A test added for this example

dream/KnowledgeExtraction/KEtool_examples/ParallelStations_withfailures/ParallelStations_example.py

@@ -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()