Create app.py

app.py

@@ -0,0 +1,992 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import random
+import gc
+
+@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_con = gspread.service_account_from_dict(credentials)
+          
+          return gc_con
+
+gcservice_account = init_conn()
+
+freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
+
+@st.cache_resource(ttl = 300)
+def init_baslines():
+    sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260')
+    worksheet = sh.worksheet('DK_Build_Up')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player'}, inplace = True)
+    dk_roo_raw = load_display.dropna(subset=['Median'])
+
+    worksheet = sh.worksheet('FD_Build_Up')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    load_display.rename(columns={"Fantasy": "Median", 'Nickname': 'Player'}, inplace = True)
+    fd_roo_raw = load_display.dropna(subset=['Median'])
+
+    worksheet = sh.worksheet('DK_Salaries')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+    raw_display.rename(columns={"name": "Player"}, inplace = True)
+    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    
+    worksheet = sh.worksheet('FD_Salaries')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+    raw_display.rename(columns={"name": "Player"}, inplace = True)
+    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+
+    return dk_roo_raw, fd_roo_raw, dk_ids, fd_ids
+
+dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baslines()
+t_stamp = f"Last Update: " + "idk man recently probably" + f" CST"
+
+static_exposure = pd.DataFrame(columns=['Player', 'count'])
+overall_exposure = pd.DataFrame(columns=['Player', 'count'])
+    
+def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port):
+    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:
+        if insert_port == 1:
+            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+        elif insert_port == 0:
+            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+            
+        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[:, 9].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners
+
+def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth):
+    RunsVar = 1
+    seed_depth_def = seed_depth1
+    Strength_var_def = Strength_var
+    strength_grow_def = strength_grow
+    Teams_used_def = Teams_used
+    Total_Runs_def = Total_Runs
+    
+    st.write('Creating Seed Frames')
+    
+    while RunsVar <= seed_depth_def:
+        if RunsVar <= 3:
+            FieldStrength = Strength_var_def
+            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio_init = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
+            maps_dict.update(maps_dict2)
+        elif RunsVar > 3 and RunsVar <= 4:
+            FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
+            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio_merge_3 = pd.concat([FinalPortfolio_init, FinalPortfolio3], axis=0)
+            FinalPortfolio_merge_4 = pd.concat([FinalPortfolio_merge_3, FinalPortfolio4], axis=0)
+            FinalPortfolio_step_2 = FinalPortfolio_merge_4.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
+            maps_dict.update(maps_dict3)
+            maps_dict.update(maps_dict4)
+        elif RunsVar > 4:
+            FieldStrength = 1
+            FinalPortfolio5, maps_dict5 = get_correlated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio6, maps_dict6 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .25, sharp_split, field_growth)
+            FinalPortfolio_merge_5 = pd.concat([FinalPortfolio_step_2, FinalPortfolio5], axis=0)
+            FinalPortfolio_merge_6 = pd.concat([FinalPortfolio_merge_5, FinalPortfolio6], axis=0)
+            FinalPortfolio_export = FinalPortfolio_merge_6.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
+            maps_dict.update(maps_dict5)
+            maps_dict.update(maps_dict6)
+        RunsVar += 1
+    
+    return FinalPortfolio_export, maps_dict
+
+def create_overall_dfs(pos_players, table_name, dict_name, pos):
+    if pos == "UTIL":
+        pos_players = pos_players.sort_values(by='Value', ascending=False)
+        table_name_raw = pos_players.reset_index(drop=True)
+        overall_table_name = table_name_raw.head(round(len(table_name_raw)))
+        overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
+        overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
+    elif pos != "UTIL":
+        table_name_raw = pos_players[pos_players['Position'].str.contains(pos)].reset_index(drop=True)
+        overall_table_name = table_name_raw.head(round(len(table_name_raw)))
+        overall_table_name = overall_table_name.assign(Var = range(0,len(overall_table_name)))
+        overall_dict_name = pd.Series(overall_table_name.Player.values, index=overall_table_name.Var).to_dict()
+    
+    return overall_table_name, overall_dict_name
+
+
+def get_overall_merged_df():
+    ref_dict = {
+        'pos':['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'],
+        'pos_dfs':['PG_Table', 'SG_Table', 'SF_Table', 'PF_Table', 'C_Table', 'G_Table', 'F_Table', 'UTIL_Table'],
+        'pos_dicts':['pg_dict', 'sg_dict', 'sf_dict', 'pf_dict', 'c_dict', 'g_dict', 'f_dict', 'util_dict']
