examples changed to be able to run from test suite

dream/KnowledgeExtraction/KEtool_examples/TwoParallelStations/TwoParallelStations_example.py

@@ -22,44 +22,66 @@ Created on 20 Jun 2014
 # along with DREAM.  If not, see <http://www.gnu.org/licenses/>.
 # ===========================================================================
 
-from DistributionFitting import DistFittest
-from DistributionFitting import Distributions
-from ImportExceldata import Import_Excel
-from ExcelOutput import Output
-from ReplaceMissingValues import HandleMissingValues
+from dream.KnowledgeExtraction.DistributionFitting import DistFittest
+from dream.KnowledgeExtraction.DistributionFitting import Distributions
+from dream.KnowledgeExtraction.ImportExceldata import Import_Excel
+from dream.KnowledgeExtraction.ExcelOutput import Output
+from dream.KnowledgeExtraction.ReplaceMissingValues import HandleMissingValues
 from JSONOutput import JSONOutput
 import xlrd
 import json
 import dream.simulation.LineGenerationJSON as ManPyMain #import ManPy main JSON script
 
-#Read from the given directory the Excel document with the input data
-workbook = xlrd.open_workbook('inputData.xls')
-worksheets = workbook.sheet_names()
-worksheet_ProcessingTimes = worksheets[0]     #Define the worksheet with the Processing times data
+def main(test=0, ExcelFileName='KEtool_examples/TwoParallelStations/inputData.xls',
+                JSONFileName='KEtool_examples/TwoParallelStations/JSON_TwoParallelStations.json',
+                workbook=None,
+                jsonFile=None):
 
-inputData = Import_Excel()                              #Call the Python object Import_Excel
-ProcessingTimes = inputData.Input_data(worksheet_ProcessingTimes, workbook)   #Create the Processing Times dictionary with key Machines 1,2 and values the processing time data
-
-##Get from the above dictionaries the M1 key and define the following lists with data 
-M1_ProcTime = ProcessingTimes.get('M1',[])         
-M2_ProcTime = ProcessingTimes.get('M2',[])  
-
-#Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
-misValues =HandleMissingValues()
-M1_ProcTime = misValues.ReplaceWithMean(M1_ProcTime)
-M2_ProcTime = misValues.ReplaceWithMean(M2_ProcTime)
-
-MLE = Distributions()      #Call the Distributions object (Maximum Likelihood Estimation - MLE)
-KS = DistFittest()      #Call the DistFittest object  (Kolmoghorov-Smirnov test)
-
-M1ProcTime_dist = KS.ks_test(M1_ProcTime)
-M2ProcTime_dist = MLE.Normal_distrfit(M2_ProcTime)
-
-
-#======================= Output preparation: output the updated values in the JSON file of this example ================================#
+    #Read from the given directory the Excel document with the input data
+    if not workbook:
+        workbook = xlrd.open_workbook(ExcelFileName)
+    worksheets = workbook.sheet_names()
+    worksheet_ProcessingTimes = worksheets[0]     #Define the worksheet with the Processing times data
+    
+    inputData = Import_Excel()                              #Call the Python object Import_Excel
+    ProcessingTimes = inputData.Input_data(worksheet_ProcessingTimes, workbook)   #Create the Processing Times dictionary with key Machines 1,2 and values the processing time data
+    
+    ##Get from the above dictionaries the M1 key and define the following lists with data 
+    M1_ProcTime = ProcessingTimes.get('M1',[])         
+    M2_ProcTime = ProcessingTimes.get('M2',[])  
+    
+    #Call the HandleMissingValues object and replace the missing values in the lists with the mean of the non-missing values
+    misValues =HandleMissingValues()
+    M1_ProcTime = misValues.ReplaceWithMean(M1_ProcTime)
+    M2_ProcTime = misValues.ReplaceWithMean(M2_ProcTime)
+    
+    MLE = Distributions()      #Call the Distributions object (Maximum Likelihood Estimation - MLE)
+    KS = DistFittest()      #Call the DistFittest object  (Kolmoghorov-Smirnov test)
+    
+    M1ProcTime_dist = KS.ks_test(M1_ProcTime)
+    M2ProcTime_dist = MLE.Normal_distrfit(M2_ProcTime)
+    
+    
+    #======================= Output preparation: output the updated values in the JSON file of this example ================================#
 jsonFile = open('JSON_ParallelStations.json','r')      #It opens the JSON file 
-data = json.load(jsonFile)                                                              #It loads the file
-jsonFile.close()
+        jsonFile = open(JSONFileName,'r')      #It opens the JSON file 
+        data = json.load(jsonFile)                                                              #It loads the file
+        jsonFile.close()
+    else:
+        data = json.load(jsonFile) 
+    nodes = data['graph']['node']                                                         #It creates a variable that holds the 'nodes' dictionary
+    
+    procTimeM1dict={}
+    procTimeM2dict={}
+    dist=M1ProcTime_dist['distributionType']
+    del M1ProcTime_dist['distributionType']
+    procTimeM1dict[dist]=M1ProcTime_dist
+    
+    dist=M2ProcTime_dist['distributionType']
+    del M2ProcTime_dist['distributionType']
+    procTimeM2dict[dist]=M2ProcTime_dist
+    
+    for element in nodes:
 
 exportJSON=JSONOutput()
 stationId1='St1'
@@ -70,19 +92,27 @@ data2=exportJSON.ProcessingTimes(data1, stationId2, M2ProcTime_dist)
 jsonFile = open('JSON_ParallelStations_Output.json',"w")     #It opens the JSON file
 jsonFile.write(json.dumps(data2, 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 ==========================#
+    # save the KE output in xls
+    export=Output()
+    
+    # save KE results in Excel
+    export.PrintStatisticalMeasures(M1_ProcTime,'M1_ProcTime_StatResults.xls')   
+    export.PrintStatisticalMeasures(M2_ProcTime,'M2_ProcTime_StatResults.xls')
+    
+    export.PrintDistributionFit(M1_ProcTime,'M1_ProcTime_DistFitResults.xls')
+    export.PrintDistributionFit(M2_ProcTime,'M2_ProcTime_DistFitResults.xls')
 
-#=================== Calling the ExcelOutput object, outputs the outcomes of the statistical analysis in xls files ==========================#
-export=Output()
+    # save the KE output in JSON
+    jsonFile = open('JSON_ParallelStations_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
 
-export.PrintStatisticalMeasures(M1_ProcTime,'M1_ProcTime_StatResults.xls')   
-export.PrintStatisticalMeasures(M2_ProcTime,'M2_ProcTime_StatResults.xls')
-
-export.PrintDistributionFit(M1_ProcTime,'M1_ProcTime_DistFitResults.xls')
-export.PrintDistributionFit(M2_ProcTime,'M2_ProcTime_DistFitResults.xls')
-#calls ManPy main script with the input
-simulationOutput=ManPyMain.main(input_data=json.dumps(data))
-# save the simulation output
-jsonFile = open('ManPyOutput.json',"w")     #It opens the JSON file
-jsonFile.write(simulationOutput)                                           #It writes the updated data to the JSON file 
-jsonFile.close()                                                                        #It closes the file
+    # save the simulation output
+    jsonFile = open('ManPyOutput.json',"w")     #It opens the JSON file
+    jsonFile.write(simulationOutput)                                           #It writes the updated data to the JSON file 
+    jsonFile.close()                                                                        #It closes the file
 
+if __name__ == '__main__':
+    main()