modify InputData_DemandPlanning to have a function that we can call externally

dream/KnowledgeExtraction/PilotCases/InputData_DemandPlanning.py

@@ -23,185 +23,204 @@ Created on 7 May 2014
 @author: Panos
 '''
 
-from DistributionFitting import Distributions
-from ImportExceldata import Import_Excel
-from ReplaceMissingValues import HandleMissingValues
+from dream.KnowledgeExtraction.DistributionFitting import Distributions
+from dream.KnowledgeExtraction.ImportExceldata import Import_Excel
+from dream.KnowledgeExtraction.ReplaceMissingValues import HandleMissingValues
 import rpy2.robjects as robjects
 from xlwt import Workbook
 import json
 import xlrd
 import random
+import urllib
 
-#Read data from the exported Excel file from RapidMiner and call the Import_Excel object of the KE tool to import this data in the tool
-workbook = xlrd.open_workbook(r'Input8PPOS.xlsx','r')     
-worksheets = workbook.sheet_names()
-worksheet_RapidMiner = worksheets[0] 
-
-PPOSToBeDisaggregated='PPOS4'
-PPOSQuantity=1000
-PlannedWeek=2
-
-A= Import_Excel()
-Turnovers=A.Input_data(worksheet_RapidMiner, workbook) #Dictionary with the data from the Excel file
-
-#Create lists with the MAs' names and the Turnovers for the first twelve weeks of 2010 retrieving this data from the dictionary 
-PPOS=Turnovers.get('Ppos',[])
-SP=Turnovers.get('SP',[])
-MA=Turnovers.get('FP Material No PGS+',[])
-GlobalDemand=Turnovers.get('Global demand',[])
-
-#Call the Distributions object and fit the data from the list in Normal distribution, so as to have info on Global demand (mean and standard deviation)
-D=Distributions()
-E=HandleMissingValues()
-MA=E.DeleteMissingValue(MA)
-t=D.Normal_distrfit(GlobalDemand)
-avg=t.get('mean')
-stdev=t.get('stdev')
-
-def constrained_sum_sample_pos(n, total):
-    """Return a randomly chosen list of n positive integers summing to total.
-    Each such list is equally likely to occur."""
- 
-    dividers = sorted(random.sample(xrange(1, total), n - 1))
-    return [a - b for a, b in zip(dividers + [total], [0] + dividers)]
-
-def constrained_sum_sample_nonneg(n, total):
-    """Return a randomly chosen list of n nonnegative integers summing to total.
-    Each such list is equally likely to occur."""
-
-    return [x - 1 for x in constrained_sum_sample_pos(n, total + n)]     
-    
-DemandProfile={} #Create a dictionary
-MinPackagingSize=10
-week=[1,2,3,4,5,6,7,8,9,10] # list that defines the planning horizon, i.e. 10 weeks
-
-for i in week:
-    Demand=int(abs(random.normalvariate(avg,stdev))) # Generate a random, non-negative, integer number from the Normal distribution
-    AllocatedPercent=0.8-(0.05*i) # Defines a number starts with 0.8 or 80% and reduced with every iteration at 0.05 or 5%
-    Remaining_Demand=int((1-AllocatedPercent)*Demand) # Defines the Remaining demand
-    a=constrained_sum_sample_nonneg(len(MA),100)
-    myInt=100
-    a=robjects.FloatVector(a)
-    lista = [x/myInt for x in a] # Define a list with the same length as the MA list and elements float numbers with total sum equal to 1  
-    b=constrained_sum_sample_nonneg(len(MA),Remaining_Demand) # Define a list with the same length as the MA list and elements with total sum the Remaining demand  
-    dicta={}
-    for index in range(0,len(MA)):
-        MinUnits=round(b[index]*(random.uniform(0,0.2)),0)
-        TotalUnits=b[index]
-        if TotalUnits<MinPackagingSize:
-            TotalUnits=0
-        if MinUnits<MinPackagingSize:
-            MinUnits=0
-        dicta.update({MA[index]:[TotalUnits,MinUnits]}) # it updates a dictionary with key the different MAs and values the remaining demand and (b[index]*lista[index])        
