Test added for this example

dream/KnowledgeExtraction/KEtoolSimul8_examples/ParallelStationsFailures/ParallelStationsFailures.py

@@ -22,120 +22,139 @@ Created on 3 Dec 2014
 # along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
 # ===========================================================================
 
-from Transformations import BasicTransformations
-from DistributionFitting import Distributions
-import ImportDatabase
+from dream.KnowledgeExtraction.Transformations import BasicTransformations
+from dream.KnowledgeExtraction.DistributionFitting import Distributions
 from xml.etree import ElementTree as et
-from Simul8XML import Simul8Output
+from dream.KnowledgeExtraction.Simul8XML import Simul8Output
+from dream.KnowledgeExtraction.ImportDatabase import ConnectionData
+import os
 
 #================================= Extract data from the database ==========================================#
-#The Import Database object is used to import the data, the user has to specify the path to the file containing the connection data 
-cnxn=ImportDatabase.ConnectionData(seekName='ServerData', implicitExt='txt', number_of_cursors=3)
-cursors=cnxn.getCursors()
+#The Import Database object is used to import the data, the user has to specify the path to the file containing the connection data
+ 
+def main(test=0, simul8XMLFileName='ParallelStations.xml',
+                DBFilePath = 'C:\Users\Panos\Documents\KE tool_documentation',
+                file_path=None, simul8XMLFile=None):
+    if not file_path:
+        cnxn=ConnectionData(seekName='ServerData', file_path=DBFilePath, implicitExt='txt', number_of_cursors=3)
+        cursors=cnxn.getCursors()
 #Database queries used to extract the required data, in this example the processing times are given subtracting the TIME IN data point from the TIME OUT data point
-a = cursors[0].execute("""
-        select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
-        from production_status
-                """)
-MILL1=[] #Initialization of MILL1 list
-MILL2=[] #Initialization of MILL2 list
-for j in range(a.rowcount):
-    #get the next line
-    ind1=a.fetchone() 
-    if ind1.stat_code == 'MILL1':
-        procTime=[]
-        procTime.insert(0,ind1.TIMEIN)
-        procTime.insert(1,ind1.TIMEOUT)
-        MILL1.append(procTime)
-    elif ind1.stat_code == 'MILL2':
-        procTime=[]
-        procTime.insert(0,ind1.TIMEIN)
-        procTime.insert(1,ind1.TIMEOUT)
-        MILL2.append(procTime)
+    a = cursors[0].execute("""
+            select prod_code, stat_code,emp_no, TIMEIN, TIMEOUT
+            from production_status
+                    """)
+    MILL1=[] #Initialization of MILL1 list
+    MILL2=[] #Initialization of MILL2 list
+    for j in range(a.rowcount):
+        #get the next line
+        ind1=a.fetchone() 
+        if ind1.stat_code == 'MILL1':
+            procTime=[]
+            procTime.insert(0,ind1.TIMEIN)
+            procTime.insert(1,ind1.TIMEOUT)
+            MILL1.append(procTime)
+        elif ind1.stat_code == 'MILL2':
+            procTime=[]
+            procTime.insert(0,ind1.TIMEIN)
+            procTime.insert(1,ind1.TIMEOUT)
+            MILL2.append(procTime)
+        else:
+            continue
+    #The  BasicTransformations object is called to conduct some data transformations     
+    transform = BasicTransformations()
+    procTime_MILL1=[]
+    for elem in MILL1:
+        t1=[]
+        t2=[]
+        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
+        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
+        dt=transform.subtraction(t2, t1)
+        procTime_MILL1.append(dt[0])
+    
+    procTime_MILL2=[]
+    for elem in MILL2:
+        t1=[]
+        t2=[]
+        t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
+        t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
+        dt=transform.subtraction(t2, t1)
+        procTime_MILL2.append(dt[0])
+    #Database queries used again to extract the MTTF and MTTR data points
+    b = cursors[1].execute("""
+            select stat_code, MTTF_hour
+            from failures
+                    """)
+    
+    c = cursors[2].execute("""
+            select stat_code, MTTR_hour
+            from repairs
+                    """)         
+    MTTF_MILL1=[] #Initialization of the list that will contain the MTTF data points for MILL1  
+    MTTF_MILL2=[] #Initialization of the list that will contain the MTTF data points for MILL2
+    for j in range(b.rowcount):
+        #get the next line
+        ind2=b.fetchone() 
+        if ind2.stat_code == 'MILL1':
+            MTTF_MILL1.append(ind2.MTTF_hour)
+        elif ind2.stat_code == 'MILL2':
+            MTTF_MILL2.append(ind2.MTTF_hour)
+        else:
+            continue
+    
+    MTTR_MILL1=[] #Initialization of the list that will contain the MTTR data points for MILL1 
+    MTTR_MILL2=[] #Initialization of the list that will contain the MTTR data points for MILL1 
+    for j in range(c.rowcount):
+        #get the next line
+        ind3=c.fetchone() 
+        if ind3.stat_code == 'MILL1':
+            MTTR_MILL1.append(ind3.MTTR_hour)
+        elif ind3.stat_code == 'MILL2':
+            MTTR_MILL2.append(ind3.MTTR_hour)
+        else:
+            continue   
+    
+    #======================= Fit data to statistical distributions ================================#
+    #The Distributions object is called to fit statistical distributions to the in scope data
+    dist_proctime = Distributions() 
+    distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
+    distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
+    
+    dist_MTTF = Distributions()
+    dist_MTTR = Distributions()
+    distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
+    distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
+    
+    distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
+    distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2) 
+    
+    #======================= Output preparation: output the updated values in the XML file of this example ================================#
+    
+    if not simul8XMLFile:
+        datafile=(os.path.join(os.path.dirname(os.path.realpath(__file__)), simul8XMLFileName))       #It defines the name or the directory of the XML file 
+        tree = et.parse(datafile)    
     else:
-        continue
-#The  BasicTransformations object is called to conduct some data transformations     
-transform = BasicTransformations()
-procTime_MILL1=[]
-for elem in MILL1:
-    t1=[]
-    t2=[]
-    t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
-    t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
-    dt=transform.subtraction(t2, t1)
-    procTime_MILL1.append(dt[0])
+        datafile=simul8XMLFile
+        tree = et.parse(datafile)
+         
+    simul8 = Simul8Output()    #Call the Simul8Output object
+    #Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
+    procTimes1 = simul8.ProcTimes(tree,'MILL1',distProcTime_MILL1)
+    procTimes2 = simul8.ProcTimes(procTimes1,'MILL2',distProcTime_MILL2)
+    #Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
+    MTTF1 = simul8.MTBF(procTimes2,'MILL1',distMTTF_MILL1)
+    MTTR1 = simul8.MTTR(MTTF1,'MILL1',distMTTR_MILL1)
+    
+    MTTF2 = simul8.MTBF(MTTR1,'MILL2',distMTTF_MILL2)
+    MTTR2 = simul8.MTTR(MTTF2,'MILL2',distMTTR_MILL2)
+    #Output the XML file with the processed data 
+    output= MTTR2.write('KEtool_ParallelStations.xml')
+    
+    if test:
+        output=et.parse('KEtool_ParallelStations.xml')
+        return output 
 