+        }
+    
+    for i in range(0,8):
+        ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i] =\
+            create_overall_dfs(pos_players, ref_dict['pos_dfs'][i], ref_dict['pos_dicts'][i], ref_dict['pos'][i])
+        
+    df_out = pd.concat(ref_dict['pos_dfs'], ignore_index=True)
+    
+    return ref_dict
+
+def calculate_range_var(count, min_val, FieldStrength, field_growth):
+    var = round(len(count[0]) * FieldStrength)
+    var = max(var, min_val)
+    var += round(field_growth)
+    
+    return min(var, len(count[0]))
+
+def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
+    
+            full_pos_player_dict = get_overall_merged_df()
+          
+            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'],
+                                                  [20, 15, 15, 20, 20, 30, 30, 50], ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']):
+                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)
+            
+            # Generate random portfolios
+            rng = np.random.default_rng()
+            total_elements = [1, 1, 1, 1, 1, 1, 1, 1]
+            keys = ['pg', 'sg', 'sf', 'pf', 'c', 'g', 'f', 'util']
+            
+            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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'])
+            RandomPortfolio['User/Field'] = 0
+          
+            return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
+
+def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
+    
+    sizesplit = round(Total_Sample_Size * sharp_split)
+    
+    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
+    
+    RandomPortfolio['PG'] = pd.Series(list(RandomPortfolio['PG'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
+    RandomPortfolio['SG'] = pd.Series(list(RandomPortfolio['SG'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
+    RandomPortfolio['SF'] = pd.Series(list(RandomPortfolio['SF'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
+    RandomPortfolio['PF'] = pd.Series(list(RandomPortfolio['PF'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
+    RandomPortfolio['C'] = pd.Series(list(RandomPortfolio['C'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][5])), dtype="string[pyarrow]")
+    RandomPortfolio['F'] = pd.Series(list(RandomPortfolio['F'].map(full_pos_player_dict['pos_dicts'][6])), dtype="string[pyarrow]")
+    RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][7])), 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'] == 9].drop(columns=['plyr_list','plyr_count']).\
+        reset_index(drop=True)
+    
+    RandomPortfolio['PGs'] = RandomPortfolio['PG'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['SGs'] = RandomPortfolio['SG'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['SFs'] = RandomPortfolio['SF'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['PFs'] = RandomPortfolio['PF'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Cs'] = RandomPortfolio['C'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Fs'] = RandomPortfolio['F'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32)
+    
+    RandomPortfolio['PGp'] = RandomPortfolio['PG'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['SGp'] = RandomPortfolio['SG'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['SFp'] = RandomPortfolio['SF'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['PFp'] = RandomPortfolio['PF'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Cp'] = RandomPortfolio['C'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Fp'] = RandomPortfolio['F'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16)
+    
+    RandomPortfolio['PGo'] = RandomPortfolio['PG'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['SGo'] = RandomPortfolio['SG'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['SFo'] = RandomPortfolio['SF'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['PFo'] = RandomPortfolio['PF'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Co'] = RandomPortfolio['C'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Fo'] = RandomPortfolio['F'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16)
+    
+    RandomPortArray = RandomPortfolio.to_numpy()
+    
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,9:17].astype(int))]
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,17:25].astype(np.double))]
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,25:33].astype(np.double))]
+    
+    RandomPortArrayOut = np.delete(RandomPortArray, np.s_[9:33], axis=1)
+    RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own'])
+    RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
+    
+    if insert_port == 1:
+        CleanPortfolio['Salary'] = sum([CleanPortfolio['PG'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['SG'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['SF'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['PF'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['C'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['G'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['F'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['UTIL'].map(maps_dict['Salary_map'])