-        DemandProfile.update({i:dicta}) #It updates a dictionary with key the number of each iteration (week) and value the dictionary dicta
-
-Table=[]
-i=0
-for i in range(len(MA)):
-    Table.append([PPOS[i],SP[i],MA[i]])
-    i+=1
-uniquePPOS=[]
-for ppos in PPOS:
-    if not ppos in uniquePPOS and ppos!='':
-        uniquePPOS.append(ppos)
-
-
-book=Workbook()
-sheet1 = book.add_sheet('Future', cell_overwrite_ok=True)
-aggrTable=[]
-for key in DemandProfile.keys():
-    for elem in DemandProfile[key]:
-        if DemandProfile[key].get(elem)[0]> 0:
-            MAkey=elem
-            totalUnits=DemandProfile[key].get(elem)[0]
-            minUnits=DemandProfile[key].get(elem)[1]
-            plannedWeek=key
-            aggrTable.append([MAkey,totalUnits,minUnits,plannedWeek])
-        else: 
-            continue  
-t=1
-aggrTable.sort(key=lambda x:x[1], reverse=False)
-for i in sorted(aggrTable, key= lambda x:int(x[3])):
-    sheet1.write(0,0,'Order ID')
-    sheet1.write(0,1,'MA ID')
-    sheet1.write(0,2,'Total # Units')
-    sheet1.write(0,3,'Min # Units')
-    sheet1.write(0,4,'Planned Week')
-    sheet1.write(t,1,i[0])
-    sheet1.write(t,2,i[1])
-    sheet1.write(t,3,i[2])
-    sheet1.write(t,4,i[3])
-    sheet1.write(t,0,t)    
-    book.save('DemandProfile.xls')
-    t+=1
-
-# open json file
-futureDemandProfileFile=open('futureDemandProfile.json', mode='w')
-futureDemandProfile={}
- 
-t=1
-for i in sorted(aggrTable, key= lambda x:int(x[3])):
-    dicta={'MAID':i[0],'TotalUnits':i[1],'MinUnits':i[2],'PlannedWeek':i[3]}
-    futureDemandProfile[t]=dicta
-    futureDemandProfileString=json.dumps(futureDemandProfile, indent=5)
-    t+=1
- 
-#write json file
-futureDemandProfileFile.write(futureDemandProfileString)
+def generateDemandPlanning(input_url):
+    """Generate random demand from spreadsheet at input_url.
+    """
+
+
+    # Read data from the exported Excel file from RapidMiner and call the Import_Excel object of the KE tool to import this data in the tool
+
+    demand_data = urllib.urlopen(input_url).read()
+    workbook = xlrd.open_workbook(file_contents=demand_data)
+
+    worksheets = workbook.sheet_names()
+    worksheet_RapidMiner = worksheets[0] 
+
+    PPOSToBeDisaggregated='PPOS4'
+    PPOSQuantity=1000
+    PlannedWeek=2
+
+    A= Import_Excel()
+    Turnovers=A.Input_data(worksheet_RapidMiner, workbook) #Dictionary with the data from the Excel file
+
+    #Create lists with the MAs' names and the Turnovers for the first twelve weeks of 2010 retrieving this data from the dictionary 
+    PPOS=Turnovers.get('Ppos',[])
+    SP=Turnovers.get('SP',[])
+    MA=Turnovers.get('FP Material No PGS+',[])
+    GlobalDemand=Turnovers.get('Global demand',[])
+
+    #Call the Distributions object and fit the data from the list in Normal distribution, so as to have info on Global demand (mean and standard deviation)
+    D=Distributions()
+    E=HandleMissingValues()
+    MA=E.DeleteMissingValue(MA)
+    t=D.Normal_distrfit(GlobalDemand)
+    avg=t.get('mean')
+    stdev=t.get('stdev')
+
+    def constrained_sum_sample_pos(n, total):
+        """Return a randomly chosen list of n positive integers summing to total.
+        Each such list is equally likely to occur."""
+     
+        dividers = sorted(random.sample(xrange(1, total), n - 1))
+        return [a - b for a, b in zip(dividers + [total], [0] + dividers)]
+
+    def constrained_sum_sample_nonneg(n, total):
+        """Return a randomly chosen list of n nonnegative integers summing to total.
+        Each such list is equally likely to occur."""
+
+        return [x - 1 for x in constrained_sum_sample_pos(n, total + n)]     
         
