Merge branch 'batchesTabExit'

dream/plugins/AddBatchStations.py

@@ -126,6 +126,10 @@ class AddBatchStations(plugin.InputPreparationPlugin):
                     # add an edge from batchReassembly to destination
                     self.addEdge(data, batchReassemblyId, destination)             
 
+        # set all the Queue types to gether wip data
+        for node in data["graph"]["node"].values():
+            if node['_class'] in ['Dream.Queue', 'Dream.LineClearance', 'Dream.RoutingQueue']:
+                node['gatherWipStat'] = 1
 
 
         return data

dream/plugins/BatchesTabularExit.py

+from copy import copy
+import json
+import time
+import random
+import operator
+import StringIO
+import xlrd
+import numpy
+
+from dream.plugins import plugin
+
+class BatchesTabularExit(plugin.OutputPreparationPlugin):
+    """ Output the exit stats in a tab
+    """
+
+    def postprocess(self, data):
+        numberOfReplications=int(data['general']['numberOfReplications'])
+        confidenceLevel=float(data['general']['confidenceLevel'])
+        maxSimTime=data['general']['maxSimTime']
+        timeUnit=data['general']['timeUnit']
+        if numberOfReplications==1:
+            # create the titles of the columns
+            data['result']['result_list'][0]['exit_output'] = [['KPI','Unit','Value']]
+            # loop the results and search for elements that have 'Exit' as family
+            for record in data['result']['result_list'][-1]['elementList']:
+                family=record.get('family',None)
+                # when found, add a row with the results of the specific exit
+                if family=='Exit':
+                    batchesThroughput=record['results']['throughput'][0]
+                    data['result']['result_list'][0]['exit_output'].append(['Number of batches produced','Batches',
+                                                                            batchesThroughput])                     
+                    unitsThroughput=record['results']['unitsThroughput'][0]
+                    data['result']['result_list'][0]['exit_output'].append(['Number of units produced','Units',
+                                                                            unitsThroughput]) 
+                    lineThroughput=batchesThroughput/float(maxSimTime)
+                    data['result']['result_list'][0]['exit_output'].append(['Line throughput','Batches/'+timeUnit,
+                                                                            "%.2f" % lineThroughput]) 
+                    unitDepartureRate=unitsThroughput/float(maxSimTime)
+                    data['result']['result_list'][0]['exit_output'].append(['Average Unit Departure Rate',
+                                                                            'Units/'+timeUnit,
+                                                                            "%.2f" % unitDepartureRate]) 
+                    avgCycleTime=record['results']['lifespan'][0]
+                    data['result']['result_list'][0]['exit_output'].append(['Average Cycle Time',
+                                                                            timeUnit,
+                                                                            "%.2f" % avgCycleTime])                   
+        elif numberOfReplications>1:
+            # create the titles of the columns
+            data['result']['result_list'][0]['exit_output'] = [['KPI','Unit','Average','Std Dev','Min','Max',
+                                                                str(float(confidenceLevel)*100)+'% CI LB ',
+                                                                str(float(confidenceLevel)*100)+'% CI UB']]
+            for record in data['result']['result_list'][0]['elementList']:
+                family=record.get('family',None)
+                # when found, add a row with the results of the specific exit
+                if family=='Exit':
+                    batchesThroughputList=record['results']['throughput']
+                    batchesThroughputCI=self.getConfidenceInterval(batchesThroughputList,confidenceLevel)
+                    data['result']['result_list'][0]['exit_output'].append(['Number of batches produced','Batches',
+                                                                            "%.2f" % self.getAverage(batchesThroughputList),
+                                                                            "%.2f" % self.getStDev(batchesThroughputList),
+                                                                            min(batchesThroughputList),
+                                                                            max(batchesThroughputList),
+                                                                            "%.2f" % batchesThroughputCI['lb'],
+                                                                            "%.2f" % batchesThroughputCI['ub']]
+                                                                           )    
+                    unitsThroughputList=record['results']['unitsThroughput']
+                    unitsThroughputCI=self.getConfidenceInterval(unitsThroughputList,confidenceLevel)
+                    data['result']['result_list'][0]['exit_output'].append(['Number of units produced','Units',
+                                                                            "%.2f" % self.getAverage(unitsThroughputList),
+                                                                            "%.2f" % self.getStDev(unitsThroughputList),
+                                                                            min(unitsThroughputList),
+                                                                            max(unitsThroughputList),
+                                                                            "%.2f" % unitsThroughputCI['lb'],
+                                                                            "%.2f" % unitsThroughputCI['ub']]
+                                                                           )    
+                    lineThroughputList=[x/float(maxSimTime) for x in batchesThroughputList]
+                    lineThroughputCI=self.getConfidenceInterval(lineThroughputList,confidenceLevel)
+                    data['result']['result_list'][0]['exit_output'].append(['Line throughput','Batches/'+timeUnit,
+                                                                            "%.2f" % self.getAverage(lineThroughputList),
+                                                                            "%.2f" % self.getStDev(lineThroughputList),
+                                                                            "%.2f" % min(lineThroughputList),
+                                                                            "%.2f" % max(lineThroughputList),
+                                                                            "%.2f" % lineThroughputCI['lb'],
+                                                                            "%.2f" % lineThroughputCI['ub']]
+                                                                           ) 
+                    unitDepartureRateList=[x/float(maxSimTime) for x in unitsThroughputList]
+                    unitDepartureRateCI=self.getConfidenceInterval(unitDepartureRateList,confidenceLevel)
+                    data['result']['result_list'][0]['exit_output'].append(['Unit Departure Rate',
+                                                                            'Units/'+timeUnit,
+                                                                            "%.2f" % self.getAverage(unitDepartureRateList),
+                                                                            "%.2f" % self.getStDev(unitDepartureRateList),
+                                                                            "%.2f" % min(unitDepartureRateList),
+                                                                            "%.2f" % max(unitDepartureRateList),
+                                                                            "%.2f" % unitDepartureRateCI['lb'],
+                                                                            "%.2f" % unitDepartureRateCI['ub']]
+                                                                           )      
+                    avgCycleTime=record['results']['lifespan']         
+                    avgCycleTimeList=record['results']['lifespan']
+                    avgCycleTimeCI=self.getConfidenceInterval(avgCycleTimeList,confidenceLevel)
+                    data['result']['result_list'][0]['exit_output'].append(['Cycle Time',timeUnit,
+                                                                            "%.2f" % self.getAverage(avgCycleTimeList),
+                                                                            "%.2f" % self.getStDev(avgCycleTimeList),
+                                                                            "%.2f" % min(avgCycleTimeList),
+                                                                            "%.2f" % max(avgCycleTimeList),
+                                                                            "%.2f" % avgCycleTimeCI['lb'],
+                                                                            "%.2f" % avgCycleTimeCI['ub']]
+                                                                           ) 
+        return data
\ No newline at end of file

