diff --git "a/database_queries.py" "b/database_queries.py" --- "a/database_queries.py" +++ "b/database_queries.py" @@ -102,7 +102,7 @@ def init_nfl_baselines(type_var: str, site_var: str, slate_var: str): return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map -def init_DK_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translation, lineup_num, player_var2): +def init_DK_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -129,18 +129,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -170,18 +170,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -211,18 +211,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -270,7 +270,7 @@ def init_DK_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translat return DK_seed -def init_FD_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translation, lineup_num, player_var2): +def init_FD_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -298,18 +298,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -339,18 +339,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -380,18 +380,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -506,7 +506,7 @@ def init_nba_baselines(type_var: str, site_var: str, slate_var: str): return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map -def init_DK_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translation, lineup_num, player_var2): +def init_DK_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -532,18 +532,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']) @@ -572,18 +572,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']) @@ -612,18 +612,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -669,7 +669,7 @@ def init_DK_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translat return DK_seed -def init_FD_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translation, lineup_num, player_var2): +def init_FD_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -696,18 +696,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']) @@ -736,18 +736,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']) @@ -776,18 +776,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -900,7 +900,7 @@ def init_nhl_baselines(type_var: str, site_var: str, slate_var: str): return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map -def init_DK_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translation, lineup_num, player_var2): +def init_DK_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -926,18 +926,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']) @@ -966,18 +966,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']) @@ -1006,18 +1006,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -1063,7 +1063,7 @@ def init_DK_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translat return DK_seed -def init_FD_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translation, lineup_num, player_var2): +def init_FD_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translation, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -1090,18 +1090,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G']) @@ -1130,18 +1130,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G']) @@ -1170,18 +1170,18 @@ 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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G']) @@ -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).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -1296,7 +1296,7 @@ def init_mma_baselines(type_var: str, site_var: str, slate_var: str): return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map -def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, player_var2): +def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -1322,18 +1322,18 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1362,18 +1362,18 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1402,18 +1402,18 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1437,18 +1437,18 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -1459,7 +1459,7 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla return DK_seed -def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, player_var2): +def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -1486,18 +1486,18 @@ def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1526,18 +1526,18 @@ def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1566,18 +1566,18 @@ def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1602,18 +1602,18 @@ def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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']) @@ -1692,7 +1692,7 @@ def init_pga_baselines(type_var: str, site_var: str, slate_var: str): return dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map -def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, player_var2): +def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -1718,18 +1718,18 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1758,18 +1758,18 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1798,18 +1798,18 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1838,18 +1838,18 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1863,7 +1863,7 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla return DK_seed -def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, player_var2): +def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, salary_min, salary_max, player_var2): if prio_var == 'Mix': prio_var = None @@ -1889,18 +1889,18 @@ def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1929,18 +1929,18 @@ def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -1969,18 +1969,18 @@ def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6']) @@ -2009,18 +2009,18 @@ def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, pla # Combine all player conditions with $or if query_conditions: filter_query = {'$or': query_conditions} - cursor1 = collection.find(filter_query).limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find(filter_query).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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().limit(math.ceil(lineup_num * (prio_mix / 100))) - cursor2 = collection.find().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().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=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'])