+                                        ]).astype(np.int16)
+    if insert_port == 1:
+        CleanPortfolio['Projection'] = sum([CleanPortfolio['PG'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['SG'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['SF'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['PF'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['C'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['G'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['F'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['UTIL'].map(maps_dict['Projection_map'])
+                                            ]).astype(np.float16)
+    if insert_port == 1:
+        CleanPortfolio['Own'] = sum([CleanPortfolio['PG'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['SG'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['SF'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['PF'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['C'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['G'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['F'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['UTIL'].map(maps_dict['Own_map'])
+                                     ]).astype(np.float16)
+    
+    if site_var1 == 'Draftkings':
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
+    elif site_var1 == 'Fanduel':
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
+    
+    RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
+    
+    RandomPortfolio = RandomPortfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']]
+    
+    return RandomPortfolio, maps_dict
+          
+def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split, field_growth):
+    
+    sizesplit = round(Total_Sample_Size * sharp_split)
+    
+    RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
+    
+    RandomPortfolio['PG'] = pd.Series(list(RandomPortfolio['PG'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
+    RandomPortfolio['SG'] = pd.Series(list(RandomPortfolio['SG'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
+    RandomPortfolio['SF'] = pd.Series(list(RandomPortfolio['SF'].map(full_pos_player_dict['pos_dicts'][2])), dtype="string[pyarrow]")
+    RandomPortfolio['PF'] = pd.Series(list(RandomPortfolio['PF'].map(full_pos_player_dict['pos_dicts'][3])), dtype="string[pyarrow]")
+    RandomPortfolio['C'] = pd.Series(list(RandomPortfolio['C'].map(full_pos_player_dict['pos_dicts'][4])), dtype="string[pyarrow]")
+    RandomPortfolio['G'] = pd.Series(list(RandomPortfolio['G'].map(full_pos_player_dict['pos_dicts'][5])), dtype="string[pyarrow]")
+    RandomPortfolio['F'] = pd.Series(list(RandomPortfolio['F'].map(full_pos_player_dict['pos_dicts'][6])), dtype="string[pyarrow]")
+    RandomPortfolio['UTIL'] = pd.Series(list(RandomPortfolio['UTIL'].map(full_pos_player_dict['pos_dicts'][7])), 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'] == 9].drop(columns=['plyr_list','plyr_count']).\
+        reset_index(drop=True)
+    
+    RandomPortfolio['PGs'] = RandomPortfolio['PG'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['SGs'] = RandomPortfolio['SG'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['SFs'] = RandomPortfolio['SF'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['PFs'] = RandomPortfolio['PF'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Cs'] = RandomPortfolio['C'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Gs'] = RandomPortfolio['G'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['Fs'] = RandomPortfolio['F'].map(maps_dict['Salary_map']).astype(np.int32)
+    RandomPortfolio['UTILs'] = RandomPortfolio['UTIL'].map(maps_dict['Salary_map']).astype(np.int32)
+    
+    RandomPortfolio['PGp'] = RandomPortfolio['PG'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['SGp'] = RandomPortfolio['SG'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['SFp'] = RandomPortfolio['SF'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['PFp'] = RandomPortfolio['PF'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Cp'] = RandomPortfolio['C'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Gp'] = RandomPortfolio['G'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['Fp'] = RandomPortfolio['F'].map(maps_dict['Projection_map']).astype(np.float16)
+    RandomPortfolio['UTILp'] = RandomPortfolio['UTIL'].map(maps_dict['Projection_map']).astype(np.float16)
+    
+    RandomPortfolio['PGo'] = RandomPortfolio['PG'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['SGo'] = RandomPortfolio['SG'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['SFo'] = RandomPortfolio['SF'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['PFo'] = RandomPortfolio['PF'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Co'] = RandomPortfolio['C'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Go'] = RandomPortfolio['G'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['Fo'] = RandomPortfolio['F'].map(maps_dict['Own_map']).astype(np.float16)
+    RandomPortfolio['UTILo'] = RandomPortfolio['UTIL'].map(maps_dict['Own_map']).astype(np.float16)
+    
+    RandomPortArray = RandomPortfolio.to_numpy()
+    
