Delete streamlit_app.py

streamlit_app.py

@@ -1,1398 +0,0 @@
-import streamlit as st
-st.set_page_config(layout="wide")
-
-for name in dir():
-    if not name.startswith('_'):
-        del globals()[name]
-
-import pulp
-import numpy as np
-import pandas as pd
-import streamlit as st
-import gspread
-import time
-import random
-import scipy.stats
-import os
-
-@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"
-          }
-
-          gc = gspread.service_account_from_dict(credentials)
-          return gc
-
-gc = init_conn()
-
-game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
-              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
-
-player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
-                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
-
-freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
-
-@st.cache_resource(ttl = 3600)
-def set_slate_teams():
-    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
-    worksheet = sh.worksheet('Site_Info')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
-@st.cache_resource(ttl = 600)
-def player_stat_table():
-    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
-    worksheet = sh.worksheet('Player_Projections')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
-@st.cache_resource(ttl = 600)
-def load_dk_player_projections():
-    sh = gc.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)
-    raw_display = load_display.dropna(subset=['Median'])
-    del load_display
-
-    return raw_display
-
-@st.cache_resource(ttl = 600)
-def load_fd_player_projections():
-    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
-    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'])
-    del load_display
-
-    return raw_display
-
-@st.cache_resource(ttl = 3600)
-def set_export_ids():
-    sh = gc.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)
-    raw_display = load_display.dropna(subset=['Median'])
-    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
-    
-    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'])
-    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
-    
-    del load_display
-    del raw_display
-
-    return dk_ids, fd_ids
-
-@st.cache_data
-def convert_df_to_csv(df):
-    return df.to_csv().encode('utf-8')
-
-def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs):
-    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
-    while RunsVar <= seed_depth_def:
-        if RunsVar <= 3:
-            FieldStrength = Strength_var_def
-            RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = RandomPortfolio
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
-            maps_dict.update(maps_dict2)
-            del FinalPortfolio2
-            del 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 * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
-            del FinalPortfolio3
-            del maps_dict3
-            del FinalPortfolio4
-            del maps_dict4
-        elif RunsVar > 4:
-            FieldStrength = 1
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
-            FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
-            FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
-            maps_dict.update(maps_dict3)
-            maps_dict.update(maps_dict4)
-            del FinalPortfolio3
-            del maps_dict3
-            del FinalPortfolio4
-            del maps_dict4
-        RunsVar += 1
-        
-    return FinalPortfolio, 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))
-        
-        del merged_frame
-        del data_raw
-    
-        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()
-        
-        del pos_players
-        del table_name_raw
-    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()
-        
-        del pos_players
-        del table_name_raw
-    
-    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 df_out, 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):
-    
-            O_merge, full_pos_player_dict = get_overall_merged_df()
-            max_var = len(raw_baselines[raw_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, 30, 10], ['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)
-            
-            ranges_dict['qb_range'] = round(max_var / 2)
-            ranges_dict['dst_range'] = round(max_var / 2)
-            # Generate unique ranges
-            # for key, value in ranges_dict.items():
-            #     ranges_dict[f"{key}_Uniques"] = list(range(0, value, 1))
-            
-            # 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
-            
-            del O_merge
-          
-            return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
-
-def get_correlated_portfolio_for_sim(Total_Sample_Size):
-    
-    sizesplit = round(Total_Sample_Size * sharp_split)
-    
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines)
-    stack_num = random.randint(1, 3)
-    stacking_dict = create_stack_options(raw_baselines, stack_num)
-    
-    # # Create a dictionary for mapping positions to their corresponding dictionaries
-    # dict_map = {
-    #     'QB': qb_dict,
-    #     'RB1': full_pos_player_dict['pos_dicts'][0],
-    #     'RB2': full_pos_player_dict['pos_dicts'][0],
-    #     'WR1': full_pos_player_dict['pos_dicts'][1],
-    #     'WR2': full_pos_player_dict['pos_dicts'][1],
-    #     'WR3': full_pos_player_dict['pos_dicts'][1],
-    #     'TE': full_pos_player_dict['pos_dicts'][2],
-    #     'FLEX': full_pos_player_dict['pos_dicts'][3],
-    #     'DST': def_dict
-    # }
-    
-    # # Apply mapping for each position
-    # for pos, mapping in dict_map.items():
-    #     RandomPortfolio[pos] = RandomPortfolio[pos].map(mapping).astype("string[pyarrow]")
-    
-    # # This part appears to be for filtering. Consider if it can be optimized depending on the data characteristics
-    # RandomPortfolio['plyr_list'] = RandomPortfolio.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['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)
-    
-    del sizesplit
-    del full_pos_player_dict
-    del ranges_dict
-    del stack_num
-    del stacking_dict    
-    
-    
-    
-    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()
-    del RandomPortfolio
-    
-    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)
-    del RandomPortArray
-    del RandomPortArrayOut
-    
-    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 - (FieldStrength * 1000)].reset_index(drop=True)
