Update app.py

app.py

@@ -51,8 +51,8 @@ gcservice_account, gcservice_account2, db, NFL_Data = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
-dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']
-fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']
+dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 @st.cache_data(ttl = 599)
 def init_DK_seed_frames():  
@@ -61,7 +61,7 @@ def init_DK_seed_frames():
         cursor = collection.find()
     
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']]
+        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         DK_seed = raw_display.to_numpy()
 
         return DK_seed
@@ -73,7 +73,7 @@ def init_FD_seed_frames():
         cursor = collection.find()
     
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count']]
+        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         FD_seed = raw_display.to_numpy()
 
         return FD_seed
@@ -140,8 +140,8 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         sample_arrays1 = np.c_[
             fp_random, 
             np.sum(np.random.normal(
-                loc=vec_projection_map(fp_random[:, :-6]),
-                scale=vec_stdev_map(fp_random[:, :-6])),
+                loc=vec_projection_map(fp_random[:, :-7]),
+                scale=vec_stdev_map(fp_random[:, :-7])),
             axis=1)
         ]
 
@@ -306,7 +306,7 @@ with tab1:
                 Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
                 
                 # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
                 Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
                 
                 # Sorting
@@ -344,7 +344,7 @@ with tab1:
                 Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
                 
                 # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
                 Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
                 
                 # Sorting