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Commit d38da9ba authored by panos's avatar panos
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A test added for this example

parent 41191a72
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Showing with 164 additions and 147 deletions
dream/KnowledgeExtraction/KEtool_examples/ParallelStations_withfailures/ParallelStations_example.py
View file @ d38da9ba
......@@ -27,22 +27,28 @@ from dream.KnowledgeExtraction.DistributionFitting import DistFittest
from dream.KnowledgeExtraction.DistributionFitting import Distributions
from dream.KnowledgeExtraction.ExcelOutput import Output
from dream.KnowledgeExtraction.JSONOutput import JSONOutput
import ImportDatabase
from dream.KnowledgeExtraction.ImportDatabase import ConnectionData
from xml.etree import ElementTree as et
from CMSDOutput import CMSDOutput
from dream.KnowledgeExtraction.CMSDOutput import CMSDOutput
import json
import os
#================================= Extract data from the database ==========================================#
cnxn=ImportDatabase.ConnectionData(seekName='ServerData', implicitExt='txt', number_of_cursors=3)
cursors=cnxn.getCursors()
a = cursors[0].execute("""
def main(test=0, JSONFileName='JSON_example.json',
CMSDFileName='CMSD_ParallelStations.xml',
DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
file_path=None,
jsonFile=None, cmsdFile=None):
if not file_path:
cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
cursors=cnxn.getCursors()
a = cursors[0].execute("""
select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
from production_status
""")
MILL1=[]
MILL2=[]
for j in range(a.rowcount):
MILL1=[]
MILL2=[]
for j in range(a.rowcount):
#get the next line
ind1=a.fetchone()
if ind1.stat_code == 'MILL1':
......@@ -58,9 +64,9 @@ for j in range(a.rowcount):
else:
continue
transform = BasicTransformations()
procTime_MILL1=[]
for elem in MILL1:
transform = BasicTransformations()
procTime_MILL1=[]
for elem in MILL1:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
......@@ -68,8 +74,8 @@ for elem in MILL1:
dt=transform.subtraction(t2, t1)
procTime_MILL1.append(dt[0])
procTime_MILL2=[]
for elem in MILL2:
procTime_MILL2=[]
for elem in MILL2:
t1=[]
t2=[]
t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
......@@ -78,18 +84,18 @@ for elem in MILL2:
procTime_MILL2.append(dt[0])
b = cursors[1].execute("""
b = cursors[1].execute("""
select stat_code, MTTF_hour
from failures
""")
c = cursors[2].execute("""
c = cursors[2].execute("""
select stat_code, MTTR_hour
from repairs
""")
MTTF_MILL1=[]
MTTF_MILL2=[]
for j in range(b.rowcount):
MTTF_MILL1=[]
MTTF_MILL2=[]
for j in range(b.rowcount):
#get the next line
ind2=b.fetchone()
if ind2.stat_code == 'MILL1':
......@@ -99,9 +105,9 @@ for j in range(b.rowcount):
else:
continue
MTTR_MILL1=[]
MTTR_MILL2=[]
for j in range(c.rowcount):
MTTR_MILL1=[]
MTTR_MILL2=[]
for j in range(c.rowcount):
#get the next line
ind3=c.fetchone()
if ind3.stat_code == 'MILL1':
......@@ -111,71 +117,82 @@ for j in range(c.rowcount):
else:
continue
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
datafile=('CMSD_ParallelStations.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
exportCMSD=CMSDOutput()
stationId1='M1'
stationId2='M2'
procTime1=exportCMSD.ProcessingTimes(tree, stationId1, distProcTime_MILL1)
procTime2=exportCMSD.ProcessingTimes(procTime1, stationId2, distProcTime_MILL2)
TTF1=exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
TTR1=exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
TTF2=exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
TTR2=exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
TTR2.write('CMSD_ParallelStations_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 this example ================================#
jsonFile = open('JSON_example.json','r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
exportJSON=JSONOutput()
stationId1='M1'
stationId2='M2'
data1=exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
data2=exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
data3=exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
data4=exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
data5=exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
data6=exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
