#================================================ This script is a simple example of the Knowledge extraction tool ===============================================================#
#The following is the Main script, that calls two Python objects in order to conduct the three main components of the Knowledge extraction tool
#In the following example the operation times of the topology's two machines are given in an Excel document.
#Import_Excel object imports data from the Excel document to the tool and DistFittest object fits the data to a statistical distribution using Kolmogorov-Smirnov test
workbook=xlrd.open_workbook('inputsKEtool.xls')#Using xlrd library opens the Excel document with the input data
worksheets=workbook.sheet_names()
worksheet_OperationTime=worksheets[0]#It creates a variable that holds the first Excel worksheet
X=Import_Excel()#Call the import_Excel object
OperationTimes=X.Input_data(worksheet_OperationTime,workbook)#It defines a Python dictionary, giving as name OpearationTimes and as value the returned dictionary from the import_Excel object
Machine1_OpearationTimes=OperationTimes.get('Machine1',[])#Two lists are defined (Machine1_OpearationTimes, Machine2_OpearationTimes) with the operation times data of each machine
B.ks_test(Machine2_OpearationTimes)#It conducts the Kolmogorov-Smirnov test in the list with the operation times data
B.ks_test(Machine1_OpearationTimes)
lista=[]#It creates a list, that contains the outcome of the Kolmogorov-Smirnov tests
lista.append(B.ks_test(Machine1_OpearationTimes))
lista.append(B.ks_test(Machine2_OpearationTimes))
names=[]#It defines the following five lists
aParameters=[]
bParameters=[]
aParameterValue=[]
bParameterValue=[]
forindexinrange(len(lista)):
names.append(lista[index].get('type'))#Insert the distribution names from the dictionary into the names list
aParameters.append(lista[index].get('aParameter'))#Insert the name of the first parameter of each distribution from the dictionary into the aParameters list
try:
bParameters.append(lista[index].get('bParameter'))#Insert the name of the second parameter of each distribution from the dictionary (if there are any->use of except) into the bParameters list
except:
bParameters.append('')
aParameterValue.append(lista[index].get('aParameterValue'))#Insert the value of the first parameter of each distribution from the dictionary into the aParameterValue list
try:
bParameterValue.append(lista[index].get('bParameterValue'))#Insert the value of the second parameter of each distribution from the dictionary (if there are any->use of except) into the bParameterValue list
except:
bParameterValue.append('')
#==================================== Output preparation: output the updated values in the CMSD information model of Topology10 ====================================================#
datafile=('CMSD_Topology10.xml')#It defines the name or the directory of the XML file that is manually written the CMSD information model
tree=et.parse(datafile)#This file will be parsed using the XML.ETREE Python library
root=tree.getroot()
process=tree.findall('./DataSection/ProcessPlan/Process')#It creates a new variable and using the 'findall' order in XML.ETREE library, this new variable holds all the processes defined in the XML file
forprocessinprocess:
process_identifier=process.find('Identifier').text#It creates a new variable that holds the text of the Identifier element in the XML file
ifprocess_identifier=='A020':#It checks using if...elif syntax if the process identifier is 'A020', so the process that uses the first machine
OperationTime=process.get('OpeationTime')#It gets the element attribute OpearationTime inside the Process node
Distribution=process.get('./OperationTime/Distribution')#It gets the element attribute Distribution inside the OpearationTime node
Name=process.find('./OperationTime/Distribution/Name')#It finds the subelement Name inside the Distribution attribute
Name.text=str(names[0])#It changes the text between the Name element tags, putting the name of the distribution (e.g. in Normal distribution that will be Normal)
Name.text=str(aParameters[0])#It changes the text between the Name element tags, putting the name of the distribution's first parameter (e.g. in Normal that will be the mean)
Value.text=str(aParameterValue[0])#It changes the text between the Value element tags, putting the value of the distribution's first parameter (e.g. in Normal so for mean value that will be 5.0)
Name.text=str(bParameters[0])#It changes the text between the Name element tags, putting the name of the distribution's second parameter (e.g. in Normal that will be the standarddeviation)
Value.text=str(bParameterValue[0])#It changes the text between the Value element tags, putting the value of the distribution's second parameter (e.g. in Normal so for standarddeviation value that will be 1.3)
elifprocess_identifier=='A040':#It checks using if...elif syntax if the process identifier is 'A040', so the process that uses the second machine
tree.write('CMSD_Topology10_Output.xml',encoding="utf8")#It writes the element tree to a specified file, using the 'utf8' output encoding
#================================= Output preparation: output the updated values in the JSON file of Topology10 =========================================================#
jsonFile=open('JSON_Topology10.json','r')#It opens the Topology10 JSON file
data=json.load(jsonFile)#It loads the file
jsonFile.close()
nodes=data.get('coreObject',[])#It creates a variable that holds the 'coreObject' list
forelementinnodes:
name=element.get('name')#It creates a variable that gets the element attribute 'name'
processingTime=element.get('processingTime',{})#It creates a variable that gets the element attribute 'processingTime'
ifname=='Machine1':#It checks using if...elif syntax if the name is 'Machine1', so the first machine in the Topology10
processingTime['distributionType']=str(names[0])#It changes the attribute's ('distributionType') name, putting the name of the distribution (e.g. in Normal distribution that will be Normal)
processingTime[str(aParameters[0])]=str(aParameterValue[0])# It adds a new attribute in the JSON file with the name of the first argument in aParameter's list (e.g. in Normal that will be the mean), putting the value of the distribution's first parameter (e.g. in Normal so for mean value that will be 7.0)
processingTime[str(bParameters[0])]=str(bParameterValue[0])# It adds a new attribute in the JSON file with the name of the second argument in aParameter's list (e.g. in Normal that will be the standarddeviation), putting the value of the distribution's second parameter (e.g. in Normal so for standarddeviation value that will be 7.0)
jsonFile=open('JSON_Topology10_Output.json',"w")#It opens the JSON file
jsonFile.write(json.dumps(data,indent=True))#It writes the updated data to the JSON file
jsonFile.close()#It closes the file
#================================ Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in Excel files =============================================#
