diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,123 +1,151 @@ import streamlit as st st.set_page_config(layout="wide") - -for name in dir(): - if not name.startswith('_'): - del globals()[name] - import numpy as np import pandas as pd -import streamlit as st import gspread -import random -import gc +import pymongo +import time @st.cache_resource def init_conn(): - 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" - } + scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] + + credentials = { + "type": "service_account", + "project_id": "model-sheets-connect", + "private_key_id": st.secrets['model_sheets_connect_pk'], + "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", + "client_email": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com", + "client_id": "100369174533302798535", + "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%40model-sheets-connect.iam.gserviceaccount.com" + } + + credentials2 = { + "type": "service_account", + "project_id": "sheets-api-connect-378620", + "private_key_id": st.secrets['sheets_api_connect_pk'], + "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" + } + + uri = st.secrets['mongo_uri'] + client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) + db = client["testing_db"] + + NFL_Data = st.secrets['NFL_Data'] - gc_con = gspread.service_account_from_dict(credentials) - - return gc_con + gc = gspread.service_account_from_dict(credentials) + gc2 = gspread.service_account_from_dict(credentials2) -gcservice_account = init_conn() + return gc, gc2, db, NFL_Data + +gcservice_account, gcservice_account2, db, NFL_Data = init_conn() -freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'} +percentages_format = {'Exposure': '{:.2%}'} +freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'} +dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count'] +fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count'] -@st.cache_resource(ttl = 360) -def init_baselines(): - sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348') - worksheet = sh.worksheet('DK_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) +@st.cache_data(ttl = 599) +def init_DK_seed_frames(): - dk_raw = load_display.dropna(subset=['Median']) + collection = db["DK_MLB_seed_frame"] + cursor = collection.find() + + raw_display = pd.DataFrame(list(cursor)) + raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']] + DK_seed = raw_display.to_numpy() - worksheet = sh.worksheet('FD_ROO') - load_display = pd.DataFrame(worksheet.get_all_records()) - load_display.replace('', np.nan, inplace=True) + return DK_seed + +@st.cache_data(ttl = 599) +def init_FD_seed_frames(): - fd_raw = load_display.dropna(subset=['Median']) + collection = db["FD_MLB_seed_frame"] + cursor = collection.find() + + raw_display = pd.DataFrame(list(cursor)) + raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']] + FD_seed = raw_display.to_numpy() + return FD_seed + +@st.cache_data(ttl = 599) +def init_baselines(): + try: + sh = gcservice_account.open_by_url(NFL_Data) + except: + sh = gcservice_account2.open_by_url(NFL_Data) + worksheet = sh.worksheet('DK_ROO') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) + load_display['STDev'] = load_display['Median'] / 4 + load_display = load_display.drop_duplicates(subset=['Player'], keep='first') - dk_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) + dk_raw = load_display.dropna(subset=['Median']) - worksheet = sh.worksheet('FD_ROO') + worksheet = sh.worksheet('fd_roo') load_display = pd.DataFrame(worksheet.get_all_records()) load_display.replace('', np.nan, inplace=True) - raw_display = load_display.dropna(subset=['Median']) + load_display['STDev'] = load_display['Median'] / 4 + load_display = load_display.drop_duplicates(subset=['Player'], keep='first') - fd_ids = dict(zip(raw_display['Player'], raw_display['player_id'])) - - return dk_raw, fd_raw, dk_ids, fd_ids - -dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines() -t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" + fd_raw = load_display.dropna(subset=['Median']) -static_exposure = pd.DataFrame(columns=['Player', 'count']) -overall_exposure = pd.DataFrame(columns=['Player', 'count']) - -def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port): + return dk_raw, fd_raw + +@st.cache_data +def convert_df(array): + array = pd.DataFrame(array, columns=column_names) + return array.to_csv().encode('utf-8') + +@st.cache_data +def calculate_DK_value_frequencies(np_array): + unique, counts = np.unique(np_array[:, :9], return_counts=True) + frequencies = counts / len(np_array) # Normalize by the number of rows + combined_array = np.column_stack((unique, frequencies)) + return combined_array + +@st.cache_data +def calculate_FD_value_frequencies(np_array): + unique, counts = np.unique(np_array[:, :9], return_counts=True) + frequencies = counts / len(np_array) # Normalize by the number of rows + combined_array = np.column_stack((unique, frequencies)) + return combined_array + +@st.cache_data +def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size): SimVar = 1 Sim_Winners = [] - fp_array = FinalPortfolio.values - - if insert_port == 1: - up_array = CleanPortfolio.values + fp_array = seed_frame[:sharp_split, :] # Pre-vectorize functions vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) - if insert_port == 1: - vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__) - vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__) - st.write('Simulating contest on frames') while SimVar <= Sim_size: - if insert_port == 1: - fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))] - elif insert_port == 0: - fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] + fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] sample_arrays1 = np.c_[ fp_random, np.sum(np.random.normal( - loc=vec_projection_map(fp_random[:, :-5]), - scale=vec_stdev_map(fp_random[:, :-5])), + loc=vec_projection_map(fp_random[:, :-6]), + scale=vec_stdev_map(fp_random[:, :-6])), axis=1) ] - if insert_port == 1: - sample_arrays2 = np.c_[ - up_array, - np.sum(np.random.normal( - loc=vec_up_projection_map(up_array[:, :-5]), - scale=vec_up_stdev_map(up_array[:, :-5])), - axis=1) - ] - sample_arrays = np.vstack((sample_arrays1, sample_arrays2)) - else: - sample_arrays = sample_arrays1 + sample_arrays = sample_arrays1 final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] @@ -126,953 +154,344 @@ def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, in return Sim_Winners -def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth): - RunsVar = 1 - seed_depth_def = seed_depth1 - Strength_var_def = Strength_var - strength_grow_def = strength_grow - Teams_used_def = Teams_used - Total_Runs_def = Total_Runs - - st.write('Creating Seed Frames') - - while RunsVar <= seed_depth_def: - if RunsVar <= 3: - FieldStrength = Strength_var_def - FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0) - maps_dict.update(maps_dict2) - elif RunsVar > 3 and RunsVar <= 4: - FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001)) - FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0) - FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0) - FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) - maps_dict.update(maps_dict3) - maps_dict.update(maps_dict4) - elif RunsVar > 4: - FieldStrength = 1 - FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth) - FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0) - FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0) - FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True) - maps_dict.update(maps_dict5) - maps_dict.update(maps_dict6) - RunsVar += 1 - - return FinalPortfolio_export, maps_dict - -def create_stack_options(player_data, wr_var): - merged_frame = pd.DataFrame(columns = ['QB', 'Player']) - data_raw = player_data.sort_values(by='Median', ascending=False) - - for team in data_raw['Team'].unique(): - data_split = data_raw.loc[data_raw['Team'] == team] - qb_frame = data_split.loc[data_split['Position'] == 'QB'].reset_index() - wr_frame = data_split.loc[data_split['Position'] == 'WR'].iloc[wr_var-1:wr_var] - wr_frame['QB'] = qb_frame['Player'][0] - merge_slice = wr_frame[['QB', 'Player']] - merged_frame = pd.concat([merged_frame, merge_slice]) - merged_frame = merged_frame.reset_index() - correl_dict = dict(zip(merged_frame.QB, merged_frame.Player)) - - return correl_dict - -def create_overall_dfs(pos_players, table_name, dict_name, pos): - if pos == "FLEX": - pos_players = pos_players.sort_values(by='Value', ascending=False) - table_name_raw = pos_players.reset_index(drop=True) - overall_table_name = table_name_raw.head(round(len(table_name_raw))) - overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) - overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() - elif pos != "FLEX": - table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True) - overall_table_name = table_name_raw.head(round(len(table_name_raw))) - overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name))) - overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict() - - return overall_table_name, overall_dict_name - - -def get_overall_merged_df(): - ref_dict = { - 'pos':['RB', 'WR', 'TE', 'FLEX'], - 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], - 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] - } - - for i in range(0,4): - ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\ - create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i]) - - df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True) - - return ref_dict - -def calculate_range_var(count, min_val, FieldStrength, field_growth): - var = round(len(count[0]) * FieldStrength) - var = max(var, min_val) - var += round(field_growth) - - return min(var, len(count[0])) - -def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth): - - full_pos_player_dict = get_overall_merged_df() - qb_baselines = raw_baselines[raw_baselines['Position'] == 'QB'] - qb_baselines = qb_baselines.drop_duplicates(subset='Team') - max_var = len(qb_baselines[qb_baselines['Position'] == 'QB']) - - field_growth_rounded = round(field_growth) - ranges_dict = {} - - # Calculate ranges - for df, dict_val, min_val, key in zip(ref_dict['pos_dfs'], ref_dict['pos_dicts'], [10, 20, 10, 30], ['RB', 'WR', 'TE', 'FLEX']): - count = create_overall_dfs(pos_players, df, dict_val, key) - ranges_dict[f"{key.lower()}_range"] = calculate_range_var(count, min_val, FieldStrength, field_growth_rounded) - if max_var <= 10: - ranges_dict['qb_range'] = round(max_var) - ranges_dict['dst_range'] = round(max_var) - elif max_var > 10 and max_var <= 16: - ranges_dict['qb_range'] = round(max_var / 1.25) - ranges_dict['dst_range'] = round(max_var) - elif max_var > 16: - ranges_dict['qb_range'] = round(max_var / 1.5) - ranges_dict['dst_range'] = round(max_var) - - # Generate random portfolios - rng = np.random.default_rng() - total_elements = [1, 2, 3, 1, 1, 1] - keys = ['qb', 'rb', 'wr', 'te', 'flex', 'dst'] - - all_choices = [rng.choice(ranges_dict[f"{key}_range"], size=(Total_Sample_Size, elem)) for key, elem in zip(keys, total_elements)] - RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) - RandomPortfolio['User/Field'] = 0 - - return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict - -def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): - - sizesplit = round(Total_Sample_Size * sharp_split) - - RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) - stack_num = random.randint(1, 3) - stacking_dict = create_stack_options(raw_baselines, stack_num) - - RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]") - RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['QB'].map(stacking_dict)), dtype="string[pyarrow]") - RandomPortfolio['WR2'] = pd.Series(list(RandomPortfolio['WR2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR3'] = pd.Series(list(RandomPortfolio['WR3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['TE'] = pd.Series(list(RandomPortfolio['TE'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['FLEX'] = pd.Series(list(RandomPortfolio['FLEX'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['DST'] = pd.Series(list(RandomPortfolio['DST'].map(def_dict)), dtype="string[pyarrow]") - RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() - RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) - RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ - reset_index(drop=True) - - RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB1s'] = RandomPortfolio['RB1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB2s'] = RandomPortfolio['RB2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR1s'] = RandomPortfolio['WR1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR2s'] = RandomPortfolio['WR2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR3s'] = RandomPortfolio['WR3'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['TEs'] = RandomPortfolio['TE'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['FLEXs'] = RandomPortfolio['FLEX'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['DSTs'] = RandomPortfolio['DST'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['QBp'] = RandomPortfolio['QB'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB1p'] = RandomPortfolio['RB1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB2p'] = RandomPortfolio['RB2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR1p'] = RandomPortfolio['WR1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR2p'] = RandomPortfolio['WR2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR3p'] = RandomPortfolio['WR3'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['TEp'] = RandomPortfolio['TE'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['FLEXp'] = RandomPortfolio['FLEX'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['DSTp'] = RandomPortfolio['DST'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['QBo'] = RandomPortfolio['QB'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB1o'] = RandomPortfolio['RB1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB2o'] = RandomPortfolio['RB2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR1o'] = RandomPortfolio['WR1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR2o'] = RandomPortfolio['WR2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR3o'] = RandomPortfolio['WR3'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['TEo'] = RandomPortfolio['TE'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['FLEXo'] = RandomPortfolio['FLEX'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['DSTo'] = RandomPortfolio['DST'].map(maps_dict['Own_map']).astype(np.float16) - - RandomPortArray = RandomPortfolio.to_numpy() - - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] - - RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) - RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['QB'].map(maps_dict['Salary_map']), - CleanPortfolio['RB1'].map(maps_dict['Salary_map']), - CleanPortfolio['RB2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR1'].map(maps_dict['Salary_map']), - CleanPortfolio['WR2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR3'].map(maps_dict['Salary_map']), - CleanPortfolio['TE'].map(maps_dict['Salary_map']), - CleanPortfolio['FLEX'].map(maps_dict['Salary_map']), - CleanPortfolio['DST'].map(maps_dict['Salary_map']) - ]).astype(np.int16) - if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['QB'].map(up_dict['Projection_map']), - CleanPortfolio['RB1'].map(up_dict['Projection_map']), - CleanPortfolio['RB2'].map(up_dict['Projection_map']), - CleanPortfolio['WR1'].map(up_dict['Projection_map']), - CleanPortfolio['WR2'].map(up_dict['Projection_map']), - CleanPortfolio['WR3'].map(up_dict['Projection_map']), - CleanPortfolio['TE'].map(up_dict['Projection_map']), - CleanPortfolio['FLEX'].map(up_dict['Projection_map']), - CleanPortfolio['DST'].map(up_dict['Projection_map']) - ]).astype(np.float16) - if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['QB'].map(maps_dict['Own_map']), - CleanPortfolio['RB1'].map(maps_dict['Own_map']), - CleanPortfolio['RB2'].map(maps_dict['Own_map']), - CleanPortfolio['WR1'].map(maps_dict['Own_map']), - CleanPortfolio['WR2'].map(maps_dict['Own_map']), - CleanPortfolio['WR3'].map(maps_dict['Own_map']), - CleanPortfolio['TE'].map(maps_dict['Own_map']), - CleanPortfolio['FLEX'].map(maps_dict['Own_map']), - CleanPortfolio['DST'].map(maps_dict['Own_map']) - ]).astype(np.float16) - - if site_var1 == 'Draftkings': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - - RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']] - - return RandomPortfolio, maps_dict - -def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth): - - sizesplit = round(Total_Sample_Size * (1-sharp_split)) +DK_seed = init_DK_seed_frames() +FD_seed = init_FD_seed_frames() +dk_raw, fd_raw = init_baselines() - RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth) - - RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]") - RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]") - RandomPortfolio['WR1'] = pd.Series(list(RandomPortfolio['WR1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR2'] = pd.Series(list(RandomPortfolio['WR2'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['WR3'] = pd.Series(list(RandomPortfolio['WR3'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]") - RandomPortfolio['TE'] = pd.Series(list(RandomPortfolio['TE'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]") - RandomPortfolio['FLEX'] = pd.Series(list(RandomPortfolio['FLEX'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]") - RandomPortfolio['DST'] = pd.Series(list(RandomPortfolio['DST'].map(def_dict)), dtype="string[pyarrow]") - RandomPortfolio['plyr_list'] = RandomPortfolio[RandomPortfolio.columns.values.tolist()].values.tolist() - RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x))) - RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).\ - reset_index(drop=True) - - RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB1s'] = RandomPortfolio['RB1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['RB2s'] = RandomPortfolio['RB2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR1s'] = RandomPortfolio['WR1'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR2s'] = RandomPortfolio['WR2'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['WR3s'] = RandomPortfolio['WR3'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['TEs'] = RandomPortfolio['TE'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['FLEXs'] = RandomPortfolio['FLEX'].map(maps_dict['Salary_map']).astype(np.int32) - RandomPortfolio['DSTs'] = RandomPortfolio['DST'].map(maps_dict['Salary_map']).astype(np.int32) - - RandomPortfolio['QBp'] = RandomPortfolio['QB'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB1p'] = RandomPortfolio['RB1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['RB2p'] = RandomPortfolio['RB2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR1p'] = RandomPortfolio['WR1'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR2p'] = RandomPortfolio['WR2'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['WR3p'] = RandomPortfolio['WR3'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['TEp'] = RandomPortfolio['TE'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['FLEXp'] = RandomPortfolio['FLEX'].map(maps_dict['Projection_map']).astype(np.float16) - RandomPortfolio['DSTp'] = RandomPortfolio['DST'].map(maps_dict['Projection_map']).astype(np.float16) - - RandomPortfolio['QBo'] = RandomPortfolio['QB'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB1o'] = RandomPortfolio['RB1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['RB2o'] = RandomPortfolio['RB2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR1o'] = RandomPortfolio['WR1'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR2o'] = RandomPortfolio['WR2'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['WR3o'] = RandomPortfolio['WR3'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['TEo'] = RandomPortfolio['TE'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['FLEXo'] = RandomPortfolio['FLEX'].map(maps_dict['Own_map']).astype(np.float16) - RandomPortfolio['DSTo'] = RandomPortfolio['DST'].map(maps_dict['Own_map']).astype(np.float16) - - RandomPortArray = RandomPortfolio.to_numpy() - - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,10:19].astype(int))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,19:28].astype(np.double))] - RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,28:37].astype(np.double))] - - RandomPortArrayOut = np.delete(RandomPortArray, np.s_[10:37], axis=1) - RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']) - RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - if insert_port == 1: - CleanPortfolio['Salary'] = sum([CleanPortfolio['QB'].map(maps_dict['Salary_map']), - CleanPortfolio['RB1'].map(maps_dict['Salary_map']), - CleanPortfolio['RB2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR1'].map(maps_dict['Salary_map']), - CleanPortfolio['WR2'].map(maps_dict['Salary_map']), - CleanPortfolio['WR3'].map(maps_dict['Salary_map']), - CleanPortfolio['TE'].map(maps_dict['Salary_map']), - CleanPortfolio['FLEX'].map(maps_dict['Salary_map']), - CleanPortfolio['DST'].map(maps_dict['Salary_map']) - ]).astype(np.int16) - if