Bugs fixed in the Production Line example

dream/KnowledgeExtraction/KEtool_examples/ProductionLine/CMSD_Output.py

@@ -359,14 +359,14 @@ def CMSD_example(list1,list2):
     Name.text=str(list1['P1']['distributionType'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='mean'
+    Name.text='shape'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P1']['mean'])
+    Value.text=str(list1['P1']['shape'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='stdev'
+    Name.text='rate'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P1']['stdev'])
+    Value.text=str(list1['P1']['rate'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -420,14 +420,14 @@ def CMSD_example(list1,list2):
     Name.text=str(list1['P2']['distributionType'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='mean'
+    Name.text='shape'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P2']['mean'])
+    Value.text=str(list1['P2']['shape'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='stdev'
+    Name.text='rate'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P2']['stdev'])
+    Value.text=str(list1['P2']['rate'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -606,14 +606,14 @@ def CMSD_example(list1,list2):
     Name.text=str(list1['P5']['distributionType'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='mean'
+    Name.text='shape'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P5']['mean'])
+    Value.text=str(list1['P5']['shape'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='stdev'
+    Name.text='rate'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P5']['mean'])
+    Value.text=str(list1['P5']['rate'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -792,14 +792,14 @@ def CMSD_example(list1,list2):
     Name.text=str(list1['P8']['distributionType'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='mean'
+    Name.text='shape'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P8']['mean'])
+    Value.text=str(list1['P8']['shape'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='stdev'
+    Name.text='rate'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P8']['stdev'])
+    Value.text=str(list1['P8']['rate'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -854,14 +854,14 @@ def CMSD_example(list1,list2):
     Name.text=str(list1['P9']['distributionType'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='mean'
+    Name.text='shape'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P9']['mean'])
+    Value.text=str(list1['P9']['shape'])
     DistributionParameter=SubElement(Distribution,'DistributionParameter')
     Name=SubElement(DistributionParameter,'Name')
-    Name.text='stdev'
+    Name.text='rate'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P9']['stdev'])
+    Value.text=str(list1['P9']['rate'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -923,7 +923,7 @@ def CMSD_example(list1,list2):
     Name=SubElement(DistributionParameter,'Name')
     Name.text='stdev'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P9']['mean'])
+    Value.text=str(list1['P10']['stdev'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')
@@ -985,7 +985,7 @@ def CMSD_example(list1,list2):
     Name=SubElement(DistributionParameter,'Name')
     Name.text='stdev'
     Value=SubElement(DistributionParameter,'Value')
-    Value.text=str(list1['P11']['mean'])
+    Value.text=str(list1['P11']['stdev'])
     
     Property=SubElement(Process,'Property')
     Name=SubElement(Property,'Name')

dream/KnowledgeExtraction/KEtool_examples/ProductionLine/JSON_Output.py

+'''
+Created on 9 Oct 2014
+
+@author: Panos
+'''
+# ===========================================================================
+# Copyright 2013 University of Limerick
+#
+# This file is part of DREAM.
+#
+# DREAM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# DREAM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
+# ===========================================================================
+
+import json
+
+def JSON_example(list1,list2):
+    
+    jsonFile= open('JSON_example.json','r')
+    data = json.load(jsonFile)
+    jsonFile.close()
+       
+    nodes=data.get('nodes',{})        
+    for (element_id,element) in nodes.iteritems():
+        name=element.get('name')
+        scrapQuantity=element.get('scrapQuantity',{})
+        processingTime=element.get('processingTime',{})
+        
+        if name in list1.keys(): 
+            element['processingTime']= list1[name]
+        else:
+            continue
+        if name in list2.keys():
+            element['scrapQuantity']= list2[name]
+        else:
+            continue
+        jsonFile = open('JSON_exampleOutput.json',"w")
+        jsonFile.write(json.dumps(data, indent=True))
+        jsonFile.close()
+    return json.dumps(data, indent=True)
\ No newline at end of file

dream/KnowledgeExtraction/KEtool_examples/ProductionLine/ProdLine_example.py

@@ -24,6 +24,7 @@ Created on 19 Feb 2014
 
 from StatisticalMeasures import BasicStatisticalMeasures
 from DataManipulation import DataManagement
+from DistributionFitting import DistFittest
 from DistributionFitting import Distributions
 from CMSD_Output import CMSD_example
 from JSON_Output import JSON_example
@@ -36,7 +37,7 @@ import dream.simulation.LineGenerationJSON as ManPyMain
 #================= Main script of KE tool  =====================================#
 
