modify InputData_DemandPlanning to have a function that we can call externally

dream/KnowledgeExtraction/PilotCases/InputData_DemandPlanning.py

@@ -23,59 +23,68 @@ 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):
+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):
+    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
+    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:
+    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
@@ -95,21 +104,21 @@ for i in week:
             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=[]
+    i=0
+    for i in range(len(MA)):
         Table.append([PPOS[i],SP[i],MA[i]])
         i+=1
-uniquePPOS=[]
-for ppos in PPOS:
+    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():
+    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
@@ -119,9 +128,9 @@ for key in DemandProfile.keys():
                 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])):
+    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')
@@ -132,36 +141,35 @@ for i in sorted(aggrTable, key= lambda x:int(x[3])):
         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={}
+    # open json file
+    futureDemandProfileFile=open('futureDemandProfile.json', mode='w')
+    futureDemandProfile={}
      
-t=1
-for i in sorted(aggrTable, key= lambda x:int(x[3])):
+    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)
+    #write json file
+    futureDemandProfileFile.write(futureDemandProfileString)
             
-###==================================================================================================### 
-sheet2 = book.add_sheet('PPOS', cell_overwrite_ok=True)
+    ###==================================================================================================### 
+    sheet2 = book.add_sheet('PPOS', cell_overwrite_ok=True)
 
-dictPPOS={}
-dictPPOSMA={}
+    dictPPOS={}
+    dictPPOSMA={}
 
-for ind in uniquePPOS:
+    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():
+    t=1
+    for key in dictPPOSMA.keys():
         for elem in dictPPOSMA[key]:   
             if key==PPOSToBeDisaggregated:
                 c=constrained_sum_sample_nonneg(len(dictPPOSMA[key]),PPOSQuantity)
@@ -178,8 +186,8 @@ for key in dictPPOSMA.keys():
                         MinUnits=0
                     dictPPOS.update({dictPPOSMA[key][i]:[TotalUnits,MinUnits]})
                                  
-t=1
-for i in range(0,len(dictPPOS)):
+    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')
@@ -190,18 +198,29 @@ for i in range(0,len(dictPPOS)):
         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)):
+    # 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)
\ No newline at end of file
+    #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