insert_port == 1: - CleanPortfolio['Projection'] = sum([CleanPortfolio['QB'].map(up_dict['Projection_map']), - CleanPortfolio['RB1'].map(up_dict['Projection_map']), - CleanPortfolio['RB2'].map(up_dict['Projection_map']), - CleanPortfolio['WR1'].map(up_dict['Projection_map']), - CleanPortfolio['WR2'].map(up_dict['Projection_map']), - CleanPortfolio['WR3'].map(up_dict['Projection_map']), - CleanPortfolio['TE'].map(up_dict['Projection_map']), - CleanPortfolio['FLEX'].map(up_dict['Projection_map']), - CleanPortfolio['DST'].map(up_dict['Projection_map']) - ]).astype(np.float16) - if insert_port == 1: - CleanPortfolio['Own'] = sum([CleanPortfolio['QB'].map(maps_dict['Own_map']), - CleanPortfolio['RB1'].map(maps_dict['Own_map']), - CleanPortfolio['RB2'].map(maps_dict['Own_map']), - CleanPortfolio['WR1'].map(maps_dict['Own_map']), - CleanPortfolio['WR2'].map(maps_dict['Own_map']), - CleanPortfolio['WR3'].map(maps_dict['Own_map']), - CleanPortfolio['TE'].map(maps_dict['Own_map']), - CleanPortfolio['FLEX'].map(maps_dict['Own_map']), - CleanPortfolio['DST'].map(maps_dict['Own_map']) - ]).astype(np.float16) - - if site_var1 == 'Draftkings': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - elif site_var1 == 'Fanduel': - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True) - RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True) - - RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False) - - RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']] - - return RandomPortfolio, maps_dict - -tab1, tab2 = st.tabs(['Uploads', 'Contest Sim']) - -with tab1: - with st.container(): - col1, col2 = st.columns([3, 3]) - - with col1: - st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.") - proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader') - - if proj_file is not None: - try: - proj_dataframe = pd.read_csv(proj_file) - proj_dataframe = proj_dataframe.dropna(subset='Median') - proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() - try: - proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) - except: - pass - - except: - proj_dataframe = pd.read_excel(proj_file) - proj_dataframe = proj_dataframe.dropna(subset='Median') - proj_dataframe['Player'] = proj_dataframe['Player'].str.strip() - try: - proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float) - except: - pass - st.table(proj_dataframe.head(10)) - player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary)) - player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median)) - player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own)) - - with col2: - st.info("The Portfolio file must contain only columns in order and explicitly named: 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', and 'DST'. Upload your projections first to avoid an error message.") - portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader') - - if portfolio_file is not None: - try: - portfolio_dataframe = pd.read_csv(portfolio_file) - - except: - portfolio_dataframe = pd.read_excel(portfolio_file) - - try: - try: - portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] - split_portfolio = portfolio_dataframe - split_portfolio[['QB', 'QB_ID']] = split_portfolio.QB.str.split("(", n=1, expand = True) - split_portfolio[['RB1', 'RB1_ID']] = split_portfolio.RB1.str.split("(", n=1, expand = True) - split_portfolio[['RB2', 'RB2_ID']] = split_portfolio.RB2.str.split("(", n=1, expand = True) - split_portfolio[['WR1', 'WR1_ID']] = split_portfolio.WR1.str.split("(", n=1, expand = True) - split_portfolio[['WR2', 'WR2_ID']] = split_portfolio.WR2.str.split("(", n=1, expand = True) - split_portfolio[['WR3', 'WR3_ID']] = split_portfolio.WR3.str.split("(", n=1, expand = True) - split_portfolio[['TE', 'TE_ID']] = split_portfolio.TE.str.split("(", n=1, expand = True) - split_portfolio[['FLEX', 'FLEX_ID']] = split_portfolio.FLEX.str.split("(", n=1, expand = True) - split_portfolio[['DST', 'DST_ID']] = split_portfolio.DST.str.split("(", n=1, expand = True) - - split_portfolio['QB'] = split_portfolio['QB'].str.strip() - split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() - split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() - split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() - split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() - split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() - split_portfolio['TE'] = split_portfolio['TE'].str.strip() - split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() - split_portfolio['DST'] = split_portfolio['DST'].str.strip() - - st.table(split_portfolio.head(10)) - - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) - - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) - - - except: - portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] - - split_portfolio = portfolio_dataframe - split_portfolio[['QB_ID', 'QB']] = split_portfolio.QB.str.split(":", n=1, expand = True) - split_portfolio[['RB1_ID', 'RB1']] = split_portfolio.RB1.str.split(":", n=1, expand = True) - split_portfolio[['RB2_ID', 'RB2']] = split_portfolio.RB2.str.split(":", n=1, expand = True) - split_portfolio[['WR1_ID', 'WR1']] = split_portfolio.WR1.str.split(":", n=1, expand = True) - split_portfolio[['WR2_ID', 'WR2']] = split_portfolio.WR2.str.split(":", n=1, expand = True) - split_portfolio[['WR3_ID', 'WR3']] = split_portfolio.WR3.str.split(":", n=1, expand = True) - split_portfolio[['TE_ID', 'TE']] = split_portfolio.TE.str.split(":", n=1, expand = True) - split_portfolio[['FLEX_ID', 'FLEX']] = split_portfolio.FLEX.str.split(":", n=1, expand = True) - split_portfolio[['DST_ID', 'DST']] = split_portfolio.DST.str.split(":", n=1, expand = True) - - split_portfolio['QB'] = split_portfolio['QB'].str.strip() - split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() - split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() - split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() - split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() - split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() - split_portfolio['TE'] = split_portfolio['TE'].str.strip() - split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() - split_portfolio['DST'] = split_portfolio['DST'].str.strip() - - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) - - st.table(split_portfolio.head(10)) - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) - - except: - split_portfolio = portfolio_dataframe - - split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), - split_portfolio['RB1'].map(player_salary_dict), - split_portfolio['RB2'].map(player_salary_dict), - split_portfolio['WR1'].map(player_salary_dict), - split_portfolio['WR2'].map(player_salary_dict), - split_portfolio['WR3'].map(player_salary_dict), - split_portfolio['TE'].map(player_salary_dict), - split_portfolio['FLEX'].map(player_salary_dict), - split_portfolio['DST'].map(player_salary_dict)]) - - split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), - split_portfolio['RB1'].map(player_proj_dict), - split_portfolio['RB2'].map(player_proj_dict), - split_portfolio['WR1'].map(player_proj_dict), - split_portfolio['WR2'].map(player_proj_dict), - split_portfolio['WR3'].map(player_proj_dict), - split_portfolio['TE'].map(player_proj_dict), - split_portfolio['FLEX'].map(player_proj_dict), - split_portfolio['DST'].map(player_proj_dict)]) - - split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), - split_portfolio['RB1'].map(player_own_dict), - split_portfolio['RB2'].map(player_own_dict), - split_portfolio['WR1'].map(player_own_dict), - split_portfolio['WR2'].map(player_own_dict), - split_portfolio['WR3'].map(player_own_dict), - split_portfolio['TE'].map(player_own_dict), - split_portfolio['FLEX'].map(player_own_dict), - split_portfolio['DST'].map(player_own_dict)]) - - gc.collect() - +tab1, tab2 = st.tabs(['Contest Sims', 'Data Export']) with tab2: col1, col2 = st.columns([1, 7]) with col1: - st.info(t_stamp) 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_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baselines() - t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST" + DK_seed = init_DK_seed_frames() + FD_seed = init_FD_seed_frames() + dk_raw, fd_raw = init_baselines() - slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User')) + slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate')) site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) if site_var1 == 'Draftkings': - if slate_var1 == 'User': - raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] - elif slate_var1 != 'User': - raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var1)] - raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] + raw_baselines = dk_raw + column_names = dk_columns + + 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': - if slate_var1 == 'User': - raw_baselines = proj_dataframe - elif slate_var1 != 'User': - raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)] - raw_baselines = raw_baselines[raw_baselines['version'] == 'overall'] + raw_baselines = fd_raw + column_names = fd_columns + + 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 = [5, 4, 3, 2, 1, 0]) + elif stack_var1 == 'Full Slate': + stack_var2 = [5, 4, 3, 2, 1, 0] + - st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation") - insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'), key='insert_port1') - if insert_port1 == 'Yes': - insert_port = 1 - elif insert_port1 == 'No': - insert_port = 0 - contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large')) + if st.button("Prepare data export", key='data_export'): + data_export = st.session_state.working_seed.copy() + st.download_button( + label="Export optimals set", + data=convert_df(data_export), + file_name='NFL_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[:, 11], team_var2)] + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], 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: + st.session_state.working_seed = DK_seed.copy() + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)] + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], 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[:, 11], team_var2)] + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], 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: + st.session_state.working_seed = FD_seed.copy() + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)] + st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], 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_seed = init_DK_seed_frames() + FD_seed = init_FD_seed_frames() + dk_raw, fd_raw = init_baselines() + sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), 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 elif contest_var1 == 'Medium': Contest_Size = 5000 elif contest_var1 == 'Large': Contest_Size = 10000 - strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very')) + elif contest_var1 == 'Custom': + Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...") + 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 = .33 - Strength_var = .50 - scaling_var = 5 + sharp_split = 500000 + elif strength_var1 == 'Below Average': + sharp_split = 400000 elif strength_var1 == 'Average': - sharp_split = .50 - Strength_var = .25 - scaling_var = 10 + sharp_split = 300000 + elif strength_var1 == 'Above Average': + sharp_split = 200000 elif strength_var1 == 'Very': - sharp_split = .75 - Strength_var = .01 - scaling_var = 15 - - Sort_function = 'Median' - Sim_function = 'Projection' - - if Contest_Size <= 1000: - strength_grow = .01 - elif Contest_Size > 1000 and Contest_Size <= 2500: - strength_grow = .025 - elif Contest_Size > 2500 and Contest_Size <= 5000: - strength_grow = .05 - elif Contest_Size > 5000 and Contest_Size <= 20000: - strength_grow = .075 - elif Contest_Size > 20000: - strength_grow = .1 - - field_growth = 100 * strength_grow + sharp_split = 100000 + with col2: - with st.container(): - if st.button("Simulate Contest"): - with st.container(): - for key in st.session_state.keys(): - del st.session_state[key] - - if slate_var1 == 'User': - initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] - - # Define the calculation to be applied - def calculate_own(position, own, mean_own, factor, max_own=75): - return np.where((position == 'QB') & (own - mean_own >= 0), - own * (factor * (own - mean_own) / 100) + mean_own, - own) - - # Set the factors based on the contest_var1 - factor_qb, factor_other = { - 'Small': (10, 5), - 'Medium': (6, 3), - 'Large': (3, 1.5), - }[contest_var1] - - # Apply the calculation to the DataFrame - initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1) - initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75) - initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum()) - - # Drop unnecessary columns and create the final DataFrame - Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] - - elif slate_var1 != 'User': - # Copy only the necessary columns - initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] - - # Define the calculation to be applied - def calculate_own(position, own, mean_own, factor, max_own=75): - return np.where((position == 'QB') & (own - mean_own >= 0), - own * (factor * (own - mean_own) / 100) + mean_own, - own) - - # Set the factors based on the contest_var1 - factor_qb, factor_other = { - 'Small': (10, 5), - 'Medium': (6, 3), - 'Large': (3, 1.5), - }[contest_var1] - - # Apply the calculation to the DataFrame - initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_qb if row['Position'] == 'QB' else factor_other), axis=1) - initial_proj['Own%'] = initial_proj['Own%'].clip(upper=75) - initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum()) - - # Drop unnecessary columns and create the final DataFrame - Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']] - - if insert_port == 1: - UserPortfolio = portfolio_dataframe[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']] - elif insert_port == 0: - UserPortfolio = pd.DataFrame(columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) - - Overall_Proj.replace('', np.nan, inplace=True) - Overall_Proj = Overall_Proj.dropna(subset=['Median']) - Overall_Proj = Overall_Proj.assign(Value=lambda x: (x.Median / (x.Salary / 1000))) - Overall_Proj['Sort_var'] = (Overall_Proj['Median'].rank(ascending=False) + Overall_Proj['Value'].rank(ascending=False)) / 2 - Overall_Proj = Overall_Proj.sort_values(by='Sort_var', ascending=False) - Overall_Proj['Own'] = np.where((Overall_Proj['Median'] > 0) & (Overall_Proj['Own'] == 0), 1, Overall_Proj['Own']) - Overall_Proj = Overall_Proj.loc[Overall_Proj['Own'] > 0] - - Overall_Proj['Floor'] = np.where(Overall_Proj['Position'] == 'QB', Overall_Proj['Median'] * .5, Overall_Proj['Median'] * .25) - Overall_Proj['Ceiling'] = np.where(Overall_Proj['Position'] == 'WR', Overall_Proj['Median'] + Overall_Proj['Median'], Overall_Proj['Median'] + Overall_Proj['Floor']) - Overall_Proj['STDev'] = Overall_Proj['Median'] / 4 - - Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True) - Teams_used = Teams_used.reset_index() - Teams_used['team_item'] = Teams_used['index'] + 1 - Teams_used = Teams_used.drop(columns=['index']) - Teams_used_dictraw = Teams_used.drop(columns=['team_item']) - - team_list = Teams_used['Team'].to_list() - item_list = Teams_used['team_item'].to_list() - - FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01) - FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size)) - - if FieldStrength < 0: - FieldStrength = Strength_var - field_split = Strength_var - - for checkVar in range(len(team_list)): - Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list) + 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)), + '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'])), + 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), + 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) + } + Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, 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} + 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': + st.session_state.working_seed = DK_seed.copy() + elif sim_site_var1 == 'Fanduel': + st.session_state.working_seed = FD_seed.copy() + maps_dict = { + 'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.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'])), + 'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)), + 'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)) + } + Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, 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} + 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() + 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[:,0:9].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[:,0:9].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']) + 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': + qb_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': + qb_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) + qb_working['Freq'] = qb_working['Freq'].astype(int) + qb_working['Position'] = qb_working['Player'].map(maps_dict['Pos_map']) + qb_working['Salary'] = qb_working['Player'].map(maps_dict['Salary_map']) + qb_working['Proj Own'] = qb_working['Player'].map(maps_dict['Own_map']) / 100 + qb_working['Exposure'] = qb_working['Freq']/(1000) + qb_working['Edge'] = qb_working['Exposure'] - qb_working['Proj Own'] + qb_working['Team'] = qb_working['Player'].map(maps_dict['Team_map']) + st.session_state.qb_freq = qb_working.copy() - qbs_raw = Overall_Proj[Overall_Proj.Position == 'QB'] - qbs_raw.dropna(subset=['Median']).reset_index(drop=True) - qbs_raw = qbs_raw.reset_index(drop=True) - qbs_raw = qbs_raw.sort_values(by=['Median'], ascending=False) + if sim_site_var1 == 'Draftkings': + rbwrte_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:7].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + rbwrte_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:7].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + rbwrte_working['Freq'] = rbwrte_working['Freq'].astype(int) + rbwrte_working['Position'] = rbwrte_working['Player'].map(maps_dict['Pos_map']) + rbwrte_working['Salary'] = rbwrte_working['Player'].map(maps_dict['Salary_map']) + rbwrte_working['Proj Own'] = rbwrte_working['Player'].map(maps_dict['Own_map']) / 100 + rbwrte_working['Exposure'] = rbwrte_working['Freq']/(1000) + rbwrte_working['Edge'] = rbwrte_working['Exposure'] - rbwrte_working['Proj Own'] + rbwrte_working['Team'] = rbwrte_working['Player'].map(maps_dict['Team_map']) + st.session_state.rbwrte_freq = rbwrte_working.copy() - qbs = qbs_raw.head(round(len(qbs_raw))) - qbs = qbs.assign(Var = range(0,len(qbs))) - qb_dict = pd.Series(qbs.Player.values, index=qbs.Var).to_dict() - - defs_raw = Overall_Proj[Overall_Proj.Position.str.contains("D")] - defs_raw.dropna(subset=['Median']).reset_index(drop=True) - defs_raw = defs_raw.reset_index(drop=True) - defs_raw = defs_raw.sort_values(by=['Own', 'Value'], ascending=False) + if sim_site_var1 == 'Draftkings': + rb_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:3].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + rb_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:3].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + rb_working['Freq'] = rb_working['Freq'].astype(int) + rb_working['Position'] = rb_working['Player'].map(maps_dict['Pos_map']) + rb_working['Salary'] = rb_working['Player'].map(maps_dict['Salary_map']) + rb_working['Proj Own'] = rb_working['Player'].map(maps_dict['Own_map']) / 100 + rb_working['Exposure'] = rb_working['Freq']/(1000) + rb_working['Edge'] = rb_working['Exposure'] - rb_working['Proj Own'] + rb_working['Team'] = rb_working['Player'].map(maps_dict['Team_map']) + st.session_state.rb_freq = rb_working.copy() - defs = defs_raw.head(round(len(defs_raw))) - defs = defs.assign(Var = range(0,len(defs))) - def_dict = pd.Series(defs.Player.values, index=defs.Var).to_dict() - - rbs_raw = Overall_Proj[Overall_Proj.Position == 'RB'] - rbs_raw.dropna(subset=['Median']).reset_index(drop=True) - rbs_raw = rbs_raw.reset_index(drop=True) - rbs_raw = rbs_raw.sort_values(by=['Own', 'Value'], ascending=False) - - wrs_raw = Overall_Proj[Overall_Proj.Position == 'WR'] - wrs_raw.dropna(subset=['Median']).reset_index(drop=True) - wrs_raw = wrs_raw.reset_index(drop=True) - wrs_raw = wrs_raw.sort_values(by=['Own', 'Median'], ascending=False) - - tes_raw = Overall_Proj[Overall_Proj.Position == 'TE'] - tes_raw.dropna(subset=['Median']).reset_index(drop=True) - tes_raw = tes_raw.reset_index(drop=True) - tes_raw = tes_raw.sort_values(by=['Own', 'Value'], ascending=False) + if sim_site_var1 == 'Draftkings': + wr_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,3:6].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + wr_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,3:6].