-    elif site_var1 == 'Fanduel':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (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):
-    
-    sizesplit = round(Total_Sample_Size * (1-sharp_split))
-
-    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines)
-          
-    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)
-    
-    del sizesplit
-    del full_pos_player_dict
-    del ranges_dict
-    
-    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()
-    del RandomPortfolio
-    
-    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)
-    del RandomPortArray
-    del RandomPortArrayOut
-    # st.table(RandomPortfolioDF.head(50))
-          
-    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 - (FieldStrength * 1000)].reset_index(drop=True)
-    elif site_var1 == 'Fanduel':
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
-        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= 59500 - (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
-
-player_stats = player_stat_table()
-dk_roo_raw = load_dk_player_projections()
-fd_roo_raw = load_fd_player_projections()
-t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
-site_slates = set_slate_teams()
-dkid_dict, fdid_dict = set_export_ids()
-
-static_exposure = pd.DataFrame(columns=['Player', 'count'])
-overall_exposure = pd.DataFrame(columns=['Player', 'count'])
-
-tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
-
-with tab1:
-    with st.container():
-          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.")
-          col1, col2 = st.columns([3, 3])
-          
-          with col1:
-                    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))
-                              player_team_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Team))
-                              
-          with col2:
-                    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()
-      
-                                    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)])
-                                    
-                                    split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict)
-                                    split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict)
-                                    split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict)
-                                    split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict)
-                                    split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict)
-                                    split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict)
-                                    split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict)
-                                    split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict)
-                                    split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict)
-      
-                                    split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team',
-                                                                        'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']]
-      
-                                    split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1)
-                                    split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1)
-                                    split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1
-                                
-                                
-                                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)])
-                                    
-                                    split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict)
-                                    split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict)
-                                    split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict)
-                                    split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict)
-                                    split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict)
-                                    split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict)
-                                    split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict)
-                                    split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict)
-                                    split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict)
-      
-                                    split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team',
-                                                                        'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']]
-      
-                                    split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1)
-                                    split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1)
-                                    split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1
-                                  
-                            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)])
- 
-                            split_portfolio['QB_team'] = split_portfolio['QB'].map(player_team_dict)
-                            split_portfolio['RB1_team'] = split_portfolio['RB1'].map(player_team_dict)
-                            split_portfolio['RB2_team'] = split_portfolio['RB2'].map(player_team_dict)
-                            split_portfolio['WR1_team'] = split_portfolio['WR1'].map(player_team_dict)
-                            split_portfolio['WR2_team'] = split_portfolio['WR2'].map(player_team_dict)
-                            split_portfolio['WR3_team'] = split_portfolio['WR3'].map(player_team_dict)
-                            split_portfolio['TE_team'] = split_portfolio['TE'].map(player_team_dict)
-                            split_portfolio['FLEX_team'] = split_portfolio['FLEX'].map(player_team_dict)
-                            split_portfolio['DST_team'] = split_portfolio['DST'].map(player_team_dict)
-  
-                            split_portfolio = split_portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Projection', 'Ownership', 'QB_team',
-                                                                'RB1_team', 'RB2_team', 'WR1_team', 'WR2_team', 'WR3_team', 'TE_team', 'FLEX_team', 'DST_team']]
-  
-                            split_portfolio['Main_Stack'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).index[0],axis=1)