-procTime_MILL2=[]
-for elem in MILL2:
-    t1=[]
-    t2=[]
-    t1.append(((elem[0].hour)*60)*60 + (elem[0].minute)*60 + elem[0].second)
-    t2.append(((elem[1].hour)*60)*60 + (elem[1].minute)*60 + elem[1].second)
-    dt=transform.subtraction(t2, t1)
-    procTime_MILL2.append(dt[0])
-#Database queries used again to extract the MTTF and MTTR data points
-b = cursors[1].execute("""
-        select stat_code, MTTF_hour
-        from failures
-                """)
-
-c = cursors[2].execute("""
-        select stat_code, MTTR_hour
-        from repairs
-                """)         
-MTTF_MILL1=[] #Initialization of the list that will contain the MTTF data points for MILL1  
-MTTF_MILL2=[] #Initialization of the list that will contain the MTTF data points for MILL2
-for j in range(b.rowcount):
-    #get the next line
-    ind2=b.fetchone() 
-    if ind2.stat_code == 'MILL1':
-        MTTF_MILL1.append(ind2.MTTF_hour)
-    elif ind2.stat_code == 'MILL2':
-        MTTF_MILL2.append(ind2.MTTF_hour)
-    else:
-        continue
-
-MTTR_MILL1=[] #Initialization of the list that will contain the MTTR data points for MILL1 
-MTTR_MILL2=[] #Initialization of the list that will contain the MTTR data points for MILL1 
-for j in range(c.rowcount):
-    #get the next line
-    ind3=c.fetchone() 
-    if ind3.stat_code == 'MILL1':
-        MTTR_MILL1.append(ind3.MTTR_hour)
-    elif ind3.stat_code == 'MILL2':
-        MTTR_MILL2.append(ind3.MTTR_hour)
-    else:
-        continue   
-
-#======================= Fit data to statistical distributions ================================#
-#The Distributions object is called to fit statistical distributions to the in scope data
-dist_proctime = Distributions() 
-distProcTime_MILL1 = dist_proctime.Lognormal_distrfit(procTime_MILL1)
-distProcTime_MILL2 = dist_proctime.Weibull_distrfit(procTime_MILL2)
-
-dist_MTTF = Distributions()
-dist_MTTR = Distributions()
-distMTTF_MILL1 = dist_MTTF.Exponential_distrfit(MTTF_MILL1)
-distMTTF_MILL2 = dist_MTTF.Exponential_distrfit(MTTF_MILL2)
-
-distMTTR_MILL1 = dist_MTTR.Normal_distrfit(MTTR_MILL1)
-distMTTR_MILL2 = dist_MTTR.Normal_distrfit(MTTR_MILL2) 
-
-#======================= Output preparation: output the updated values in the XML file of this example ================================#
-
-datafile = ('ParallelStations.xml')  #define the input xml file
-tree = et.parse(datafile) 
-simul8 = Simul8Output()    #Call the Simul8Output object
-#Assign the statistical distribution found above in the XML file using methods of the Simul8Output object
-procTimes1 = simul8.ProcTimes(tree,'MILL1',distProcTime_MILL1)
-procTimes2 = simul8.ProcTimes(procTimes1,'MILL2',distProcTime_MILL2)
-#Again assign the MTTF and MTTR probability distributions calling the relevant methods from the Simul8Output object
-MTTF1 = simul8.MTBF(procTimes2,'MILL1',distMTTF_MILL1)
-MTTR1 = simul8.MTTR(MTTF1,'MILL1',distMTTR_MILL1)
-
-MTTF2 = simul8.MTBF(MTTR1,'MILL2',distMTTF_MILL2)
-MTTR2 = simul8.MTTR(MTTF2,'MILL2',distMTTR_MILL2)
-#Output the XML file with the processed data 
-output= MTTR2.write('KEtool_ParallelStations.xml')  
+if __name__ == '__main__':
+    main() 
+