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,9:17].astype(int))]
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,17:25].astype(np.double))]
+    RandomPortArray = np.c_[RandomPortArray, np.einsum('ij->i',RandomPortArray[:,25:33].astype(np.double))]
+    
+    RandomPortArrayOut = np.delete(RandomPortArray, np.s_[9:33], axis=1)
+    RandomPortfolioDF = pd.DataFrame(RandomPortArrayOut, columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own'])
+    RandomPortfolioDF = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
+    
+    if insert_port == 1:
+        CleanPortfolio['Salary'] = sum([CleanPortfolio['PG'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['SG'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['SF'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['PF'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['C'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['G'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['F'].map(maps_dict['Salary_map']),
+                                        CleanPortfolio['UTIL'].map(maps_dict['Salary_map'])
+                                        ]).astype(np.int16)
+    if insert_port == 1:
+        CleanPortfolio['Projection'] = sum([CleanPortfolio['PG'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['SG'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['SF'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['PF'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['C'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['G'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['F'].map(maps_dict['Projection_map']),
+                                            CleanPortfolio['UTIL'].map(maps_dict['Projection_map'])
+                                            ]).astype(np.float16)
+    if insert_port == 1:
+        CleanPortfolio['Own'] = sum([CleanPortfolio['PG'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['SG'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['SF'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['PF'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['C'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['G'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['F'].map(maps_dict['Own_map']),
+                                     CleanPortfolio['UTIL'].map(maps_dict['Own_map'])
+                                     ]).astype(np.float16)
+    
+    if site_var1 == 'Draftkings':
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 50000].reset_index(drop=True)
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (49500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
+    elif site_var1 == 'Fanduel':
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] <= 60000].reset_index(drop=True)
+        RandomPortfolioDF = RandomPortfolioDF[RandomPortfolioDF['Salary'] >= (59500 - (5000 * (1 - (len(Teams_used) / 32)))) - (FieldStrength * 1000)].reset_index(drop=True)
+    
+    RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
+    
+    RandomPortfolio = RandomPortfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'User/Field', 'Salary', 'Projection', 'Own']]
+    
+    return RandomPortfolio, maps_dict
+
+tab1, tab2 = st.tabs(['Uploads', 'Contest Sim'])
+
+with tab1:
+    with st.container():          
+          col1, col2 = st.columns([3, 3])
+          
+          with col1:
+                    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'. Upload your projections first to avoid an error message.")
+                    proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+
+                    if proj_file is not None:
+                              try:
+                                        proj_dataframe = pd.read_csv(proj_file)
+                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
+                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
+                                        try:
+                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
+                                        except:
+                                            pass
+                                        
+                              except:
+                                        proj_dataframe = pd.read_excel(proj_file)
+                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
+                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
+                                        try:
+                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
+                                        except:
+                                            pass
+                              st.table(proj_dataframe.head(10))
+                              player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
+                              player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
+                              player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
+                              
+          with col2:
+                    st.info("The Portfolio file must contain only columns in order and explicitly named: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. Upload your projections first to avoid an error message.")