-                            split_portfolio['Main_Stack_Size'] = split_portfolio.iloc[:, 15:19].apply(lambda row: row.value_counts().nlargest(2).values[0],axis=1)
-                            split_portfolio['Main_Stack_Size'] = split_portfolio['Main_Stack_Size'] - 1
-                              
-                            for player_cols in split_portfolio.iloc[:, :9]:
-                                      static_col_raw = split_portfolio[player_cols].value_counts()
-                                      static_col = static_col_raw.to_frame()
-                                      static_col.reset_index(inplace=True)
-                                      static_col.columns = ['Player', 'count']
-                                      static_exposure = pd.concat([static_exposure, static_col], ignore_index=True)
-                            static_exposure['Exposure'] = static_exposure['count'] / len(split_portfolio)
-                            static_exposure = static_exposure[['Player', 'Exposure']]
-                            
-                            del player_salary_dict
-                            del player_proj_dict
-                            del player_own_dict
-                            del player_team_dict
-                            del static_col_raw
-                            del static_col
-    with st.container():
-          col1, col2 = st.columns([3, 3])
-          
-          if portfolio_file is not None:
-                    with col1:
-                              team_split_var1 = st.radio("Are you wanting to isolate any lineups with specific main stacks?", ('Full Portfolio', 'Specific Stacks'))
-                              if team_split_var1 == 'Specific Stacks':
-                                        team_var1 = st.multiselect('Which main stacks would you like to include in the Portfolio?', options = split_portfolio['Main_Stack'].unique())
-                              elif team_split_var1 == 'Full Portfolio':
-                                        team_var1 = split_portfolio.Main_Stack.values.tolist()
-                    with col2:
-                              player_split_var1 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
-                              if player_split_var1 == 'Specific Players':
-                                        find_var1 = st.multiselect('Which players must be included in the lineups?', options = static_exposure['Player'].unique())
-                              elif player_split_var1 == 'Full Players':
-                                        find_var1 = static_exposure.Player.values.tolist()
-
-                    split_portfolio = split_portfolio[split_portfolio['Main_Stack'].isin(team_var1)]
-                    if player_split_var1 == 'Specific Players':
-                              split_portfolio = split_portfolio[np.equal.outer(split_portfolio.to_numpy(copy=False),  find_var1).any(axis=1).all(axis=1)]
-                    elif player_split_var1 == 'Full Players':
-                              split_portfolio = split_portfolio
-
-                    for player_cols in split_portfolio.iloc[:, :9]:
-                              exposure_col_raw = split_portfolio[player_cols].value_counts()
-                              exposure_col = exposure_col_raw.to_frame()
-                              exposure_col.reset_index(inplace=True)
-                              exposure_col.columns = ['Player', 'count']
-                              overall_exposure = pd.concat([overall_exposure, exposure_col], ignore_index=True)
-                    overall_exposure['Exposure'] = overall_exposure['count'] / len(split_portfolio)
-                    overall_exposure = overall_exposure.groupby('Player').sum()
-                    overall_exposure.reset_index(inplace=True)
-                    overall_exposure = overall_exposure[['Player', 'Exposure']]
-                    overall_exposure = overall_exposure.set_index('Player')
-                    overall_exposure = overall_exposure.sort_values(by='Exposure', ascending=False)
-                    overall_exposure['Exposure'] = overall_exposure['Exposure'].astype(float).map(lambda n: '{:.2%}'.format(n))
-                    
-    with st.container():
-          col1, col2 = st.columns([1, 6])
-          
-          with col1:
-                    if portfolio_file is not None:
-                              st.header('Exposure View')
-                              st.dataframe(overall_exposure)
-
-          with col2:
-                    if portfolio_file is not None:
-                              st.header('Portfolio View')
-                              split_portfolio = split_portfolio.reset_index()
-                              split_portfolio['Lineup'] = split_portfolio['index'] + 1
-                              display_portfolio = split_portfolio[['Lineup', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'Salary', 'Main_Stack', 'Main_Stack_Size', 'Projection', 'Ownership']]
-                              display_portfolio = display_portfolio.set_index('Lineup')
-                              st.dataframe(display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Ownership']).format(precision=2))
-                              del split_portfolio
-                              del exposure_col_raw
-                              del exposure_col
-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()
-              dk_roo_raw = load_dk_player_projections()
-              fd_roo_raw = load_fd_player_projections()
-              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
-              site_slates = set_slate_teams()
-              dkid_dict, fdid_dict = set_export_ids()
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate', 'User'))
-        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']
-        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']
-        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 contest_var1 == 'Small':
-            Contest_Size = 1000
-        elif contest_var1 == 'Medium':
-            Contest_Size = 5000
-        elif contest_var1 == 'Large':
-            Contest_Size = 10000
-        linenum_var1 = 1000
-        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
-        if strength_var1 == 'Not Very':
-            sharp_split = .33
-            Strength_var = .50
-            scaling_var = 5
-        elif strength_var1 == 'Average':
-            sharp_split = .50
-            Strength_var = .25
-            scaling_var = 10
-        elif strength_var1 == 'Very':
-            sharp_split = .75
-            Strength_var = .01
