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import numpy as np
import math
from database import *
from datetime import datetime
# Probably should have done this in a dictionary to start with
wrong_nfl_team_names = ['Denver Broncos', 'Washington Commanders', 'Cincinnati Bengals', 'Arizona Cardinals', 'Los Angeles Rams', 'Pittsburgh Steelers',
'Jacksonville Jaguars', 'New England Patriots', 'Tampa Bay Buccaneers', 'San Francisco 49ers', 'Green Bay Packers', 'New York Jets',
'Indianapolis Colts', 'Miami Dolphins', 'Detroit Lions', 'Las Vegas Raiders', 'Atlanta Falcons', 'Seattle Seahawks', 'Houston Texans',
'New Orleans Saints', 'Carolina Panthers', 'New York Giants', 'Cleveland Browns', 'Tennessee Titans', 'Philadelphia Eagles', 'Dallas Cowboys',
'Kansas City Chiefs', 'Los Angeles Chargers', 'Baltimore Ravens', 'Buffalo Bills', 'Minnesota Vikings', 'Chicago Bears']
right_nfl_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams', 'Steelers', 'Jaguars', 'Patriots', 'Buccaneers', '49ers', 'Packers',
'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']
def init_nfl_baselines(type_var: str, site_var: str, slate_var: str):
if slate_var == 'Main':
slate_var = 'Main Slate'
elif slate_var == 'Secondary':
slate_var = 'Secondary Slate'
elif slate_var == 'Auxiliary':
slate_var = 'Late Slate'
if type_var == 'Classic':
collection = nfl_db["DK_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own','player_id', 'slate', 'version']]
raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
load_display = raw_display[raw_display['Position'] != 'K']
dk_roo_raw = load_display.dropna(subset=['Median'])
dk_roo_raw = dk_roo_raw[dk_roo_raw['version'] == 'overall']
dk_roo_raw = dk_roo_raw[dk_roo_raw['slate'] == slate_var]
dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
collection = nfl_db["FD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own','player_id', 'slate', 'version']]
raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
load_display = raw_display[raw_display['Position'] != 'K']
fd_roo_raw = load_display.dropna(subset=['Median'])
fd_roo_raw = fd_roo_raw[fd_roo_raw['version'] == 'overall']
fd_roo_raw = fd_roo_raw[fd_roo_raw['slate'] == slate_var]
fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
dk_roo_raw = dk_roo_raw.drop(columns=['player_id', 'slate', 'version'])
fd_roo_raw = fd_roo_raw.drop(columns=['player_id', 'slate', 'version'])
dk_roo_raw = dk_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
fd_roo_raw = fd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
dk_sd_roo_raw = None
fd_sd_roo_raw = None
dk_sd_id_map = None
fd_sd_id_map = None
elif type_var == 'Showdown':
collection = nfl_db["DK_SD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate']]
raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
# load_display = raw_display[raw_display['Position'] != 'K']
dk_sd_roo_raw = raw_display.dropna(subset=['Median'])
dk_sd_id_map = dict(zip(dk_sd_roo_raw['Player'], dk_sd_roo_raw['player_id']))
collection = nfl_db["FD_SD_NFL_ROO"]
cursor = collection.find()
raw_display = pd.DataFrame(list(cursor))
raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own', 'player_id', 'slate']]
raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display['Player'])
# load_display = raw_display[raw_display['Position'] != 'K']
fd_sd_roo_raw = raw_display.dropna(subset=['Median'])
fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_id']))
dk_sd_roo_raw = dk_sd_roo_raw.drop(columns=['player_id', 'slate'])
fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_id', 'slate'])
dk_sd_roo_raw = dk_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
fd_sd_roo_raw = fd_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain_ownership'})
dk_roo_raw = None
fd_roo_raw = None
dk_id_map = None
fd_id_map = None
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):
if prio_var == 'Mix':
prio_var = None
if type_var == 'Classic':
if slate_var == 'Main':
collection = nfl_db['DK_NFL_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['DK_NFL_seed_frame_Main Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif slate_var == 'Secondary':
collection = nfl_db['DK_NFL_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['DK_NFL_seed_frame_Secondary Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif slate_var == 'Auxiliary':
collection = nfl_db['DK_NFL_Late_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['DK_NFL_seed_frame_Late Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif type_var == 'Showdown':
collection = nfl_db[nfl_db_translation[slate_var]]
if prio_var == None:
if player_var2 != []:
player_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
for column in ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']:
raw_display[column] = raw_display[column].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display[column])
DK_seed = raw_display.to_numpy()
return DK_seed
def init_FD_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translation, lineup_num, player_var2):
if prio_var == 'Mix':
prio_var = None
if type_var == 'Classic':
if slate_var == 'Main':
collection = nfl_db['FD_NFL_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['FD_NFL_seed_frame_Main Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif slate_var == 'Secondary':
collection = nfl_db['FD_NFL_Secondary_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['FD_NFL_Secondary_seed_frame_Secondary Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif slate_var == 'Auxiliary':
collection = nfl_db['FD_NFL_Late_name_map']
cursor = collection.find()
raw_data = pd.DataFrame(list(cursor))
raw_data['value'] = raw_data['value'].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_data['value'])
names_dict = dict(zip(raw_data['key'], raw_data['value']))
collection = nfl_db['FD_NFL_Late_seed_frame_Late Slate']
if prio_var == None:
if player_var2 != []:
player_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
# Map names
raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
elif type_var == 'Showdown':
collection = nfl_db[nfl_db_translation[slate_var]]
if prio_var == None:
if player_var2 != []:
player_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
query_conditions = []
for player in player_var2:
# Create a condition for each player to check if they appear in any column
player_condition = {'$or': [{col: player} for col in player_columns]}
query_conditions.append(player_condition)
# 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)))
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)))
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)))
raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
else:
cursor = collection.find().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'])
raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']]
for column in ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']:
raw_display[column] = raw_display[column].map(dict(zip(wrong_nfl_team_names, right_nfl_name_teams)), na_action='ignore').fillna(raw_display[column])
FD_seed = raw_display.to_numpy()
return FD_seed |