How to use the finmarketpy.economics.eventstudy.EventStudy function in finmarketpy

from datetime import timedelta

import numpy

from finmarketpy.util.marketconstants import MarketConstants
from findatapy.market import IOEngine
from findatapy.util import ConfigManager
from findatapy.market import SpeedCache

try:
    from numbapro import autojit
except: pass

marketconstants = MarketConstants()

class EventsFactory(EventStudy):
    """Provides methods to fetch data on economic data events and to perform basic event studies for market data around
    these events. Note, requires a file of input of the following (transposed as columns!) - we give an example for
    NFP released on 7 Feb 2003 (note, that release-date-time-full, need not be fully aligned by row).

    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.Date	                31/01/2003 00:00
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.close	                xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.actual-release	        143
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-median	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-average	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-low	            xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high.1	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.number-observations	xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision	        185
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision-date	20030307
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-dt	            20030207

def __init__(self, df = None):
        super(EventStudy, self).__init__()

        self.config = ConfigManager()
        self.logger = LoggerManager().getLogger(__name__)
        self.filter = Filter()
        self.io_engine = IOEngine()
        self.speed_cache = SpeedCache()

        if df is not None:
            self._econ_data_frame = df
        else:
            self.load_economic_events()

        return