class BorderAnalytics:
def __init__(self, inputFile, outputFilePath):
self.input = inputFile
self.outputFilePath = outputFilePath
self.inputHandler = None
self.outputHandler = None
self.preprocessor = None
self.borderCrossingCompute = None
def inputFile(self, name):
self.inputHandler = InputHandler(name)
return self.inputHandler.parse()
def preprocess(self, inputList):
self.preprocessor = Preprocessor(inputList)
return self.preprocessor.parseDateTime()
def computeTotalCrossing(self):
store = self.inputFile(self.input)
print("Store size:" + str(len(store)))
processedList = self.preprocess(store)
print("List processed:" + str(len(store)))
self.borderCrossingCompute = BorderCrossingComputation(processedList)
print("Starting computation for total crossing")
self.borderCrossingCompute.computeTotalCross()
print("Finished")
return self.borderCrossingCompute.get_total_cross()
def computeAverageCrossing(self):
print("Starting avg cross computation")
self.borderCrossingCompute.calculate_running_avg()
print("Finished")
return self.borderCrossingCompute.get_monthly_avg()
def startAnalysis(self):
totalCrossing = self.computeTotalCrossing()
avgCrossing = self.computeAverageCrossing()
print("Merging results")
output = self.borderCrossingCompute.getTotalCrossAndAverage()
print("Finished")
print("Saving output")
self.saveOutput(output)
def saveOutput(self, output):
self.outputHandler = OutputHandler(self.outputFilePath)
header = ["Border", "Date", "Measure", "Value", "Average"]
status = self.outputHandler.save_to_csv(header, output)
print(status)
class BorderAnalytics:
def __init__(self, inputFile, outputFilePath):
self.input = inputFile
self.outputFilePath = outputFilePath
self.inputHandler = None
self.outputHandler = None
self.preprocessor = None
self.borderCrossingCompute = None
def inputFile(self, name):
"""
This function fetches the input file from
the name given and redirects it to the
InputHandler.py file to get input data
rows in a lists
:param string name: name of the input file
:returns: input file in list
"""
self.inputHandler = InputHandler(name)
return self.inputHandler.parse()
def preprocess(self, inputList):
"""
This function parses date time values into
correct format.
:param inputList: list of input data
:return: parsed list of date time values
"""
self.preprocessor = Preprocessor(inputList)
return self.preprocessor.parseDateTime()
def computeTotalCrossing(self):
"""
This function computes the total crossing for each
defined condition
:return: a sorted array of data with total crossings value
"""
store = self.inputFile(self.input)
print("Store size:" + str(len(store)))
processedList = self.preprocess(store)
print("List processed:" + str(len(store)))
self.borderCrossingCompute = BorderCrossingComputation(processedList)
print("Starting computation for total crossing")
self.borderCrossingCompute.computeTotalCross()
print("Finished")
return self.borderCrossingCompute.get_total_cross()
def computeAverageCrossing(self):
"""
This function computes the average for each category
as required
:return: a dictionary of computed averages
"""
print("Starting avg cross computation")
self.borderCrossingCompute.calculate_running_avg()
print("Finished")
return self.borderCrossingCompute.get_monthly_avg()
def startAnalysis(self):
"""
This function starts the core computation process of
the project
:return: a sorted list of final output
"""
totalCrossing = self.computeTotalCrossing()
avgCrossing = self.computeAverageCrossing()
print("Merging results")
output = self.borderCrossingCompute.getTotalCrossAndAverage()
print("Finished")
print("Saving output")
self.saveOutput(output)
def saveOutput(self, output):
"""
This function saves the output list data structure
into a csv file
:param output: list of output rows
"""
self.outputHandler = OutputHandler(self.outputFilePath)
header = ["Border", "Date", "Measure", "Value", "Average"]
status = self.outputHandler.save_to_csv(header, output)