dream/plugins/BatchesTabularQueues.py

+from copy import copy
+import json
+import time
+import random
+import operator
+import StringIO
+import xlrd
+import math
+
+from dream.plugins import plugin
+
+class BatchesTabularQueues(plugin.OutputPreparationPlugin):
+    """ Output the exit stats in a tab
+    """
+
+    def postprocess(self, data):
+        numberOfReplications=int(data['general']['numberOfReplications'])
+        confidenceLevel=float(data['general']['confidenceLevel'])
+        maxSimTime=float(data['general']['maxSimTime'])
+        if numberOfReplications==1:
+            # create the titles of the columns
+            data['result']['result_list'][-1]['buffer_output'] = [['Buffer','Final Value','Average',
+                                                                  'Std Dev','Min','Max',]]
+            # loop the results and search for elements that have 'Exit' as family
+            for record in data['result']['result_list'][-1]['elementList']:
+                family=record.get('family',None)
+                # when found, add a row with the results of the specific exit
+                if family=='Buffer':
+                    bufferId=record['id']
+                    wip_stat_list=record['results']['wip_stat_list'][0]
+                    bufferLevels=[int(x[1]) for x in wip_stat_list]
+                    maxLevel=max(bufferLevels)
+                    finalValue=wip_stat_list[-1][1]
+                    timeListDict=self.createTimeListDict(wip_stat_list,maxSimTime)
+                    totalLevel=0
+                    minLevel=float('inf')
+                    # count the minimum that has no zero duration
+                    for level, duration in timeListDict.iteritems():
+                        if duration and (level<minLevel):
+                            minLevel=level
+                            
+                    for level, duration in timeListDict.iteritems():
+                        totalLevel+=level*duration
+                    averageLevel=totalLevel/float(maxSimTime)
+                    totalDistance=0
+                    for level, duration in timeListDict.iteritems():
+                        totalDistance+=((level-averageLevel)*(level-averageLevel))*duration
+                    stdevLevel=math.sqrt(totalDistance/float(maxSimTime-1))
+                    data['result']['result_list'][-1]['buffer_output'].append([bufferId,finalValue,
+                                                                              "%.2f" % averageLevel,
+                                                                              "%.2f" % stdevLevel,
+                                                                              minLevel,maxLevel])
+                                        
+        elif numberOfReplications>1:
+            # create the titles of the columns
+            pass
+        return data
+
+    # takes the time list that ManPy outputs and creates a dict so that it is easier to get avg etc
+    def createTimeListDict(self, timeList,maxSimTime):
+        timeListDict={}
+        i=0
+        for record in timeList:
+            time=record[0]
+            level=int(record[1])
+            try:
+                nextTime=timeList[i+1][0]
+            except IndexError:
+                nextTime=maxSimTime
+            i+=1
+            if not (level in timeListDict.keys()):
+                timeListDict[level]=0
+            timeListDict[level]+=nextTime-time
+        return timeListDict
+            