-###==================================================================================================### 
-sheet2 = book.add_sheet('PPOS', cell_overwrite_ok=True)
-
-dictPPOS={}
-dictPPOSMA={}
-
-for ind in uniquePPOS:
-    indices=[i for i,j in enumerate(PPOS) if j==ind]
-    mas=[ma for ma in MA if (MA.index(ma) in indices)]
-    dictPPOSMA.update({ind: mas})
-
-t=1
-for key in dictPPOSMA.keys():
-    for elem in dictPPOSMA[key]:   
-        if key==PPOSToBeDisaggregated:
-            c=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),PPOSQuantity)
-            d=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),100)
-            myInt=100
-            d=robjects.FloatVector(d)
-            listd = [x/myInt for x in d]
-            for i in range(0,len(dictPPOSMA[key])):
-                MinUnits=round(c[i]*(random.uniform(0,0.2)),0)
-                TotalUnits=c[i]
-                if TotalUnits<MinPackagingSize:
-                    TotalUnits=0
-                if MinUnits<MinPackagingSize:
-                    MinUnits=0
-                dictPPOS.update({dictPPOSMA[key][i]:[TotalUnits,MinUnits]})
-                             
-t=1
-for i in range(0,len(dictPPOS)):
-    sheet2.write(0,0,'Order ID')
-    sheet2.write(0,1,'MA ID')
-    sheet2.write(0,2,'Total # Units')
-    sheet2.write(0,3,'Min # Units')
-    sheet2.write(0,4,'Planned Week')
-    sheet2.write(t,0,t)
-    sheet2.write(t,1,dictPPOSMA[PPOSToBeDisaggregated][i])
-    sheet2.write(t,2,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0])
-    sheet2.write(t,3,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1])
-    sheet2.write(t,4,PlannedWeek)
-    book.save('DemandProfile.xls')
-    t+=1          
-
-# open json file
-PPOSProfileFile=open('PPOSProfile.json', mode='w')
-PPOSProfile={}
-t=1
-for i in range(0,len(dictPPOS)):
-    dictb={'MAID':dictPPOSMA[PPOSToBeDisaggregated][i],'TotalUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0],'MinUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1],'PlannedWeek':PlannedWeek}
-    PPOSProfile[t]=dictb
-    PPOSProfileString=json.dumps(PPOSProfile, indent=5)
-    t+=1
- 
-#write json file
-PPOSProfileFile.write(PPOSProfileString)
+    DemandProfile={} #Create a dictionary
+    MinPackagingSize=10
+    week=[1,2,3,4,5,6,7,8,9,10] # list that defines the planning horizon, i.e. 10 weeks
+
+    for i in week:
+        Demand=int(abs(random.normalvariate(avg,stdev))) # Generate a random, non-negative, integer number from the Normal distribution
+        AllocatedPercent=0.8-(0.05*i) # Defines a number starts with 0.8 or 80% and reduced with every iteration at 0.05 or 5%
+        Remaining_Demand=int((1-AllocatedPercent)*Demand) # Defines the Remaining demand
+        a=constrained_sum_sample_nonneg(len(MA),100)
+        myInt=100
+        a=robjects.FloatVector(a)
+        lista = [x/myInt for x in a] # Define a list with the same length as the MA list and elements float numbers with total sum equal to 1  
+        b=constrained_sum_sample_nonneg(len(MA),Remaining_Demand) # Define a list with the same length as the MA list and elements with total sum the Remaining demand  
+        dicta={}
+        for index in range(0,len(MA)):
+            MinUnits=round(b[index]*(random.uniform(0,0.2)),0)
+            TotalUnits=b[index]
+            if TotalUnits<MinPackagingSize:
+                TotalUnits=0
+            if MinUnits<MinPackagingSize:
+                MinUnits=0
+            dicta.update({MA[index]:[TotalUnits,MinUnits]}) # it updates a dictionary with key the different MAs and values the remaining demand and (b[index]*lista[index])        
+            DemandProfile.update({i:dicta}) #It updates a dictionary with key the number of each iteration (week) and value the dictionary dicta
+
+    Table=[]
+    i=0
+    for i in range(len(MA)):
+        Table.append([PPOS[i],SP[i],MA[i]])
+        i+=1
+    uniquePPOS=[]
+    for ppos in PPOS:
+        if not ppos in uniquePPOS and ppos!='':
+            uniquePPOS.append(ppos)
+
+
+    book=Workbook()
+    sheet1 = book.add_sheet('Future', cell_overwrite_ok=True)
+    aggrTable=[]
+    for key in DemandProfile.keys():
+        for elem in DemandProfile[key]:
+            if DemandProfile[key].get(elem)[0]> 0:
+                MAkey=elem
+                totalUnits=DemandProfile[key].get(elem)[0]
+                minUnits=DemandProfile[key].get(elem)[1]