-            scaling_var = 15
-            
-    with col2:
-        if st.button("Simulate Contest"):
-            try:
-                del dst_freq
-                del flex_freq
-                del te_freq
-                del wr_freq
-                del rb_freq
-                del qb_freq
-                del player_freq
-                del Sim_Winner_Export
-                del Sim_Winner_Frame
-            except:
-                pass
-            with st.container():
-                st.write('Contest Simulation Starting')
-                seed_depth1 = 5
-                Total_Runs = 1000000
-                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
-        
-                Sort_function = 'Median'
-                if Sort_function == 'Median':
-                    Sim_function = 'Projection'
-                elif Sort_function == 'Own':
-                    Sim_function = 'Own'
-                
-                if slate_var1 == 'User':
-                    OwnFrame = proj_dataframe
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    del OwnFrame
-                    
-                elif slate_var1 != 'User':
-                    initial_proj = raw_baselines
-                    drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
-                    OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
-                    if contest_var1 == 'Small':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Medium':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (6 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    if contest_var1 == 'Large':
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (3 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
-                        OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (1.5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
-                        OwnFrame['Own%'] = np.where(OwnFrame['Own%'] > 75, 75, OwnFrame['Own%'])
-                        OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
-                    Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
-                    
-                    del initial_proj
-                    del drop_frame
-                    del OwnFrame
-                
-                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'])
-                Teams_used_dict = Teams_used_dictraw.to_dict()
-                
-                del Teams_used_dictraw
-        
-                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))
-                
-                del FieldStrength_raw
-                
-                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)
-        
-                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)
-        
-                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)
-        
-                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)
-        
-                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)
-                
-                del qbs_raw
-                del defs_raw
-                del rbs_raw
-                del wrs_raw
-                del tes_raw
-        
-                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
-                        del Raw_Portfolio
-                    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']
-                    }
-        
-                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))
-                    }
-                
-                del cleaport_players
-                del Overall_Proj
-                del nerf_frame
-                
-                st.write('Seed frame creation')
-                FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
-                
-                Sim_size = linenum_var1
-                SimVar = 1
-                Sim_Winners = []
-                fp_array = FinalPortfolio.values
-                
-                if insert_port == 1:
-                    up_array = CleanPortfolio.values
-                
-                # 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:
-                    try:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio), replace=False)]
-                        
-                        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])),
-                            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
-                
-                        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
-                        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
-                        Sim_Winners.append(best_lineup)
-                        SimVar += 1
-                
-                    except Exception as e:
-                        # Handle exceptions here but avoid using try-except for control flow
-                        print("Exception: ", e)
-
-                        
-                # del smple_arrays
-                # del smple_arrays1
-                # del smple_arrays2
-                # del final_array
-                # del best_lineup
-                st.write('Contest simulation complete')
-                # 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
-                
-                # Type Casting
-                type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
-                
-                # Data Copying
-                Sim_Winner_Export = 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:
-                    Sim_Winner_Export[col].replace(replace_dict, inplace=True)
-
-                
-                player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                player_freq['Freq'] = player_freq['Freq'].astype(int)
-                player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
-                player_freq['Salary'] = player_freq['Player'].map(maps_dict['Salary_map'])
-                player_freq['Proj Own'] = player_freq['Player'].map(maps_dict['Own_map']) / 100
-                player_freq['Exposure'] = player_freq['Freq']/(Sim_size)
-                player_freq['Edge'] = player_freq['Exposure'] - player_freq['Proj Own']
-                player_freq['Team'] = player_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
-
-                player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                qb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                qb_freq['Freq'] = qb_freq['Freq'].astype(int)
-                qb_freq['Position'] = qb_freq['Player'].map(maps_dict['Pos_map'])
