | |
| with tab1: | |
| if 'player_freq' in st.session_state: | |
| st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.player_freq.to_csv().encode('utf-8'), | |
| file_name='player_freq_export.csv', | |
| mime='text/csv', | |
| key='overall' | |
| ) | |
| with tab2: | |
| if 'sp_freq' in st.session_state: | |
| st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.sp_freq.to_csv().encode('utf-8'), | |
| file_name='cpt_freq.csv', | |
| mime='text/csv', | |
| key='sp' | |
| ) | |
| with tab3: | |
| if 'flex_freq' in st.session_state: | |
| st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.flex_freq.to_csv().encode('utf-8'), | |
| file_name='flex_freq.csv', | |
| mime='text/csv', | |
| key='flex' | |
| ) | |
| with tab4: | |
| if 'team_freq' in st.session_state: | |
| st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Exposures", | |
| data=st.session_state.team_freq.to_csv().encode('utf-8'), | |
| file_name='team_freq.csv', | |
| mime='text/csv', | |
| key='team' | |
| ) |