+                plannedWeek=key
+                aggrTable.append([MAkey,totalUnits,minUnits,plannedWeek])
+            else: 
+                continue  
+    t=1
+    aggrTable.sort(key=lambda x:x[1], reverse=False)
+    for i in sorted(aggrTable, key= lambda x:int(x[3])):
+        sheet1.write(0,0,'Order ID')
+        sheet1.write(0,1,'MA ID')
+        sheet1.write(0,2,'Total # Units')
+        sheet1.write(0,3,'Min # Units')
+        sheet1.write(0,4,'Planned Week')
\ No newline at end of file
+        sheet1.write(t,1,i[0])
+        sheet1.write(t,2,i[1])
+        sheet1.write(t,3,i[2])
+        sheet1.write(t,4,i[3])
+        sheet1.write(t,0,t)
+        t+=1
+
+    # open json file
+    futureDemandProfileFile=open('futureDemandProfile.json', mode='w')
+    futureDemandProfile={}
+     
+    t=1
+    for i in sorted(aggrTable, key= lambda x:int(x[3])):
+        dicta={'MAID':i[0],'TotalUnits':i[1],'MinUnits':i[2],'PlannedWeek':i[3]}
+        futureDemandProfile[t]=dicta
+        futureDemandProfileString=json.dumps(futureDemandProfile, indent=5)
+        t+=1
+     
+    #write json file
+    futureDemandProfileFile.write(futureDemandProfileString)
+            
+    ###==================================================================================================### 
+    sheet2 = book.add_sheet('PPOS', cell_overwrite_ok=True)
+
+    dictPPOS={}
+    dictPPOSMA={}
+
+    for ind in uniquePPOS:
+        indices=[i for i,j in enumerate(PPOS) if j==ind]
+        mas=[ma for ma in MA if (MA.index(ma) in indices)]
+        dictPPOSMA.update({ind: mas})
+
+    t=1
+    for key in dictPPOSMA.keys():
+        for elem in dictPPOSMA[key]:   
+            if key==PPOSToBeDisaggregated:
+                c=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),PPOSQuantity)
+                d=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),100)
+                myInt=100
+                d=robjects.FloatVector(d)
+                listd = [x/myInt for x in d]
+                for i in range(0,len(dictPPOSMA[key])):
+                    MinUnits=round(c[i]*(random.uniform(0,0.2)),0)
+                    TotalUnits=c[i]
+                    if TotalUnits<MinPackagingSize:
+                        TotalUnits=0
+                    if MinUnits<MinPackagingSize:
+                        MinUnits=0
+                    dictPPOS.update({dictPPOSMA[key][i]:[TotalUnits,MinUnits]})
+                                 
+    t=1
+    for i in range(0,len(dictPPOS)):
+        sheet2.write(0,0,'Order ID')
+        sheet2.write(0,1,'MA ID')
+        sheet2.write(0,2,'Total # Units')
+        sheet2.write(0,3,'Min # Units')
+        sheet2.write(0,4,'Planned Week')
+        sheet2.write(t,0,t)
+        sheet2.write(t,1,dictPPOSMA[PPOSToBeDisaggregated][i])
+        sheet2.write(t,2,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0])
+        sheet2.write(t,3,dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1])
+        sheet2.write(t,4,PlannedWeek)
+        t+=1          
+
+    # open json file
+    PPOSProfileFile=open('PPOSProfile.json', mode='w')
+    PPOSProfile={}
+    t=1
+    for i in range(0,len(dictPPOS)):
+        dictb={'MAID':dictPPOSMA[PPOSToBeDisaggregated][i],'TotalUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][0],'MinUnits':dictPPOS[dictPPOSMA[PPOSToBeDisaggregated][i]][1],'PlannedWeek':PlannedWeek}
+        PPOSProfile[t]=dictb
+        PPOSProfileString=json.dumps(PPOSProfile, indent=5)
+        t+=1
+
+    #write json file
+    PPOSProfileFile.write(PPOSProfileString)
+
+    import StringIO
+    out = StringIO.StringIO()
+    book.save(out)
+    book.save('DP.xls')
+    return out.getvalue()
+
+if __name__ == '__main__':
+   with open('DemandProfile.xls', 'w') as outputfile:
+     outputfile.write(generateDemandPlanning('Input8PPOS.xlsx'))
+
+

dream/KnowledgeExtraction/PilotCases/__init__.py

+# ===========================================================================
+# 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/>.
+# ===========================================================================
+# See http://peak.telecommunity.com/DevCenter/setuptools#namespace-packages
+try:
+    __import__('pkg_resources').declare_namespace(__name__)
+except ImportError:
+    from pkgutil import extend_path
+    __path__ = extend_path(__path__, __name__)
\ No newline at end of file