+                    portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
+
+                    if portfolio_file is not None:
+                            try:
+                                      portfolio_dataframe = pd.read_csv(portfolio_file)
+                                      
+                            except:
+                                      portfolio_dataframe = pd.read_excel(portfolio_file)
+                              
+                            try:
+                                try:
+                                    portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                                    split_portfolio = portfolio_dataframe
+                                    split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True)
+                                    split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True)
+                                    split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True)
+                                    split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True)
+                                    split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True)
+                                    split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True)
+                                    split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True)
+                                    split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True)
+      
+                                    split_portfolio['PG'] = split_portfolio['PG'].str.strip()
+                                    split_portfolio['SG'] = split_portfolio['SG'].str.strip()
+                                    split_portfolio['SF'] = split_portfolio['SF'].str.strip()
+                                    split_portfolio['PF'] = split_portfolio['PF'].str.strip()
+                                    split_portfolio['C'] = split_portfolio['C'].str.strip()
+                                    split_portfolio['G'] = split_portfolio['G'].str.strip()
+                                    split_portfolio['F'] = split_portfolio['F'].str.strip()
+                                    split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
+                                    
+                                    split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                              split_portfolio['SG'].map(player_salary_dict),
+                                              split_portfolio['SF'].map(player_salary_dict),
+                                              split_portfolio['PF'].map(player_salary_dict),
+                                              split_portfolio['C'].map(player_salary_dict),
+                                              split_portfolio['G'].map(player_salary_dict),
+                                              split_portfolio['F'].map(player_salary_dict),
+                                              split_portfolio['UTIL'].map(player_salary_dict)])
+                                    
+                                    split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                              split_portfolio['SG'].map(player_proj_dict),
+                                              split_portfolio['SF'].map(player_proj_dict),
+                                              split_portfolio['PF'].map(player_proj_dict),
+                                              split_portfolio['C'].map(player_proj_dict),
+                                              split_portfolio['G'].map(player_proj_dict),
+                                              split_portfolio['F'].map(player_proj_dict),
+                                              split_portfolio['UTIL'].map(player_proj_dict)])
+                                    
+                                    split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                              split_portfolio['SG'].map(player_own_dict),
+                                              split_portfolio['SF'].map(player_own_dict),
+                                              split_portfolio['PF'].map(player_own_dict),
+                                              split_portfolio['C'].map(player_own_dict),
+                                              split_portfolio['G'].map(player_own_dict),
+                                              split_portfolio['F'].map(player_own_dict),
+                                              split_portfolio['UTIL'].map(player_own_dict)])
+                                    
+                                    st.table(split_portfolio.head(10))
+                                
+                                
+                                except:
+                                    portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                                    
+                                    split_portfolio = portfolio_dataframe
+                                    split_portfolio[['PG_ID', 'PG']] = split_portfolio.PG.str.split(":", n=1, expand = True)
+                                    split_portfolio[['SG_ID', 'SG']] = split_portfolio.SG.str.split(":", n=1, expand = True)
+                                    split_portfolio[['SF_ID', 'SF']] = split_portfolio.SF.str.split(":", n=1, expand = True)
+                                    split_portfolio[['PF_ID', 'PF']] = split_portfolio.PF.str.split(":", n=1, expand = True)
+                                    split_portfolio[['C_ID', 'C']] = split_portfolio.C.str.split(":", n=1, expand = True)
+                                    split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True)
+                                    split_portfolio[['F_ID', 'F']] = split_portfolio.F.str.split(":", n=1, expand = True)
+                                    split_portfolio[['UTIL_ID', 'UTIL']] = split_portfolio.UTIL.str.split(":", n=1, expand = True)
+                                    
+                                    split_portfolio['PG'] = split_portfolio['PG'].str.strip()
+                                    split_portfolio['SG'] = split_portfolio['SG'].str.strip()
+                                    split_portfolio['SF'] = split_portfolio['SF'].str.strip()
+                                    split_portfolio['PF'] = split_portfolio['PF'].str.strip()
+                                    split_portfolio['C'] = split_portfolio['C'].str.strip()
+                                    split_portfolio['G'] = split_portfolio['G'].str.strip()
+                                    split_portfolio['F'] = split_portfolio['F'].str.strip()
