def __init__(self, year, large_dfs, processors):
self.year = year
# Select current season of all dataframes
self.data = Slicer.slice_all_data(large_dfs, self.year)
self.data.boxscores = Loader.load_boxscores(self.year)
# Create lists of dates, used for indexing and access
self.dates = pd.unique(self.data.boxscores.teams.index.get_level_values('Date').values)
self.shots_dates = pd.unique(self.data.shots.globals.index.get_level_values('Date').values)
# initialize processor classes to use for processing data
self.boxscore_processor, self.shots_processor, self.game_processor = processors
def __init__(self, year, large_dfs, processors):
self.year = year
# Select current season of all dataframes
self.data = Slicer.slice_all_data(large_dfs, self.year)
self.data.boxscores = Loader.load_boxscores(self.year)
# Create lists of dates, used for indexing and access
self.dates = pd.unique(
self.data.boxscores.teams.index.get_level_values('Date').values)
self.shots_dates = pd.unique(
self.data.shots.globals.index.get_level_values('Date').values)
# initialize processor classes to use for processing data
self.boxscore_processor, self.shots_processor, self.game_processor = processors
def process_jobs(self):
# get total number of jobs
num_jobs = len(self.processor_list)
print 'Number of Jobs: ' + str(num_jobs)
start_time = time.time() # timer function
# load dataframes that span multiple seasons
large_dfs = Loader.load_large_dfs()
# iterate through jobs
for processors, params, num in zip(self.processor_list, self.param_list, range(1, num_jobs + 1)):
print 'Starting Job #' + str(num) + ': ' + str(params['path'])
# perform job with specified params and processor objects
self.perform_job(large_dfs, params['min_year'], params['max_year'], processors, params['path'], params['output_format'])
print "ALL JOBS FINISHED"
print("Total time: --- %s seconds ---" % (time.time() - start_time))