def run_pipeline():
pipeline_in = util.get_fd_pipe_in_fname()
pipeline_out = util.get_fd_pipe_out_fname()
if os.path.exists(pipeline_out):
f = open(pipeline_out, 'r')
data = json.loads(f.read())
f.close()
return data
player_mapping = {}
dataframe = pandas.read_csv(pipeline_in)
nrows = len(dataframe)
for c in range(nrows):
curr_player = ' '.join([dataframe['First Name'][c], dataframe['Last Name'][c]])
fanduel_data = {}
fanduel_data['salary'] = dataframe['Salary'][c]
fanduel_data['FPPG'] = dataframe['FPPG'][c]
player_mapping[curr_player] = fanduel_data
data = json.dumps(player_mapping, sort_keys=True, indent=4)
f = open(pipeline_out, 'w+')
f.write(data)
f.close()
return data
import daily_lineup_pipeline
from util import get_curr_date, Player, Team, Game, parse_team_depth, parse_games, get_curr_time, load_config
import util
from query_engine import PlayerQueryEngine
import json
import datetime
import os
import datetime
"""
GLOBAL DATA LOADING
"""
fanduel_data_path = util.get_fd_pipe_out_fname()
fanduel_data = json.loads(open(fanduel_data_path, 'r').read())
player_query_engine = PlayerQueryEngine()
def player_stats_perspective(player):
name = player.get_name()
player.set_property('last_5_games', player_query_engine.get_last_n_stats(name, n=5))
if len(player.get_property('last_5_games')) > 0:
player.set_property('last_5_games_avg', player_query_engine.get_last_n_averaged_stats(name, n=5))
player.set_property('last_5_games_std', player_query_engine.get_last_n_stats_std(name, n=5))
# @TODO: Instead of repeating query, just make this query first and choose bottom half of the data
# and compute the average and std manually that way
player.set_property('last_10_games', player_query_engine.get_last_n_stats(name, n=10))
if player.get_property('last_10_games') and len(player.get_property('last_10_games')) > 0:
player.set_property('last_10_games_avg', player_query_engine.get_last_n_averaged_stats(name, n=10))