 #Read from the given directory the Excel document with the input data
-workbook = xlrd.open_workbook('inputData.xls')
+workbook = xlrd.open_workbook('input_Data.xls')
 worksheets = workbook.sheet_names()
 worksheet_ProcessingTimes = worksheets[1]     #Define the worksheet with the Processing times data
 worksheet_ScrapQuantity = worksheets[0]       #Define the worksheet with the Scrap Quantity data
@@ -46,38 +47,57 @@ ProcessingTimes= A.Input_data(worksheet_ProcessingTimes, workbook)   #Create the
 ScrapQuantity=A.Input_data(worksheet_ScrapQuantity, workbook)        #Create the Scrap Quantity dictionary with keys the different stations in the line and values the scrap quantity data of different batches in these stations
 
 ##Get from the Scrap Quantity dictionary the different keys and define the following lists with the scrap quantity data of the different stations in the topology
-P7_Scrap = ScrapQuantity.get('P7',[])         
-P1_Scrap = ScrapQuantity.get('P1',[])
-P2_Scrap= ScrapQuantity.get('P3',[])
-P3_Scrap=ScrapQuantity.get('P3',[])
-P8_Scrap=ScrapQuantity.get('P8',[])
+        
+P1_Scrap= ScrapQuantity.get('P1',[])
+P2_Scrap= ScrapQuantity.get('P2',[])
+P3_Scrap= ScrapQuantity.get('P3',[])
+P4_Scrap= ScrapQuantity.get('P4',[])
+P5_Scrap= ScrapQuantity.get('P5',[])
+P6_Scrap= ScrapQuantity.get('P6',[])
+P7_Scrap= ScrapQuantity.get('P7',[]) 
+P8_Scrap= ScrapQuantity.get('P8',[])
 P9_Scrap= ScrapQuantity.get('P9',[])
+P10_Scrap= ScrapQuantity.get('P10',[])
+P11_Scrap= ScrapQuantity.get('P11',[])
 
 ##Get from the Processing times dictionary the different keys and define the following lists with the processing times data of the different stations in the topology 
-P7_Proc = ProcessingTimes.get('P7',[])
-P1_Proc = ProcessingTimes.get('P1',[])
+
+P1_Proc= ProcessingTimes.get('P1',[])
 P2_Proc= ProcessingTimes.get('P2',[])
-P3_Proc=ProcessingTimes.get('P3',[])
-P8_Proc=ProcessingTimes.get('P8',[])
+P3_Proc= ProcessingTimes.get('P3',[])
+P4_Proc= ProcessingTimes.get('P4',[])
+P5_Proc= ProcessingTimes.get('P5',[])
+P6_Proc= ProcessingTimes.get('P6',[])
+P7_Proc= ProcessingTimes.get('P7',[])
+P8_Proc= ProcessingTimes.get('P8',[])
 P9_Proc= ProcessingTimes.get('P9',[])
+P10_Proc= ProcessingTimes.get('P10',[])
+P11_Proc= ProcessingTimes.get('P11',[])
 
 #Call the HandleMissingValues object and replace with zero the missing values in the lists with the scrap quantity data 
+
 B=HandleMissingValues()
-P7_Scrap= B.ReplaceWithZero(P7_Scrap)
+
 P1_Scrap= B.ReplaceWithZero(P1_Scrap)
 P2_Scrap= B.ReplaceWithZero(P2_Scrap)
 P3_Scrap= B.ReplaceWithZero(P3_Scrap)
+P4_Scrap= B.ReplaceWithZero(P4_Scrap)
+P5_Scrap= B.ReplaceWithZero(P5_Scrap)
+P6_Scrap= B.ReplaceWithZero(P6_Scrap)
+P7_Scrap= B.ReplaceWithZero(P7_Scrap)
 P8_Scrap= B.ReplaceWithZero(P8_Scrap)
 P9_Scrap= B.ReplaceWithZero(P9_Scrap)
+P10_Scrap= B.ReplaceWithZero(P10_Scrap)
+P11_Scrap= B.ReplaceWithZero(P11_Scrap)
 
