class_docstr: 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
  USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-date-time-full 08/01/1999 13:30
class_docstr_tok: ["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","USD-US","Employees","on","Nonfarm","Payrolls","Total","MoM","Net","Change","SA.release-date-time-full","08\/01\/1999","13:30"]
code: def get_economic_event_vol_over_event_day(
      self, vol_in, name, event, start, end, realised=False
  ):
      return self.get_economic_event_ret_over_custom_event_day(
          vol_in, name, event, start, end, lagged=realised
      )
  
imports: import math
  import numpy
  from pandas.tseries.offsets import CustomBusinessDay
  from findatapy.timeseries import Calculations, Timezone, Filter, Calendar
  import datetime
  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
  import pandas
  from findatapy.market import MarketDataGenerator, MarketDataRequest
  from findatapy.util import DataConstants, LoggerManager
label_desc: Use image node_id 14 for calling the EventsFactory obj's underlying member method code with example usage: obj.get_economic_event_vol_over_event_day(vol_in, name, event, start, end, realised) and returns: self