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + wr_working['Freq'] = wr_working['Freq'].astype(int) + wr_working['Position'] = wr_working['Player'].map(maps_dict['Pos_map']) + wr_working['Salary'] = wr_working['Player'].map(maps_dict['Salary_map']) + wr_working['Proj Own'] = wr_working['Player'].map(maps_dict['Own_map']) / 100 + wr_working['Exposure'] = wr_working['Freq']/(1000) + wr_working['Edge'] = wr_working['Exposure'] - wr_working['Proj Own'] + wr_working['Team'] = wr_working['Player'].map(maps_dict['Team_map']) + st.session_state.wr_freq = wr_working.copy() - pos_players = pd.concat([rbs_raw, wrs_raw, tes_raw]) - pos_players.dropna(subset=['Median']).reset_index(drop=True) - pos_players = pos_players.reset_index(drop=True) + if sim_site_var1 == 'Draftkings': + te_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,6:7].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + elif sim_site_var1 == 'Fanduel': + te_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,6:7].values, return_counts=True)), + columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) + te_working['Freq'] = te_working['Freq'].astype(int) + te_working['Position'] = te_working['Player'].map(maps_dict['Pos_map']) + te_working['Salary'] = te_working['Player'].map(maps_dict['Salary_map']) + te_working['Proj Own'] = te_working['Player'].map(maps_dict['Own_map']) / 100 + te_working['Exposure'] = te_working['Freq']/(1000) + te_working['Edge'] = te_working['Exposure'] - te_working['Proj Own'] + te_working['Team'] = te_working['Player'].map(maps_dict['Team_map']) + st.session_state.te_freq = te_working.copy() - if insert_port == 1: - try: - # Initialize an empty DataFrame for Raw Portfolio - Raw_Portfolio = pd.DataFrame() - - # Loop through each position and split the data accordingly - positions = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] - for pos in positions: - temp_df = UserPortfolio[pos].str.split("(", n=1, expand=True) - temp_df.columns = [pos, 'Drop'] - Raw_Portfolio = pd.concat([Raw_Portfolio, temp_df], axis=1) - - # Select only necessary columns and strip white spaces - CleanPortfolio = Raw_Portfolio[positions].apply(lambda x: x.str.strip()) - CleanPortfolio.reset_index(inplace=True) - CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 - CleanPortfolio.drop(columns=['index'], inplace=True) - - CleanPortfolio.replace('', np.nan, inplace=True) - CleanPortfolio.dropna(subset=['QB'], inplace=True) - - # Create frequency table for players - cleaport_players = pd.DataFrame( - np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)), - columns=['Player', 'Freq'] - ).sort_values('Freq', ascending=False).reset_index(drop=True) - cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) - - # Merge and update nerf_frame - nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') - for col in ['Median', 'Floor', 'Ceiling', 'STDev']: - nerf_frame[col] *= 0.90 - except: - CleanPortfolio = UserPortfolio.reset_index() - CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1 - CleanPortfolio.drop(columns=['index'], inplace=True) - - # Replace empty strings and drop rows with NaN in 'QB' column - CleanPortfolio.replace('', np.nan, inplace=True) - CleanPortfolio.dropna(subset=['QB'], inplace=True) - - # Create frequency table for players - cleaport_players = pd.DataFrame( - np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)), - columns=['Player', 'Freq'] - ).sort_values('Freq', ascending=False).reset_index(drop=True) - cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) - - # Merge and update nerf_frame - nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left') - for col in ['Median', 'Floor', 'Ceiling', 'STDev']: - nerf_frame[col] *= 0.90 - - elif insert_port == 0: - CleanPortfolio = UserPortfolio - cleaport_players = pd.DataFrame(np.column_stack(np.unique(CleanPortfolio.iloc[:,0:9].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - cleaport_players['Freq'] = cleaport_players['Freq'].astype(int) - nerf_frame = Overall_Proj - - ref_dict = { - 'pos':['RB', 'WR', 'TE', 'FLEX'], - 'pos_dfs':['RB_Table', 'WR_Table', 'TE_Table', 'FLEX_Table'], - 'pos_dicts':['rb_dict', 'wr_dict', 'te_dict', 'flex_dict'] - } + if sim_site_var1 == 'Draftkings': + flex_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) + elif sim_site_var1 == 'Fanduel': + flex_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) + flex_working['Freq'] = flex_working['Freq'].astype(int) + flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map']) + 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['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() - maps_dict = { - 'Floor_map':dict(zip(Overall_Proj.Player,Overall_Proj.Floor)), - 'Projection_map':dict(zip(Overall_Proj.Player,Overall_Proj.Median)), - 'Ceiling_map':dict(zip(Overall_Proj.Player,Overall_Proj.Ceiling)), - 'Salary_map':dict(zip(Overall_Proj.Player,Overall_Proj.Salary)), - 'Pos_map':dict(zip(Overall_Proj.Player,Overall_Proj.Position)), - 'Own_map':dict(zip(Overall_Proj.Player,Overall_Proj.Own)), - 'Team_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)), - 'STDev_map':dict(zip(Overall_Proj.Player,Overall_Proj.STDev)), - 'team_check_map':dict(zip(Overall_Proj.Player,Overall_Proj.Team)) - } - - up_dict = { - 'Floor_map':dict(zip(cleaport_players.Player,nerf_frame.Floor)), - 'Projection_map':dict(zip(cleaport_players.Player,nerf_frame.Median)), - 'Ceiling_map':dict(zip(cleaport_players.Player,nerf_frame.Ceiling)), - 'Salary_map':dict(zip(cleaport_players.Player,nerf_frame.Salary)), - 'Pos_map':dict(zip(cleaport_players.Player,nerf_frame.Position)), - 'Own_map':dict(zip(cleaport_players.Player,nerf_frame.Own)), - 'Team_map':dict(zip(cleaport_players.Player,nerf_frame.Team)), - 'STDev_map':dict(zip(cleaport_players.Player,nerf_frame.STDev)), - 'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team)) - } - - FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth) - - Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port) - - # Initial setup - Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy']) - Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2 - Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].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, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32} - Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict) - - del FinalPortfolio, insert_port, 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() - - # Conditional Replacement - columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] - - if site_var1 == 'Draftkings': - replace_dict = dkid_dict - elif site_var1 == 'Fanduel': - replace_dict = fdid_dict - - for col in columns_to_replace: - st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True) - - del replace_dict, Sim_Winner_Frame, Sim_Winners - - st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:9].values, return_counts=True)), + if sim_site_var1 == 'Draftkings': + dst_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) - st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int) - st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(2500) - st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own'] - st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list) - - st.session_state.qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:1].values, return_counts=True)), + elif