dream/plugins/plugin.py

 from copy import deepcopy
 import json
+import numpy
 
 from zope.dottedname.resolve import resolve
 
@@ -32,6 +33,25 @@ class Plugin(object):
             successors.append(edge['destination'])
     return successors
 
+  # calcualted the confidence inteval for a list and a confidence level
+  def getConfidenceInterval(self, value_list, confidenceLevel):
+    from dream.KnowledgeExtraction.ConfidenceIntervals import Intervals
+    from dream.KnowledgeExtraction.StatisticalMeasures import BasicStatisticalMeasures
+    BSM=BasicStatisticalMeasures()
+    lb, ub = Intervals().ConfidIntervals(value_list, confidenceLevel)
+    return {'lb': lb,
+            'ub': ub,
+            'avg': BSM.mean(value_list) 
+        }
+    
+  # return the average of a list    
+  def getAverage(self, value_list):
+    return sum(value_list) / float(len(value_list))
+  
+  # return the standard deviation of a list                                      
+  def getStDev(self, value_list):
+    return numpy.std(value_list)
+
   # returns name of a node given its id
   def getNameFromId(self, data, node_id):
       return data['graph']['node'][node_id]['name']

dream/simulation/Examples/GUI_instances/BatchAllInOneEmpty.json

@@ -201,22 +201,22 @@
       }
     }, 
     "output": {
-      "view_exit_stats": {
+	  "view_exit_results": {
         "configuration": {
-          "properties": {
-            "lifespan": {
-              "type": "number"
+          "handsontable_options": {}, 
+          "output_id": "exit_output"
         }, 
-            "taktTime": {
-              "type": "number"
+        "gadget": "Output_viewSpreadsheet", 
+        "title": "Exit statistics", 
+        "type": "object_view"
       },
-            "throughput": {
-              "type": "number"
-            }
-          }
+      "view_buffer_state": {
+        "configuration": {
+          "handsontable_options": {}, 
+          "output_id": "buffer_output"
         }, 
-        "gadget": "Output_viewExitStatistics", 
-        "title": "Exit Statistics", 
+        "gadget": "Output_viewSpreadsheet", 
+        "title": "Buffer Levels", 
         "type": "object_view"
       },	  
       "view_operator_gantt": {
@@ -257,8 +257,15 @@
         {
           "_class": "dream.plugins.PostProcessQueueStatistics.PostProcessQueueStatistics", 
           "output_id": "queue_statistics"
+        },
+        {
+          "_class": "dream.plugins.BatchesTabularExit.BatchesTabularExit", 
+          "output_id": "exit_output"
+        },
+        {
+          "_class": "dream.plugins.BatchesTabularQueues.BatchesTabularQueues", 
+          "output_id": "buffer_output"
         }
-
 	  ]
     }, 
     "pre_processing": {