+                                    split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
+      
+                                    split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                              split_portfolio['SG'].map(player_salary_dict),
+                                              split_portfolio['SF'].map(player_salary_dict),
+                                              split_portfolio['PF'].map(player_salary_dict),
+                                              split_portfolio['C'].map(player_salary_dict),
+                                              split_portfolio['G'].map(player_salary_dict),
+                                              split_portfolio['F'].map(player_salary_dict),
+                                              split_portfolio['UTIL'].map(player_salary_dict)])
+                                    
+                                    split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                              split_portfolio['SG'].map(player_proj_dict),
+                                              split_portfolio['SF'].map(player_proj_dict),
+                                              split_portfolio['PF'].map(player_proj_dict),
+                                              split_portfolio['C'].map(player_proj_dict),
+                                              split_portfolio['G'].map(player_proj_dict),
+                                              split_portfolio['F'].map(player_proj_dict),
+                                              split_portfolio['UTIL'].map(player_proj_dict)])
+                                                                    
+                                    
+                                    split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                              split_portfolio['SG'].map(player_own_dict),
+                                              split_portfolio['SF'].map(player_own_dict),
+                                              split_portfolio['PF'].map(player_own_dict),
+                                              split_portfolio['C'].map(player_own_dict),
+                                              split_portfolio['G'].map(player_own_dict),
+                                              split_portfolio['F'].map(player_own_dict),
+                                              split_portfolio['UTIL'].map(player_own_dict)])
+                                    
+                                    st.table(split_portfolio.head(10))
+                                  
+                            except:
+                                 split_portfolio = portfolio_dataframe
+                                
+                                 split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                           split_portfolio['SG'].map(player_salary_dict),
+                                           split_portfolio['SF'].map(player_salary_dict),
+                                           split_portfolio['PF'].map(player_salary_dict),
+                                           split_portfolio['C'].map(player_salary_dict),
+                                           split_portfolio['G'].map(player_salary_dict),
+                                           split_portfolio['F'].map(player_salary_dict),
+                                           split_portfolio['UTIL'].map(player_salary_dict)])
+                                 
+                                 split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                           split_portfolio['SG'].map(player_proj_dict),
+                                           split_portfolio['SF'].map(player_proj_dict),
+                                           split_portfolio['PF'].map(player_proj_dict),
+                                           split_portfolio['C'].map(player_proj_dict),
+                                           split_portfolio['G'].map(player_proj_dict),
+                                           split_portfolio['F'].map(player_proj_dict),
+                                           split_portfolio['UTIL'].map(player_proj_dict)])
+                                                                 
+                                 
+                                 split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                           split_portfolio['SG'].map(player_own_dict),
+                                           split_portfolio['SF'].map(player_own_dict),
+                                           split_portfolio['PF'].map(player_own_dict),
+                                           split_portfolio['C'].map(player_own_dict),
+                                           split_portfolio['G'].map(player_own_dict),
+                                           split_portfolio['F'].map(player_own_dict),
+                                           split_portfolio['UTIL'].map(player_own_dict)])
+                                 
+                            gc.collect() 
+                            
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              dk_roo_raw, fd_roo_raw, dkid_dict, fdid_dict = init_baslines()
+              t_stamp = f"Last Update: " + "idk man recently probably" + f" CST"
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main 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
+        elif site_var1 == 'Fanduel':
+              if slate_var1 == 'User':
+                  raw_baselines = proj_dataframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+              elif slate_var1 != 'User':
+                  raw_baselines = fd_roo_raw
+
+        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 = 500
+        elif contest_var1 == 'Medium':
+            Contest_Size = 2500
+        elif contest_var1 == 'Large':
+            Contest_Size = 5000
+        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
+        
+        Sort_function = 'Median'
+        Sim_function = 'Projection'
+        
+        if Contest_Size <= 1000:
+            strength_grow = .01
+        elif Contest_Size > 1000 and Contest_Size <= 2500:
+            strength_grow = .025
+        elif Contest_Size > 2500 and Contest_Size <= 5000:
+            strength_grow = .05
+        elif Contest_Size > 5000 and Contest_Size <= 20000:
+            strength_grow = .075
+        elif Contest_Size > 20000:
+            strength_grow = .1
+            
+        field_growth = 100 * strength_grow
+
+    with col2:
+        with st.container():
+            if st.button("Simulate Contest"):
+                with st.container():
