Files deleted

dream/KnowledgeExtraction/PilotCases/InputData_DemandPlanning.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/>.
-# ===========================================================================
-
-'''
-Created on 7 May 2014
-
-@author: Panos
-'''
-
-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
-
-def generateDemandPlanning(input_url, PPOSQuantity=1000, PlannedWeek=1, PPOSToBeDisaggregated='PPOS1', 
-                           MinPackagingSize=10, planningHorizon=10):
-    """Generate random demand from spreadsheet at input_url.
-    """
-    # id is given as an integer and minus one
-    # ToDo we have to standardize data
-    PPOSToBeDisaggregated='PPOS'+str(PPOSToBeDisaggregated+1)
-
-    
-    # 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] 
-
-    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
-    
-    week=[] # list that defines the planning horizon, i.e. 10 weeks
-    for i in range(int(planningHorizon)):
-        week.append(i+1)
-
-    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('Future1', 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].replace('MA', '', 1)))
-        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)
-        # XXX the MA id should not have MA prefix...
-        sheet2.write(t,1,dictPPOSMA[PPOSToBeDisaggregated][i].replace('MA', '', 1))
-
-        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