Update app.py

app.py

@@ -5,7 +5,7 @@ import pandas as pd
 import gspread
 import pymongo
 
-@st.cache_resource(ttl = 600)
+@st.cache_resource
 def init_conn():
         scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
 
@@ -57,15 +57,17 @@ fd_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salar
 def init_baselines():
     sh = gcservice_account.open_by_url(MLB_Data)
     
-    worksheet = sh.worksheet('DK_Projections')
+    worksheet = sh.worksheet('Main_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
+    load_display['STDev'] = load_display['Median'] / 3
     
     dk_raw = load_display.dropna(subset=['Median'])
     
-    worksheet = sh.worksheet('FD_Projections')
+    worksheet = sh.worksheet('Main_FD_ROO')
     load_display = pd.DataFrame(worksheet.get_all_records())
     load_display.replace('', np.nan, inplace=True)
+    load_display['STDev'] = load_display['Median'] / 3
     
     fd_raw = load_display.dropna(subset=['Median'])
 
@@ -90,6 +92,38 @@ def calculate_FD_value_frequencies(np_array):
     combined_array = np.column_stack((unique, frequencies))  
     return combined_array
 
+@st.cache_data
+def sim_contest(Sim_size, seed_frame, maps_dict):
+    SimVar = 1
+    Sim_Winners = []
+    fp_array = seed_frame
+    
+    # Pre-vectorize functions
+    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    
+    st.write('Simulating contest on frames')
+    
+    while SimVar <= Sim_size:
+        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+            
+        sample_arrays1 = np.c_[
+            fp_random, 
+            np.sum(np.random.normal(
+                loc=vec_projection_map(fp_random[:, :-4]),
+                scale=vec_stdev_map(fp_random[:, :-4])),
+            axis=1)
+        ]
+
+        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
+        
+    return Sim_Winners
+
 dk_raw, fd_raw = init_baselines()
 
 tab1, tab2 = st.tabs(['Data Export', 'Contest Sims'])
@@ -180,7 +214,68 @@ with tab2:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed, FD_seed, dk_raw, fd_raw = init_baselines()
+              dk_raw, fd_raw = init_baselines()
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+        if sim_site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+        elif sim_site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+            
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Massive'))
+        if contest_var1 == 'Small':
+            Contest_Size = 1000
+        elif contest_var1 == 'Medium':
+            Contest_Size = 5000
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
+        elif contest_var1 == 'Massive':
+            Contest_Size = 100000
+        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Not Very', 'Average', 'Very'))
+        if strength_var1 == 'Not Very':
+            sharp_split = [400000,100000] 
+        elif strength_var1 == 'Average':
+            sharp_split = [500000,200000]
+        elif strength_var1 == 'Very':
+            sharp_split = [500000,300000]
+
     
     with col2:
-        st.write("Things will go here")
+        maps_dict = {
+                'Floor_map':dict(zip(raw_baselines.Player,raw_baselines.Floor)),
+                'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                'Ceiling_map':dict(zip(raw_baselines.Player,raw_baselines.Ceiling)),
+                'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                'Own_map':dict(zip(raw_baselines.Player,raw_baselines.Own)),
+                'Small_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Small Field Own%'])),
+                'Large_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Large Field Own%'])),
+                'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                'team_check_map':dict(zip(raw_baselines.Player,raw_baselines.Team))
+                }
+        Sim_Winners = sim_contest(500, st.session_state.working_seed)
+        
+        st.table(Sim_Winners.head(10))
+                    
+        # # 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()