 # #Call the BasicSatatisticalMeasures object 
 C=BasicStatisticalMeasures()
 #Create a list with values the calculated mean value of scrap quantity on the different stations in the line
-listScrap=[C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P7_Scrap),C.mean(P8_Scrap),C.mean(P8_Scrap),C.mean(P9_Scrap), C.mean(P9_Scrap)] 
+listScrap=[C.mean(P1_Scrap),C.mean(P2_Scrap),C.mean(P3_Scrap),C.mean(P4_Scrap),C.mean(P5_Scrap),C.mean(P6_Scrap),C.mean(P7_Scrap),C.mean(P8_Scrap),C.mean(P9_Scrap),C.mean(P10_Scrap), C.mean(P11_Scrap)] 
  
-F=DataManagement()
+D= DataManagement()
  
-listScrap=F.round(listScrap)       #Round the mean values of the list so as to get integers
+listScrap=D.round(listScrap)       #Round the mean values of the list so as to get integers
 
 dictScrap={}
 dictScrap['P1']= listScrap[0]
@@ -92,35 +112,30 @@ dictScrap['P9']= listScrap[8]
 dictScrap['P10']= listScrap[9]
 dictScrap['P11']= listScrap[10]
 
-#Create a tuple with the Processing times data lists of the different stations
-a=(P1_Proc,P2_Proc,P3_Proc,P1_Proc,P2_Proc,P3_Proc,P7_Proc,P8_Proc,P8_Proc,P9_Proc,P9_Proc)
-
-E=Distributions()      #Call the DistFittest object
-
+E= DistFittest()
 dictProc={}
-dictProc['P1']= E.Normal_distrfit(P1_Proc)
-dictProc['P2']= E.Normal_distrfit(P2_Proc)
-dictProc['P3']= E.Normal_distrfit(P3_Proc)
-dictProc['P4']= E.Normal_distrfit(P1_Proc)
-dictProc['P5']= E.Normal_distrfit(P2_Proc)
-dictProc['P6']= E.Normal_distrfit(P3_Proc)
-dictProc['P7']= E.Normal_distrfit(P7_Proc)
-dictProc['P8']= E.Normal_distrfit(P8_Proc)
-dictProc['P9']= E.Normal_distrfit(P8_Proc)
-dictProc['P10']= E.Normal_distrfit(P9_Proc)
-dictProc['P11']= E.Normal_distrfit(P9_Proc)
-
-
-D=Output()
-D.PrintDistributionFit(P2_Proc,"DistributionFittingResults_P2Proc.xls")
-D.PrintStatisticalMeasures(P2_Proc, "StatisticalMeasuresResults_P2Proc.xls")
+dictProc['P1']= E.ks_test(P1_Proc)
+dictProc['P2']= E.ks_test(P2_Proc)
+dictProc['P3']= E.ks_test(P3_Proc)
+dictProc['P4']= E.ks_test(P4_Proc)
+dictProc['P5']= E.ks_test(P5_Proc)
+dictProc['P6']= E.ks_test(P6_Proc)
+dictProc['P7']= E.ks_test(P7_Proc)
+dictProc['P8']= E.ks_test(P8_Proc)
+dictProc['P9']= E.ks_test(P9_Proc)
+dictProc['P10']= E.ks_test(P10_Proc)
+dictProc['P11']= E.ks_test(P11_Proc) 
+
+F= Output()
+F.PrintDistributionFit(P2_Proc,"DistributionFittingResults_P2Proc.xls")
+F.PrintStatisticalMeasures(P2_Proc, "StatisticalMeasuresResults_P2Proc.xls")
 
 CMSD_example(dictProc,dictScrap)    #Print the CMSD document, calling the CMSD_example method with arguments the dictProc and dictScrap dictionaries
 JSON_example(dictProc,dictScrap)    #Print the JSON file, calling the JSON_example method
 
 
 #calls ManPy main script with the input
-
+ 
 simulationOutput=ManPyMain.main(input_data=str((JSON_example(dictProc,dictScrap))))
 # save the simulation output
 jsonFile = open('ManPyOutput.json',"w")     #It opens the JSON file