+                    for key in st.session_state.keys():
+                        del st.session_state[key]
+                    
+                    if slate_var1 == 'User':
+                        initial_proj = proj_dataframe[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                        
+                        # # Define the calculation to be applied
+                        # def calculate_own(position, own, mean_own, factor, max_own=85):
+                        #     return np.where((position == 'C') & (own - mean_own >= 0),
+                        #                     own * (factor * (own - mean_own) / 100) + mean_own,
+                        #                     own)
+                        
+                        # # Set the factors based on the contest_var1
+                        # factor_c, factor_other = {
+                        #     'Small': (10, 5),
+                        #     'Medium': (6, 3),
+                        #     'Large': (3, 1.5),
+                        # }[contest_var1]
+                        
+                        # # Apply the calculation to the DataFrame
+                        # initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_c if row['Position'] == 'C' else factor_other), axis=1)
+                        # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
+                        initial_proj['Own'] = initial_proj['Own%'] * (900 / initial_proj['Own%'].sum())
+                        
+                        # Drop unnecessary columns and create the final DataFrame
+                        Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                        
+                    elif slate_var1 != 'User':
+                        # Copy only the necessary columns
+                        initial_proj = raw_baselines[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                        
+                        # # Define the calculation to be applied
+                        # def calculate_own(position, own, mean_own, factor, max_own=85):
+                        #    return np.where((position == 'C') & (own - mean_own >= 0),
+                        #                    own * (factor * (own - mean_own) / 100) + mean_own,
+                        #                    own)
+                        
+                        # # Set the factors based on the contest_var1
+                        # factor_c, factor_other = {
+                        #     'Small': (10, 5),
+                        #     'Medium': (6, 3),
+                        #     'Large': (3, 1.5),
+                        # }[contest_var1]
+                        
+                        # # Apply the calculation to the DataFrame
+                        # initial_proj['Own%'] = initial_proj.apply(lambda row: calculate_own(row['Position'], row['Own'], initial_proj.loc[initial_proj['Position'] == row['Position'], 'Own'].mean(), factor_c if row['Position'] == 'C' else factor_other), axis=1)
+                        # initial_proj['Own%'] = initial_proj['Own%'].clip(upper=85)
+                        initial_proj['Own'] = initial_proj['Own'] * (900 / initial_proj['Own'].sum())
+                        
+                        # Drop unnecessary columns and create the final DataFrame
+                        Overall_Proj = initial_proj[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
+                    
+                    if insert_port == 1:
+                        UserPortfolio = portfolio_dataframe[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']]
+                    elif insert_port == 0:
+                        UserPortfolio = pd.DataFrame(columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'])
+            
+                    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'] = Overall_Proj['Median'] * .25
+                    Overall_Proj['Ceiling'] = Overall_Proj['Median'] * 1.75
+                    Overall_Proj['STDev'] = Overall_Proj['Median'] / 4
+            
+                    Teams_used = Overall_Proj['Team'].drop_duplicates().reset_index(drop=True)
+                    Teams_used = Teams_used.reset_index()
+                    Teams_used['team_item'] = Teams_used['index'] + 1
+                    Teams_used = Teams_used.drop(columns=['index'])
+                    Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
+            
+                    team_list = Teams_used['Team'].to_list()
+                    item_list = Teams_used['team_item'].to_list()
+            
+                    FieldStrength_raw = Strength_var + ((30 - len(Teams_used)) * .01)
+                    FieldStrength = FieldStrength_raw - (FieldStrength_raw * (20000 / Contest_Size))
+                    
+                    if FieldStrength < 0:
+                        FieldStrength = Strength_var
+                    field_split = Strength_var
+            
+                    for checkVar in range(len(team_list)):
+                                        Overall_Proj['Team'] = Overall_Proj['Team'].replace(team_list, item_list)
+            
+                    pgs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('PG')]
+                    pgs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    pgs_raw = pgs_raw.reset_index(drop=True)
+                    pgs_raw = pgs_raw.sort_values(by=['Median'], ascending=False)
+                    
+                    sgs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('SG')]
+                    sgs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    sgs_raw = sgs_raw.reset_index(drop=True)
+                    sgs_raw = sgs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+                    
+                    sfs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('SF')]
+                    sfs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    sfs_raw = sfs_raw.reset_index(drop=True)
+                    sfs_raw = sfs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+                    
+                    pfs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('PF')]
+                    pfs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    pfs_raw = pfs_raw.reset_index(drop=True)
+                    pfs_raw = pfs_raw.sort_values(by=['Own', 'Median'], ascending=False)
+                    
+                    cs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('C')]
+                    cs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    cs_raw = cs_raw.reset_index(drop=True)
+                    cs_raw = cs_raw.sort_values(by=['Own', 'Median'], ascending=False)