jsonFile = open('JSON_ParallelStations_Output.json',"w") #It opens the JSON file
jsonFile.write(json.dumps(data6, 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 xls files ==========================#
export=Output()
export.PrintStatisticalMeasures(procTime_MILL1,'procTimeMILL1_StatResults.xls')
export.PrintStatisticalMeasures(procTime_MILL2,'procTimeMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL1,'MTTFMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL2,'MTTFMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL1,'MTTRMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL2,'MTTRMILL2_StatResults.xls')
export.PrintDistributionFit(procTime_MILL1,'procTimeMILL1_DistFitResults.xls')
export.PrintDistributionFit(procTime_MILL2,'procTimeMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL1,'MTTFMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL2,'MTTFMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL1,'MTTRMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL2,'MTTRMILL2_DistFitResults.xls')
#======================= Fit data to statistical distributions ================================#
dist_proctime = DistFittest()
distProcTime_MILL1 = dist_proctime.ks_test(procTime_MILL1)
distProcTime_MILL2 = dist_proctime.ks_test(procTime_MILL2)
dist_MTTF = Distributions()
dist_MTTR = Distributions()
distMTTF_MILL1 = dist_MTTF.Weibull_distrfit(MTTF_MILL1)
distMTTF_MILL2 = dist_MTTF.Weibull_distrfit(MTTF_MILL2)
distMTTR_MILL1 = dist_MTTR.Poisson_distrfit(MTTR_MILL1)
distMTTR_MILL2 = dist_MTTR.Poisson_distrfit(MTTR_MILL2)
#======================== Output preparation: output the values prepared in the CMSD information model of this model ====================================================#
if not cmsdFile:
datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), CMSDFileName)) #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
exportCMSD=CMSDOutput()
stationId1='M1'
stationId2='M2'
procTime1=exportCMSD.ProcessingTimes(tree, stationId1, distProcTime_MILL1)
procTime2=exportCMSD.ProcessingTimes(procTime1, stationId2, distProcTime_MILL2)
TTF1=exportCMSD.TTF(procTime2, stationId1, distMTTF_MILL1)
TTR1=exportCMSD.TTR(TTF1, stationId1, distMTTR_MILL1)
TTF2=exportCMSD.TTF(TTR1, stationId2, distMTTF_MILL2)
TTR2=exportCMSD.TTR(TTF2, stationId2, distMTTR_MILL2)
TTR2.write('CMSD_ParallelStations_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 this example ================================#
if not jsonFile:
jsonFile = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), JSONFileName),'r') #It opens the JSON file
data = json.load(jsonFile) #It loads the file
jsonFile.close()
else:
data = json.load(jsonFile)
exportJSON=JSONOutput()
stationId1='M1'
stationId2='M2'
data1=exportJSON.ProcessingTimes(data, stationId1, distProcTime_MILL1)
data2=exportJSON.ProcessingTimes(data1, stationId2, distProcTime_MILL2)
data3=exportJSON.TTF(data2, stationId1, distMTTF_MILL1)
data4=exportJSON.TTR(data3, stationId1, distMTTR_MILL1)
data5=exportJSON.TTF(data4, stationId2, distMTTF_MILL2)
data6=exportJSON.TTR(data5, stationId2, distMTTR_MILL2)
# if we run from test return the data6
if test:
return data6
jsonFile = open('JSON_ParallelStations_Output.json',"w") #It opens the JSON file
jsonFile.write(json.dumps(data6, 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 xls files ==========================#
export=Output()
export.PrintStatisticalMeasures(procTime_MILL1,'procTimeMILL1_StatResults.xls')
export.PrintStatisticalMeasures(procTime_MILL2,'procTimeMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL1,'MTTFMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTF_MILL2,'MTTFMILL2_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL1,'MTTRMILL1_StatResults.xls')
export.PrintStatisticalMeasures(MTTR_MILL2,'MTTRMILL2_StatResults.xls')
export.PrintDistributionFit(procTime_MILL1,'procTimeMILL1_DistFitResults.xls')
export.PrintDistributionFit(procTime_MILL2,'procTimeMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL1,'MTTFMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTF_MILL2,'MTTFMILL2_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL1,'MTTRMILL1_DistFitResults.xls')
export.PrintDistributionFit(MTTR_MILL2,'MTTRMILL2_DistFitResults.xls')
if __name__ == '__main__':
main()
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