def doIntradayRevision(startTime: dt.datetime, endTime: dt.datetime,
configDict: dict) -> bool:
"""perform intraday revsion
Args:
startTime (dt.datetime): startTime
endTime (dt.datetime): endTime
configDict (dict): application dictionary
Returns:
bool: returns True if revision successfull else false
"""
conString: str = configDict['con_string_mis_warehouse']
obj_forecastedDemandFetchRepo = ForecastedDemandFetchRepo(conString)
obj_forecastedDemandFetchForRevisionRepo = ForecastedDemandFetchForRevisionRepo(
conString)
obj_revisedForecastInsertionRepo = RevisedDemandForecastInsertionRepo(
conString)
isRevisionSuccessCount = 0
countRevision = 0
# listOfEntity =['WRLDCMP.SCADA1.A0046945','WRLDCMP.SCADA1.A0046948','WRLDCMP.SCADA1.A0046953','WRLDCMP.SCADA1.A0046957','WRLDCMP.SCADA1.A0046962','WRLDCMP.SCADA1.A0046978','WRLDCMP.SCADA1.A0046980','WRLDCMP.SCADA1.A0047000']
listOfEntity = ['WRLDCMP.SCADA1.A0047000']
for entity in listOfEntity:
#fetch last 6 block actual demand
actualDemandDf = fetchDemandDataFromApi(startTime, endTime, entity,
configDict)
#fetch last 6 block forecasted demand
forecastedDemandDf = obj_forecastedDemandFetchRepo.fetchForecastedDemand(
startTime, endTime, entity)
#calculate avg bias error
avgBiasError = calculateAvgBiasError(actualDemandDf,
forecastedDemandDf)
# print(f"avg bias error = {avgBiasError}")
# avgbiasErrorPercentage = avgBiasError*100
if abs(avgBiasError * 100) > 1:
countRevision = countRevision + 1
# do revision in next time blocks from B+3
revisedForecastData: List[
Tuple] = obj_forecastedDemandFetchForRevisionRepo.fetchForecastedDemandForRevision(
startTime, endTime, entity, avgBiasError)
# print(revisedForecastData)
# insert revised forecasted demand in db
isRevisionSuccess = obj_revisedForecastInsertionRepo.insertRevisedDemandForecast(
revisedForecastData)
if isRevisionSuccess:
isRevisionSuccessCount = isRevisionSuccessCount + 1
if isRevisionSuccessCount == countRevision:
return True
else:
return False
def createMinWiseDemand_purityPercent(startDate: dt.datetime,
endDate: dt.datetime,
configDict: dict) -> bool:
""" create minwise demand data and daywise purity percentage of each entity
Args:
startDate (dt.datetime): start date
endDate (dt.datetime): end date
configDict (dict): apllication configuration dictionary
Returns:
bool: return true if insertion is success.
"""
conString: str = configDict['con_string_mis_warehouse']
#creating instance of classes
obj_purityPercentageInsRepo = PurityPercentageInsertionRepo(conString)
obj_minWiseInsertionRepo = MinWiseDemandInsertionRepo(conString)
puritySuccessCount = 0
demandSuccessCount = 0
currDate = startDate
# Iterating through each day and inserting demand and purity percentage
while currDate <= endDate:
demand_purity_dict: Idemand_purity_dict = fetchDemandDataFromApi(
currDate, configDict)
isInsertionSuccessPurity = obj_purityPercentageInsRepo.insertPurityPercentage(
demand_purity_dict['purityPercentage'])
if isInsertionSuccessPurity:
puritySuccessCount = puritySuccessCount + 1
isInsertionSuccessMinWiseDemand = obj_minWiseInsertionRepo.insertMinWiseDemand(
demand_purity_dict['data'])
if isInsertionSuccessMinWiseDemand:
demandSuccessCount = demandSuccessCount + 1
currDate += dt.timedelta(days=1)
numOfDays = (endDate - startDate).days
#checking whether data is inserted for each day or not
if puritySuccessCount == numOfDays + 1 and demandSuccessCount == numOfDays + 1:
return True
else:
return False