+                    
+                    gs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('G')]
+                    gs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    gs_raw = gs_raw.reset_index(drop=True)
+                    gs_raw = gs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+                    
+                    fs_raw = Overall_Proj[Overall_Proj['Position'].str.contains('F')]
+                    fs_raw.dropna(subset=['Median']).reset_index(drop=True)
+                    fs_raw = fs_raw.reset_index(drop=True)
+                    fs_raw = fs_raw.sort_values(by=['Own', 'Value'], ascending=False)
+            
+                    pos_players = pd.concat([pgs_raw, sgs_raw, sfs_raw, pfs_raw, cs_raw, gs_raw, fs_raw])
+                    pos_players.dropna(subset=['Median']).reset_index(drop=True)
+                    pos_players = pos_players.reset_index(drop=True)
+            
+                    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 = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                            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=['PG'], inplace=True)
+                            
+                            # Create frequency table for players
+                            cleaport_players = pd.DataFrame(
+                                np.column_stack(np.unique(CleanPortfolio.iloc[:, 0:9].values, return_counts=True)),
+                                columns=['Player', 'Freq']
+                            ).sort_values('Freq', ascending=False).reset_index(drop=True)
+                            cleaport_players['Freq'] = cleaport_players['Freq'].astype(int)
+                            
+                            # Merge and update nerf_frame
+                            nerf_frame = pd.merge(cleaport_players, Overall_Proj, on='Player', how='left')
+                            for col in ['Median', 'Floor', 'Ceiling', 'STDev']:
+                                nerf_frame[col] *= 0.90
+                        except:
+                            CleanPortfolio = UserPortfolio.reset_index()
+                            CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
+                            CleanPortfolio.drop(columns=['index'], inplace=True)
+                            
+                            CleanPortfolio.replace('', np.nan, inplace=True)
+                            CleanPortfolio.dropna(subset=['PG'], 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':['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'],
+                        'pos_dfs':['PG_Table', 'SG_Table', 'SF_Table', 'PF_Table', 'C_Table', 'G_Table', 'F_Table', 'UTIL_Table'],
+                        'pos_dicts':['pg_dict', 'sg_dict', 'sf_dict', 'pf_dict', 'c_dict', 'g_dict', 'f_dict', 'util_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))
+                        }
+                    
+                    FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
+                    
+                    Sim_Winners = sim_contest(2500, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
+                    
+                    # Initial setup
+                    Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
+                    Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
+                    Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
+                    Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                    
+                    # Type Casting
+                    type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                    Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                    
+                    del FinalPortfolio, insert_port, type_cast_dict
+                    
+                    # Sorting
+                    st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                    st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                    
+                    # Data Copying
+                    st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                    
+                    # Data Copying
+                    st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                    
+                    # Conditional Replacement
+                    columns_to_replace = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                    
+                    if site_var1 == 'Draftkings':
+                        replace_dict = dkid_dict
+                    elif site_var1 == 'Fanduel':
+                        replace_dict = fdid_dict
+                    
+                    for col in columns_to_replace:
+                        st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
+                    
+                    del replace_dict, Sim_Winner_Frame, Sim_Winners
+     
+                    st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Display.iloc[:,0:8].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
+                    st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
+                    st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map'])
+                    st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100
+                    st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(2500)
+                    st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own']
+                    st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map'])
+                    for checkVar in range(len(team_list)):
+                                        st.session_state.player_freq['Team'] = st.session_state.player_freq['Team'].replace(item_list, team_list)
+                    
+        with st.container():
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Full Frame",
+                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                    file_name='NBA_consim_export.csv',
+                    mime='text/csv',
+                )
+        
+        with st.container():
+            # tab1 = st.tabs(['Overall Exposures'])
+            # with tab1:
+                if 'player_freq' in st.session_state:
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                    )
+
+del gcservice_account
+del dk_roo_raw, fd_roo_raw
+del t_stamp
+del dkid_dict, fdid_dict
+del static_exposure, overall_exposure
+del insert_port1, Contest_Size, sharp_split, Strength_var, scaling_var, Sort_function, Sim_function, strength_grow, field_growth
+del raw_baselines
+del freq_format
+
+gc.collect()