Refactor database queries to remove unnecessary 'Team_count' filtering, ensuring valid lineup retrieval across multiple sports

database_queries.py

@@ -246,18 +246,18 @@ def init_DK_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'$gte': ['Team_count': 1]}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -416,18 +416,18 @@ def init_FD_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -647,18 +647,18 @@ def init_DK_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -812,18 +812,18 @@ def init_FD_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1041,18 +1041,18 @@ def init_DK_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1206,18 +1206,18 @@ def init_FD_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}, {'Team_count': {'$gte': 1}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])