def goto_query(self):
"""
Go to query function
"""
# destory main GUI
self.root.destroy()
# enter query GUI
query.init()
def init(schema, coll, pgConn, host, resourceUtil, workspacePath):
global collections
collections = coll;
ensureIndexes(collections)
files.init(collections, workspacePath)
ckan.init(pgConn, schema)
query.init(collections, host)
vocab.init(schema, collections)
delete.init(collections, workspacePath)
workspace.init(collections, resourceUtil, workspacePath)
mapreduce.init(collections, schema)
push.init(collections)
import configs import query import core_stats import advanced_stats import export_list # Initialize connection to NFLBB query.init() # Process and export qb stats csv qb_list = [] config = configs.qb() core_stats.generate_position_stats(qb_list, config) advanced_stats.calc_plyr(qb_list, config['sample_size'], config['cost_baseline']) export_list.export_to_json(qb_list, 'qb') export_list.export_to_csv(qb_list, '2016_qb.csv') # Process and export rb stats csv rb_list = [] config = configs.rb() core_stats.generate_position_stats(rb_list, config) advanced_stats.calc_plyr(rb_list, config['sample_size'], config['cost_baseline']) export_list.export_to_json(rb_list, 'rb') export_list.export_to_csv(rb_list, '2016_rb.csv') # # Process and export wr stats csv wr_list = [] config = configs.wr() core_stats.generate_position_stats(wr_list, config) advanced_stats.calc_plyr(wr_list, config['sample_size'], config['cost_baseline']) export_list.export_to_json(wr_list, 'wr')