sim_site_var1 == 'Fanduel': + dst_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) - st.session_state.qb_freq['Freq'] = st.session_state.qb_freq['Freq'].astype(int) - st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(2500) - st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own'] - st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Team'].replace(item_list, team_list) - - st.session_state.rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[1, 2]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.rb_freq['Freq'] = st.session_state.rb_freq['Freq'].astype(int) - st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/2500 - st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own'] - st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Team'].replace(item_list, team_list) - - st.session_state.wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[3, 4, 5]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.wr_freq['Freq'] = st.session_state.wr_freq['Freq'].astype(int) - st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/2500 - st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own'] - st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Team'].replace(item_list, team_list) - - st.session_state.te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[6]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.te_freq['Freq'] = st.session_state.te_freq['Freq'].astype(int) - st.session_state.te_freq['Position'] = st.session_state.te_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.te_freq['Salary'] = st.session_state.te_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.te_freq['Proj Own'] = st.session_state.te_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.te_freq['Exposure'] = st.session_state.te_freq['Freq']/2500 - st.session_state.te_freq['Edge'] = st.session_state.te_freq['Exposure'] - st.session_state.te_freq['Proj Own'] - st.session_state.te_freq['Team'] = st.session_state.te_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.te_freq['Team'] = st.session_state.te_freq['Team'].replace(item_list, team_list) - - st.session_state.flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,[7]].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.flex_freq['Freq'] = st.session_state.flex_freq['Freq'].astype(int) - st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/2500 - st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own'] - st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Team'].replace(item_list, team_list) - - st.session_state.dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,8:9].values, return_counts=True)), - columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True) - st.session_state.dst_freq['Freq'] = st.session_state.dst_freq['Freq'].astype(int) - st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map']) - st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map']) - st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100 - st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/2500 - st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own'] - st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map']) - for checkVar in range(len(team_list)): - st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Team'].replace(item_list, team_list) - + dst_working['Freq'] = dst_working['Freq'].astype(int) + dst_working['Position'] = dst_working['Player'].map(maps_dict['Pos_map']) + dst_working['Salary'] = dst_working['Player'].map(maps_dict['Salary_map']) + dst_working['Proj Own'] = dst_working['Player'].map(maps_dict['Own_map']) / 100 + dst_working['Exposure'] = dst_working['Freq']/(1000) + dst_working['Edge'] = dst_working['Exposure'] - dst_working['Proj Own'] + dst_working['Team'] = dst_working['Player'].map(maps_dict['Team_map']) + st.session_state.dst_freq = dst_working.copy() + + if sim_site_var1 == 'Draftkings': + team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,11:12].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[:,11:12].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': @@ -1085,88 +504,113 @@ with tab2: 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').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True) + 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_consim_export.csv', + file_name='MLB_consim_export.csv', mime='text/csv', - ) - + ) + with st.container(): - tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures']) + tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB-WR-TE Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST 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 'qb_freq' in st.session_state: + st.dataframe(st.session_state.qb_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.qb_freq.to_csv().encode('utf-8'), - file_name='qb_freq_export.csv', + file_name='qb_freq.csv', mime='text/csv', + key='sp' ) with tab3: + if 'rbwrte_freq' in st.session_state: + + st.dataframe(st.session_state.rbwrte_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.rbwrte_freq.to_csv().encode('utf-8'), + file_name='rbwrte_freq.csv', + mime='text/csv', + key='team' + ) + with tab4: if 'rb_freq' in st.session_state: - st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + + st.dataframe(st.session_state.rb_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.rb_freq.to_csv().encode('utf-8'), - file_name='rb_freq_export.csv', + file_name='rb_freq.csv', mime='text/csv', + key='stack' ) - with tab4: + with tab5: if 'wr_freq' in st.session_state: - st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + + st.dataframe(st.session_state.wr_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.wr_freq.to_csv().encode('utf-8'), - file_name='wr_freq_export.csv', + file_name='wr_freq.csv', mime='text/csv', + key='stack' ) - with tab5: + with tab6: if 'te_freq' in st.session_state: + st.dataframe(st.session_state.te_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.te_freq.to_csv().encode('utf-8'), - file_name='te_freq_export.csv', + file_name='te_freq.csv', mime='text/csv', + key='sp' ) - with tab6: + with tab7: 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.dataframe(st.session_state.flex_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.flex_freq.to_csv().encode('utf-8'), - file_name='flex_freq_export.csv', + file_name='flex_freq.csv', mime='text/csv', + key='team' ) - with tab7: + with tab8: if 'dst_freq' in st.session_state: - st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True) + + st.dataframe(st.session_state.dst_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.dst_freq.to_csv().encode('utf-8'), - file_name='dst_freq_export.csv', + file_name='dst_freq.csv', mime='text/csv', + key='stack' ) - -del gcservice_account -del dk_roo_raw, fd_roo_raw -del t_stamp -del dkid_dict, fdid_dict -del static_exposure, overall_exposure -del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth -del raw_baselines -del freq_format - -gc.collect() \ No newline at end of file + with tab9: + 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' + ) \ No newline at end of file