-                qb_freq['Salary'] = qb_freq['Player'].map(maps_dict['Salary_map'])
-                qb_freq['Proj Own'] = qb_freq['Player'].map(maps_dict['Own_map']) / 100
-                qb_freq['Exposure'] = qb_freq['Freq']/(Sim_size)
-                qb_freq['Edge'] = qb_freq['Exposure'] - qb_freq['Proj Own']
-                qb_freq['Team'] = qb_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    qb_freq['Team'] = qb_freq['Team'].replace(item_list, team_list)
-
-                qb_freq = qb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-
-                rb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                rb_freq['Freq'] = rb_freq['Freq'].astype(int)
-                rb_freq['Position'] = rb_freq['Player'].map(maps_dict['Pos_map'])
-                rb_freq['Salary'] = rb_freq['Player'].map(maps_dict['Salary_map'])
-                rb_freq['Proj Own'] = rb_freq['Player'].map(maps_dict['Own_map']) / 100
-                rb_freq['Exposure'] = rb_freq['Freq']/Sim_size
-                rb_freq['Edge'] = rb_freq['Exposure'] - rb_freq['Proj Own']
-                rb_freq['Team'] = rb_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    rb_freq['Team'] = rb_freq['Team'].replace(item_list, team_list)
-
-                rb_freq = rb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                wr_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                wr_freq['Freq'] = wr_freq['Freq'].astype(int)
-                wr_freq['Position'] = wr_freq['Player'].map(maps_dict['Pos_map'])
-                wr_freq['Salary'] = wr_freq['Player'].map(maps_dict['Salary_map'])
-                wr_freq['Proj Own'] = wr_freq['Player'].map(maps_dict['Own_map']) / 100
-                wr_freq['Exposure'] = wr_freq['Freq']/Sim_size
-                wr_freq['Edge'] = wr_freq['Exposure'] - wr_freq['Proj Own']
-                wr_freq['Team'] = wr_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    wr_freq['Team'] = wr_freq['Team'].replace(item_list, team_list)
-
-                wr_freq = wr_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                te_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                te_freq['Freq'] = te_freq['Freq'].astype(int)
-                te_freq['Position'] = te_freq['Player'].map(maps_dict['Pos_map'])
-                te_freq['Salary'] = te_freq['Player'].map(maps_dict['Salary_map'])
-                te_freq['Proj Own'] = te_freq['Player'].map(maps_dict['Own_map']) / 100
-                te_freq['Exposure'] = te_freq['Freq']/Sim_size
-                te_freq['Edge'] = te_freq['Exposure'] - te_freq['Proj Own']
-                te_freq['Team'] = te_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    te_freq['Team'] = te_freq['Team'].replace(item_list, team_list)
-
-                te_freq = te_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                flex_freq['Freq'] = flex_freq['Freq'].astype(int)
-                flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
-                flex_freq['Salary'] = flex_freq['Player'].map(maps_dict['Salary_map'])
-                flex_freq['Proj Own'] = flex_freq['Player'].map(maps_dict['Own_map']) / 100
-                flex_freq['Exposure'] = flex_freq['Freq']/Sim_size
-                flex_freq['Edge'] = flex_freq['Exposure'] - flex_freq['Proj Own']
-                flex_freq['Team'] = flex_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
-
-                flex_freq = flex_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-                dst_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
-                                           columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                dst_freq['Freq'] = dst_freq['Freq'].astype(int)
-                dst_freq['Position'] = dst_freq['Player'].map(maps_dict['Pos_map'])
-                dst_freq['Salary'] = dst_freq['Player'].map(maps_dict['Salary_map'])
-                dst_freq['Proj Own'] = dst_freq['Player'].map(maps_dict['Own_map']) / 100
-                dst_freq['Exposure'] = dst_freq['Freq']/Sim_size
-                dst_freq['Edge'] = dst_freq['Exposure'] - dst_freq['Proj Own']
-                dst_freq['Team'] = dst_freq['Player'].map(maps_dict['Team_map'])
-                for checkVar in range(len(team_list)):
-                                    dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
-
-                dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-                
-            with st.container():
-                simulate_container = st.empty()
-                st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-            
-            st.download_button(
-                label="Export Tables",
-                data=convert_df_to_csv(Sim_Winner_Export),
-                file_name='NFL_consim_export.csv',
-                mime='text/csv',
-            )
-            
-            with st.container():
-                freq_container = st.empty()
-                tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
-                with tab1:
-                    st.dataframe(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=convert_df_to_csv(player_freq),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab2:
-                    st.dataframe(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=convert_df_to_csv(qb_freq),
-                        file_name='qb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab3:
-                    st.dataframe(rb_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=convert_df_to_csv(rb_freq),
-                        file_name='rb_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab4:
-                    st.dataframe(wr_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=convert_df_to_csv(wr_freq),
-                        file_name='wr_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab5:
-                    st.dataframe(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=convert_df_to_csv(te_freq),
-                        file_name='te_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab6:
-                    st.dataframe(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=convert_df_to_csv(flex_freq),
-                        file_name='flex_freq_export.csv',
-                        mime='text/csv',
-                    )
-                with tab7:
-                    st.dataframe(dst_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=convert_df_to_csv(dst_freq),
-                        file_name='dst_freq_export.csv',
-                        mime='text/csv',
-                    )